1
|
Lewetz D, Stieger S. ESMira: A decentralized open-source application for collecting experience sampling data. Behav Res Methods 2024; 56:4421-4434. [PMID: 37604961 PMCID: PMC11288990 DOI: 10.3758/s13428-023-02194-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2023] [Indexed: 08/23/2023]
Abstract
This paper introduces ESMira, a server and mobile app (Android, iOS) developed for research projects using experience sampling method (ESM) designs. ESMira offers a very simple setup process and ease of use, while being free, decentralized, and open-source (source code is available on GitHub). The ongoing development of ESMira started in early 2019, with a focus on scientific requirements (e.g., informed consent, ethical considerations), data security (e.g., encryption), and data anonymity (e.g., completely anonymous data workflow). ESMira sets itself apart from other platforms by both being free of charge and providing study administrators with full control over study data without the need for specific technological skills (e.g., programming). This means that study administrators can have ESMira running on their own webspace without needing much technical knowledge, allowing them to remain independent from any third-party service. Furthermore, ESMira offers an extensive list of features (e.g., an anonymous built-in chat to contact participants; a reward system that allows participant incentivization without breaching anonymity; live graphical feedback for participants) and can deal with complex study designs (e.g., nested time-based sampling). In this paper, we illustrate the basic structure of ESMira, explain how to set up a new server and create studies, and introduce the platform's basic functionalities.
Collapse
Affiliation(s)
- David Lewetz
- Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, A-3500, Krems an der Donau, Austria.
| | - Stefan Stieger
- Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, A-3500, Krems an der Donau, Austria.
| |
Collapse
|
2
|
Van Der Donckt J, Vandenbussche N, Van Der Donckt J, Chen S, Stojchevska M, De Brouwer M, Steenwinckel B, Paemeleire K, Ongenae F, Van Hoecke S. Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches. Sci Rep 2024; 14:17545. [PMID: 39079945 PMCID: PMC11289092 DOI: 10.1038/s41598-024-67767-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.
Collapse
Affiliation(s)
- Jonas Van Der Donckt
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium.
| | - Nicolas Vandenbussche
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | | | - Stephanie Chen
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Marija Stojchevska
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Mathias De Brouwer
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Bram Steenwinckel
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Koen Paemeleire
- Department of Neurology, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Ghent, Belgium
- Department of Basic and Applied Medical Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Femke Ongenae
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| | - Sofie Van Hoecke
- IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium
| |
Collapse
|
3
|
Ranjan Y, Chang J, Sankesara H, Conde P, Rashid Z, Dobson RJB, Folarin A. RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research. SENSORS (BASEL, SWITZERLAND) 2024; 24:4614. [PMID: 39066012 PMCID: PMC11280905 DOI: 10.3390/s24144614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/11/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024]
Abstract
IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.
Collapse
Affiliation(s)
- Yatharth Ranjan
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| | - Jiangeng Chang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 119077, Singapore;
| | - Heet Sankesara
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| | - Pauline Conde
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| | - Zulqarnain Rashid
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| | - Richard J. B. Dobson
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| | - Amos Folarin
- Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London SE5 8AF, UK; (Y.R.); (H.S.); (P.C.); (Z.R.); (R.J.B.D.)
| |
Collapse
|
4
|
Gill SK, Barsky A, Guan X, Bunting KV, Karwath A, Tica O, Stanbury M, Haynes S, Folarin A, Dobson R, Kurps J, Asselbergs FW, Grobbee DE, Camm AJ, Eijkemans MJC, Gkoutos GV, Kotecha D. Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial. Nat Med 2024; 30:2030-2036. [PMID: 39009776 PMCID: PMC11271403 DOI: 10.1038/s41591-024-03094-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/24/2024] [Indexed: 07/17/2024]
Abstract
Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .
Collapse
Affiliation(s)
- Simrat K Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | - Andrey Barsky
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Xin Guan
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Karina V Bunting
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
- West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Andreas Karwath
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Otilia Tica
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | | | | | - Amos Folarin
- Department of Biostatistics & Health Informatics, King's College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Richard Dobson
- Department of Biostatistics & Health Informatics, King's College London, London, UK
- Health Data Research UK, University College London, London, UK
| | - Julia Kurps
- Real World Data team, The Hyve, Utrecht, the Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Amsterdam University Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, the Netherlands
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - A John Camm
- Cardiology Clinical Academic Group, St George's University of London, London, UK
| | - Marinus J C Eijkemans
- Amsterdam University Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, the Netherlands
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK.
- West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
| |
Collapse
|
5
|
Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P, Matcham F, White KM, Oetzmann C, Lamers F, Siddi S, Simblett S, Vairavan S, Myin-Germeys I, Mohr DC, Wykes T, Haro JM, Annas P, Penninx BW, Narayan VA, Hotopf M, Dobson RJ. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis. J Med Internet Res 2024; 26:e55302. [PMID: 38941600 PMCID: PMC11245656 DOI: 10.2196/55302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/22/2024] [Accepted: 03/29/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | | | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Vaibhav A Narayan
- Janssen Research and Development LLC, Titusville, NJ, United States
- Davos Alzheimer's Collaborative, Geneva, Switzerland
| | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard Jb Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| |
Collapse
|
6
|
Stegmann G, Krantsevich C, Liss J, Charles S, Bartlett M, Shefner J, Rutkove S, Kawabata K, Talkar T, Berisha V. Automated speech analytics in ALS: higher sensitivity of digital articulatory precision over the ALSFRS-R. Amyotroph Lateral Scler Frontotemporal Degener 2024:1-9. [PMID: 38932502 DOI: 10.1080/21678421.2024.2371986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
Objective: Although studies have shown that digital measures of speech detected ALS speech impairment and correlated with the ALSFRS-R speech item, no study has yet compared their performance in detecting speech changes. In this study, we compared the performances of the ALSFRS-R speech item and an algorithmic speech measure in detecting clinically important changes in speech. Importantly, the study was part of a FDA submission which received the breakthrough device designation for monitoring ALS; we provide this paper as a roadmap for validating other speech measures for monitoring disease progression. Methods: We obtained ALSFRS-R speech subscores and speech samples from participants with ALS. We computed the minimum detectable change (MDC) of both measures; using clinician-reported listener effort and a perceptual ratings of severity, we calculated the minimal clinically important difference (MCID) of each measure with respect to both sets of clinical ratings. Results: For articulatory precision, the MDC (.85) was lower than both MCID measures (2.74 and 2.28), and for the ALSFRS-R speech item, MDC (.86) was greater than both MCID measures (.82 and .72), indicating that while the articulatory precision measure detected minimal clinically important differences in speech, the ALSFRS-R speech item did not. Conclusion: The results demonstrate that the digital measure of articulatory precision effectively detects clinically important differences in speech ratings, outperforming the ALSFRS-R speech item. Taken together, the results herein suggest that this speech outcome is a clinically meaningful measure of speech change.
Collapse
Affiliation(s)
- Gabriela Stegmann
- Arizona State University, Phoenix, AZ, USA
- Aural Analytics, Scottsdale, AZ, USA
| | - Chelsea Krantsevich
- Arizona State University, Phoenix, AZ, USA
- Aural Analytics, Scottsdale, AZ, USA
| | - Julie Liss
- Arizona State University, Phoenix, AZ, USA
- Aural Analytics, Scottsdale, AZ, USA
| | - Sherman Charles
- Aural Analytics, Scottsdale, AZ, USA
- Linus Health, Boston, MA, USA
| | | | | | - Seward Rutkove
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kan Kawabata
- Aural Analytics, Scottsdale, AZ, USA
- Linus Health, Boston, MA, USA
| | - Tanya Talkar
- Aural Analytics, Scottsdale, AZ, USA
- Linus Health, Boston, MA, USA
| | - Visar Berisha
- Arizona State University, Phoenix, AZ, USA
- Aural Analytics, Scottsdale, AZ, USA
| |
Collapse
|
7
|
Zhang Y, Folarin AA, Dineley J, Conde P, de Angel V, Sun S, Ranjan Y, Rashid Z, Stewart C, Laiou P, Sankesara H, Qian L, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Schuller BW, Vairavan S, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Hotopf M, Dobson RJB, Cummins N. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. J Affect Disord 2024; 355:40-49. [PMID: 38552911 DOI: 10.1016/j.jad.2024.03.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/01/2024]
Abstract
BACKGROUND Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. METHODS The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. RESULTS From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LIMITATIONS Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. CONCLUSION This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Amos A Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Health Data Research UK London, University College London, London, UK
| | - Judith Dineley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University of Augsburg, Augsburg, Germany
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Valeria de Angel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Shaoxiong Sun
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petroula Laiou
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Heet Sankesara
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Linglong Qian
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; School of Psychology, University of Sussex, Falmer, East Sussex, UK
| | - Katie White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Björn W Schuller
- University of Augsburg, Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, UK
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Health Data Research UK London, University College London, London, UK
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| |
Collapse
|
8
|
Kuehne C, Phillips MD, Moody S, Bryson C, Campbell IC, Conde P, Cummins N, Desrivières S, Dineley J, Dobson R, Douglas D, Folarin A, Gallop L, Hemmings A, İnce B, Mason L, Rashid Z, Bromell A, Sims C, Allen K, Bailie C, Bains P, Basher M, Battisti F, Baudinet J, Bristow K, Dawson N, Dodd L, Frater V, Freudenthal R, Gripton B, Kan C, Khor JWT, Kotze N, Laverack S, Martin L, Maxwell S, McDonald S, McKnight D, McKay R, Merrin J, Nash M, Nicholls D, Palmer S, Pearce S, Roberts C, Serpell L, Severs E, Simic M, Staton A, Westaway S, Sharpe H, Schmidt U, Bartel H, French T, Kelly J, Micali N, Raman S, Treasure J, Malik U, Rabelo-da-Ponte D, Stephens F, Opitz T, Trompeter N, Wilkins J, Parnell T, Abbas R, Bromell A, Davis G, Eadie C, Gracie L, Heslop B, McKenzie K, Odubanjo E, Sims C, Street T, Tavares-Semedo A, Wilkinson E, Zocek L. Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol. BMC Psychiatry 2024; 24:409. [PMID: 38816707 PMCID: PMC11137943 DOI: 10.1186/s12888-024-05841-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse. METHODS STORY follows 720 young people aged 16-25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings ('Ōura ring') unobtrusively measures individuals' daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months. DISCUSSION By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.
Collapse
Affiliation(s)
- Carina Kuehne
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Matthew D Phillips
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Sarah Moody
- School of Health in Social Science, The University of Edinburgh, Edinburgh, UK
| | - Callum Bryson
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Iain C Campbell
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
| | - Sylvane Desrivières
- Social, Genetic & Developmental Psychiatry Centre, IoPPN, King's College London, London, UK
| | - Judith Dineley
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, London, UK
- University College London, Institute of Health Informatics, London, UK
| | - Daire Douglas
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Amos Folarin
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
- NIHR Maudsley Biomedical Research Centre, London, UK
- University College London, Institute of Health Informatics, London, UK
| | - Lucy Gallop
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Amelia Hemmings
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Başak İnce
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
| | - Luke Mason
- Department of Forensic and Neurodevelopmental Science, IoPPN, King's College London, London, UK
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, IoPPN, King's College London, London, UK
| | | | | | - Karina Allen
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Chantal Bailie
- Cornwall Partnership NHS Foundation Trus, Bodmin, Cornwall, UK
| | - Parveen Bains
- Oxford Health NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Mike Basher
- Cambridgeshire and Peterborough NHS Foundation Trust, Fulbourn, Cambridgeshire, UK
| | | | - Julian Baudinet
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Katherine Bristow
- Cambridgeshire and Peterborough NHS Foundation Trust, Fulbourn, Cambridgeshire, UK
| | - Nicola Dawson
- Bradford District Care NHS Foundation Trust, West Yorkshire, UK
| | - Lizzie Dodd
- South West Yorkshire Partnership NHS Foundation Trust, Wakefield, UK
| | - Victoria Frater
- Cumbria Northumberland Tyne and Wear NHS Foundation Trust, Newcastle Upon Tyne, UK
| | - Robert Freudenthal
- Barnet, Enfield and Haringey Mental Health NHS Foundation Trust, London, UK
| | - Beth Gripton
- Leeds and York Partnership NHS Foundation Trust, Leeds, UK
| | - Carol Kan
- Central and North West London NHS Foundation Trust, London, UK
| | - Joel W T Khor
- South West London & St. George's Mental Health NHS Trust, St George's Eating Disorders Service, London, UK
| | - Nicus Kotze
- Dorset Healthcare University NHS Foundation Trust, Poole, Dorset, UK
| | - Stuart Laverack
- Derbyshire Healthcare NHS Foundation Trust, Derby, Derbyshire, UK
| | - Lee Martin
- Leeds and York Partnership NHS Foundation Trust, Leeds, UK
| | - Sarah Maxwell
- Norfolk and Suffolk NHS Foundation Trust, Norwich, Norfolk, UK
| | - Sarah McDonald
- Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, UK
| | - Delysia McKnight
- North Staffordshire Combined Healthcare NHS Trust; Trentham, Staffordshire, UK
| | | | - Jessica Merrin
- South West Yorkshire Partnership NHS Foundation Trust, Wakefield, UK
| | - Mel Nash
- Devon Partnership NHS Foundation Trust, Exeter, Devon, UK
| | - Dasha Nicholls
- Central and North West London NHS Foundation Trust, London, UK
- Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, UK
| | | | - Samantha Pearce
- Cornwall Partnership NHS Foundation Trus, Bodmin, Cornwall, UK
| | | | - Lucy Serpell
- North East London NHS Foundation Trust, London, UK
- Division of Psychology and Language Sciences, University College London, London, UK
| | | | - Mima Simic
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Amelia Staton
- Nottinghamshire Healthcare NHS Foundation Trust, Nottingham, UK
| | - Sian Westaway
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, UK
| | - Helen Sharpe
- School of Health in Social Science, The University of Edinburgh, Edinburgh, UK
| | - Ulrike Schmidt
- Centre for Research in Eating and Weight Disorders, Institute of Psychiatry, King's College London, Psychology & Neuroscience London (IoPPN), 103 Denmark Hill, First Floor, London, SE5 8AZ, UK.
- South London and Maudsley NHS Foundation Trust, London, UK.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Adasuriya G, Barsky A, Kralj-Hans I, Mohan S, Gill S, Chen Z, Jarman J, Jones D, Valli H, Gkoutos GV, Markides V, Hussain W, Wong T, Kotecha D, Haldar S. Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF). EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:344-355. [PMID: 38774381 PMCID: PMC11104468 DOI: 10.1093/ehjdh/ztae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/04/2024] [Accepted: 02/09/2024] [Indexed: 05/24/2024]
Abstract
Aims This proof-of-concept study sought to evaluate changes in heart rate (HR) obtained from a consumer wearable device and compare against implantable loop recorder (ILR)-detected recurrence of atrial fibrillation (AF) and atrial tachycardia (AT) after AF ablation. Methods and results REMOTE-AF (NCT05037136) was a prospectively designed sub-study of the CASA-AF randomized controlled trial (NCT04280042). Participants without a permanent pacemaker had an ILR implanted at their index ablation procedure for longstanding persistent AF. Heart rate and step count were continuously monitored using photoplethysmography (PPG) from a commercially available wrist-worn wearable. Photoplethysmography-recorded HR data were pre-processed with noise filtration and episodes at 1-min interval over 30 min of HR elevations (Z-score = 2) were compared with corresponding ILR data. Thirty-five patients were enrolled, with mean age 70.3 ± 6.8 years and median follow-up 10 months (interquartile range 8-12 months). Implantable loop recorder analysis revealed 17 out of 35 patients (49%) had recurrence of AF/AT. Compared with ILR recurrence, wearable-derived elevations in HR ≥ 110 beats per minute had a sensitivity of 95.3%, specificity 54.1%, positive predictive value (PPV) 15.8%, negative predictive value (NPV) 99.2%, and overall accuracy 57.4%. With PPG-recorded HR elevation spikes (non-exercise related), the sensitivity was 87.5%, specificity 62.2%, PPV 39.2%, NPV 92.3%, and overall accuracy 64.0% in the entire patient cohort. In the AF/AT recurrence only group, sensitivity was 87.6%, specificity 68.3%, PPV 53.6%, NPV 93.0%, and overall accuracy 75.0%. Conclusion Consumer wearable devices have the potential to contribute to arrhythmia detection after AF ablation. Study Registration ClinicalTrials.gov Identifier: NCT05037136 https://clinicaltrials.gov/ct2/show/NCT05037136.
Collapse
Affiliation(s)
- Gamith Adasuriya
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Andrey Barsky
- Health Data Research UK Midlands & the NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Ines Kralj-Hans
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Siddhartha Mohan
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Simrat Gill
- Health Data Research UK Midlands & the NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Zhong Chen
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Julian Jarman
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - David Jones
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Haseeb Valli
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Georgios V Gkoutos
- Health Data Research UK Midlands & the NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Vias Markides
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Wajid Hussain
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
| | - Tom Wong
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Kings College Hospital, London, UK
| | - Dipak Kotecha
- Health Data Research UK Midlands & the NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | - Shouvik Haldar
- Heart Rhythm Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, Hill End Road, Harefield, London UB9 6JH, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| |
Collapse
|
10
|
White KM, Carr E, Leightley D, Matcham F, Conde P, Ranjan Y, Simblett S, Dawe-Lane E, Williams L, Henderson C, Hotopf M. Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial. JMIR Mhealth Uhealth 2024; 12:e44214. [PMID: 38241070 PMCID: PMC10837755 DOI: 10.2196/44214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/21/2023] [Accepted: 06/09/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Multiparametric remote measurement technologies (RMTs), which comprise smartphones and wearable devices, have the potential to revolutionize understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the utmost importance for the validity of predictive analytical methods and long-term use and can be conceptualized as both objective engagement (data availability) and subjective engagement (system usability and experiential factors). Positioning the design of user interfaces within the theoretical framework of the Behavior Change Wheel can help maximize effectiveness. In-app components containing information from credible sources, visual feedback, and access to support provide an opportunity to promote engagement with RMTs while minimizing team resources. Randomized controlled trials are the gold standard in quantifying the effects of in-app components on engagement with RMTs in patients with MDD. OBJECTIVE This study aims to evaluate whether a multiparametric RMT system with theoretically informed notifications, visual progress tracking, and access to research team contact details could promote engagement with remote symptom tracking over and above the system as usual. We hypothesized that participants using the adapted app (intervention group) would have higher engagement in symptom monitoring, as measured by objective and subjective engagement. METHODS A 2-arm, parallel-group randomized controlled trial (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms used the RADAR-base system, comprising a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50, 50%) also had access to additional in-app components. The primary outcome was objective engagement, measured as the percentage of weekly questionnaires completed during follow-up. The secondary outcomes measured subjective engagement (system engagement, system usability, and emotional self-awareness). RESULTS The levels of completion of the Patient Health Questionnaire-8 (PHQ-8) were similar between the control (67/97, 69%) and intervention (66/97, 68%) arms (P value for the difference between the arms=.83, 95% CI -9.32 to 11.65). The intervention group participants reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19), and system usability (2.29, 95% CI -5.93 to 10.52) scores than the control group participants at follow-up; however, all CIs were wide and included 0. Process evaluation suggested that participants saw the in-app components as helpful in increasing task completion. CONCLUSIONS The adapted system did not increase objective or subjective engagement in remote symptom tracking in our research cohort. This study provides an important foundation for understanding engagement with RMTs for research and the methodologies by which this work can be replicated in both community and clinical settings. TRIAL REGISTRATION ClinicalTrials.gov NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/32653.
Collapse
Affiliation(s)
- Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Erin Dawe-Lane
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Laura Williams
- NIHR MindTech MedTech Co-operative, Institute of Mental Health and Clinical Neurosciences, University of Nottingham, Nottingham, United Kingdom
| | - Claire Henderson
- Health Services & Population Research Department, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
11
|
Dalla Costa G, Nos C, Zabalza A, Buron M, Magyari M, Sellebjerg F, Guerrero AI, Roselli L, La Porta ML, Martinis M, Bailon R, Kontaxis S, Laporta E, Garcia E, Pokorny FB, Schuller BW, Folarin A, Stewart C, Leocani L, Vairavan S, Cummins N, Dobson R, Hotopf M, Narayan V, Montalban X, Sorensen PS, Comi G. A wearable device perspective on the standard definitions of disability progression in multiple sclerosis. Mult Scler 2024; 30:103-112. [PMID: 38084497 DOI: 10.1177/13524585231214362] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Multiple sclerosis (MS) is a leading cause of disability among young adults, but standard clinical scales may not accurately detect subtle changes in disability occurring between visits. This study aims to explore whether wearable device data provides more granular and objective measures of disability progression in MS. METHODS Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) is a longitudinal multicenter observational study in which 400 MS patients have been recruited since June 2018 and prospectively followed up for 24 months. Monitoring of patients included standard clinical visits with assessment of disability through use of the Expanded Disability Status Scale (EDSS), 6-minute walking test (6MWT) and timed 25-foot walk (T25FW), as well as remote monitoring through the use of a Fitbit. RESULTS Among the 306 patients who completed the study (mean age, 45.6 years; females 67%), confirmed disability progression defined by the EDSS was observed in 74 patients, who had approximately 1392 fewer daily steps than patients without disability progression. However, the decrease in the number of steps experienced over time by patients with EDSS progression and stable patients was not significantly different. Similar results were obtained with disability progression defined by the 6MWT and the T25FW. CONCLUSION The use of continuous activity monitoring holds great promise as a sensitive and ecologically valid measure of disability progression in MS.
Collapse
Affiliation(s)
| | - Carlos Nos
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Mathias Buron
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Finn Sellebjerg
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | | | - Raquel Bailon
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Estela Laporta
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Esther Garcia
- Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Florian B Pokorny
- Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Björn W Schuller
- Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Group on Language, Audio & Music, Imperial College London, London, UK
| | - Amos Folarin
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Callum Stewart
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
| | | | - Srinivasan Vairavan
- Janssen Research and Development LLC, Janssen Global Services, LLC, Titusville, NJ, USA
| | - Nicholas Cummins
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Richard Dobson
- Department of Biostatistics & Health informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Vaibhav Narayan
- Janssen Research and Development LLC, Janssen Global Services, LLC, Titusville, NJ, USA
| | - Xavier Montalban
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Per Soelberg Sorensen
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Giancarlo Comi
- Vita-Salute San Raffaele University, Milan, Italy/Multiple Sclerosis Center, Casa di Cura Igea, Milan, Italy
| |
Collapse
|
12
|
Althobiani MA, Ranjan Y, Jacob J, Orini M, Dobson RJB, Porter JC, Hurst JR, Folarin AA. Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study. JMIR Form Res 2023; 7:e51507. [PMID: 37999935 DOI: 10.2196/51507] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/23/2023] [Accepted: 10/20/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Patients with chronic respiratory diseases and those in the postdischarge period following hospitalization because of COVID-19 are particularly vulnerable, and little is known about the changes in their symptoms and physiological parameters. Continuous remote monitoring of physiological parameters and symptom changes offers the potential for timely intervention, improved patient outcomes, and reduced health care costs. OBJECTIVE This study investigated whether a real-time multimodal program using commercially available wearable technology, home-based Bluetooth-enabled spirometers, finger pulse oximeters, and smartphone apps is feasible and acceptable for patients with chronic respiratory diseases, as well as the value of low-burden, long-term passive data collection. METHODS In a 3-arm prospective observational cohort feasibility study, we recruited 60 patients from the Royal Free Hospital and University College Hospital. These patients had been diagnosed with interstitial lung disease, chronic obstructive pulmonary disease, or post-COVID-19 condition (n=20 per group) and were followed for 180 days. This study used a comprehensive remote monitoring system designed to provide real-time and relevant data for both patients and clinicians. Data were collected using REDCap (Research Electronic Data Capture; Vanderbilt University) periodic surveys, Remote Assessment of Disease and Relapses-base active app questionnaires, wearables, finger pulse oximeters, smartphone apps, and Bluetooth home-based spirometry. The feasibility of remote monitoring was measured through adherence to the protocol, engagement during the follow-up period, retention rate, acceptability, and data integrity. RESULTS Lowest-burden passive data collection methods, via wearables, demonstrated superior adherence, engagement, and retention compared with active data collection methods, with an average wearable use of 18.66 (SD 4.69) hours daily (77.8% of the day), 123.91 (SD 33.73) hours weekly (72.6% of the week), and 463.82 (SD 156.70) hours monthly (64.4% of the month). Highest-burden spirometry tasks and high-burden active app tasks had the lowest adherence, engagement, and retention, followed by low-burden questionnaires. Spirometry and active questionnaires had the lowest retention at 0.5 survival probability, indicating that they were the most burdensome. Adherence to and quality of home spirometry were analyzed; of the 7200 sessions requested, 4248 (59%) were performed. Of these, 90.3% (3836/4248) were of acceptable quality according to American Thoracic Society grading. Inclusion of protocol holidays improved retention measures. The technologies used were generally well received. CONCLUSIONS Our findings provide evidence supporting the feasibility and acceptability of remote monitoring for capturing both subjective and objective data from various sources for respiratory diseases. The high engagement level observed with passively collected data suggests the potential of wearables for long-term, user-friendly remote monitoring in respiratory disease management. The unique piloting of certain features such as protocol holidays, alert notifications for missing data, and flexible support from the study team provides a reference for future studies in this field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/28873.
Collapse
Affiliation(s)
- Malik A Althobiani
- Respiratory Medicine, University College London, London, United Kingdom
- Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom
- Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Joseph Jacob
- Respiratory Medicine, University College London, London, United Kingdom
- Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Richard James Butler Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom
| | - Joanna C Porter
- Respiratory Medicine, University College London, London, United Kingdom
- Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom
| | - John R Hurst
- Respiratory Medicine, University College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- National Institute for Health and Care Research, Biomedical Research Centre at University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom
| |
Collapse
|
13
|
Cummins N, Dineley J, Conde P, Matcham F, Siddi S, Lamers F, Carr E, Lavelle G, Leightley D, White KM, Oetzmann C, Campbell EL, Simblett S, Bruce S, Haro JM, Penninx BWJH, Ranjan Y, Rashid Z, Stewart C, Folarin AA, Bailón R, Schuller BW, Wykes T, Vairavan S, Dobson RJB, Narayan VA, Hotopf M. Multilingual markers of depression in remotely collected speech samples: A preliminary analysis. J Affect Disord 2023; 341:128-136. [PMID: 37598722 DOI: 10.1016/j.jad.2023.08.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. METHODS We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. RESULTS Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. LIMITATIONS Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. CONCLUSIONS Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.
Collapse
Affiliation(s)
- Nicholas Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Judith Dineley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- School of Psychology, University of Sussex, Falmer, UK; Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Barcelona, Spain
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Ewan Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Grace Lavelle
- School of Psychology, University of Sussex, Falmer, UK
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Edward L Campbell
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; GTM research group, AtlanTTic Research Center, University of Vigo, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, King's College London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Amos A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, Aragon Institute for Engineering Research, University of Zaragoza, Zaragoza, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain
| | - Björn W Schuller
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, UK
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
| | | | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | | | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King's College London, London, UK
| |
Collapse
|
14
|
Abreu M, Carmo AS, Peralta AR, Sá F, Plácido da Silva H, Bentes C, Fred AL. PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy. Front Physiol 2023; 14:1248899. [PMID: 37881691 PMCID: PMC10597694 DOI: 10.3389/fphys.2023.1248899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/04/2023] [Indexed: 10/27/2023] Open
Abstract
The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.
Collapse
Affiliation(s)
- Mariana Abreu
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Sofia Carmo
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Peralta
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Francisca Sá
- Departamento Neurologia, Centro Hospitalar Lisboa Ocidental, Hospital Egas Moniz, Lisboa, Portugal
| | - Hugo Plácido da Silva
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
- Lisbon Unit for Learning and Intelligent Systems (LUMLIS), A Unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), Lisboa, Portugal
| | - Carla Bentes
- Lab EEG-Sono, Centro Hospitalar Universitário Lisboa Norte, Hospital de Santa Maria, Lisboa, Portugal
| | - Ana Luísa Fred
- Instituto de Telecomunicações, Lisboa, Portugal
- Departamento de Bioengenharia, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| |
Collapse
|
15
|
Labbaf S, Abbasian M, Azimi I, Dutt N, Rahmani AM. ZotCare: a flexible, personalizable, and affordable mhealth service provider. Front Digit Health 2023; 5:1253087. [PMID: 37781455 PMCID: PMC10539601 DOI: 10.3389/fdgth.2023.1253087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023] Open
Abstract
The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.
Collapse
Affiliation(s)
- Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Mahyar Abbasian
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Iman Azimi
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Amir M. Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
- Institute for Future Health, University of California, Irvine, Irvine, CA, United States
- School of Nursing, University of California, Irvine, Irvine, CA, United States
| |
Collapse
|
16
|
Sun S, Folarin AA, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Vairavan S, Narayan VA, Annas P, Hotopf M, Dobson RJB. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. J Med Internet Res 2023; 25:e45233. [PMID: 37578823 PMCID: PMC10463088 DOI: 10.2196/45233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/11/2023] [Accepted: 04/23/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.
Collapse
Affiliation(s)
- Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| | - Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
- The Romanian League for Mental Health, Bucharest, Romania
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Physical Activity and Functional Capacity Research Group, Faculty of Health Care and Social Services, LAB University of Applied Sciences, Lahti, Finland
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
17
|
Muurling M, Pasmooij AMG, Koychev I, Roik D, Froelich L, Schwertner E, Religa D, Abdelnour C, Boada M, Almici M, Galluzzi S, Cardoso S, de Mendonça A, Owens AP, Kuruppu S, Gjestsen MT, Lazarou I, Gkioka M, Tsolaki M, Diaz A, Gove D, Visser PJ, Aarsland D, Lucivero F, de Boer C. Ethical challenges of using remote monitoring technologies for clinical research: A case study of the role of local research ethics committees in the RADAR-AD study. PLoS One 2023; 18:e0285807. [PMID: 37418385 PMCID: PMC10328231 DOI: 10.1371/journal.pone.0285807] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/02/2023] [Indexed: 07/09/2023] Open
Abstract
INTRODUCTION Clinical research with remote monitoring technologies (RMTs) has multiple advantages over standard paper-pencil tests, but also raises several ethical concerns. While several studies have addressed the issue of governance of big data in clinical research from the legal or ethical perspectives, the viewpoint of local research ethics committee (REC) members is underrepresented in the current literature. The aim of this study is therefore to find which specific ethical challenges are raised by RECs in the context of a large European study on remote monitoring in all syndromic stages of Alzheimer's disease, and what gaps remain. METHODS Documents describing the REC review process at 10 sites in 9 European countries from the project Remote Assessment of Disease and Relapse-Alzheimer's Disease (RADAR-AD) were collected and translated. Main themes emerging in the documents were identified using a qualitative analysis approach. RESULTS Four main themes emerged after analysis: data management, participant's wellbeing, methodological issues, and the issue of defining the regulatory category of RMTs. Review processes differed across sites: process duration varied from 71 to 423 days, some RECs did not raise any issues, whereas others raised up to 35 concerns, and the approval of a data protection officer was needed in half of the sites. DISCUSSION The differences in the ethics review process of the same study protocol across different local settings suggest that a multi-site study would benefit from a harmonization in research ethics governance processes. More specifically, some best practices could be included in ethical reviews across institutional and national contexts, such as the opinion of an institutional data protection officer, patient advisory board reviews of the protocol and plans for how ethical reflection is embedded within the study.
Collapse
Affiliation(s)
- Marijn Muurling
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Dora Roik
- Department of Geriatric Psychiatry, Central Institute for Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Lutz Froelich
- Department of Geriatric Psychiatry, Central Institute for Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Emilia Schwertner
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Krakow, Poland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
| | - Carla Abdelnour
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Mercè Boada
- Ace Alzheimer Center Barcelona–Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Monica Almici
- Laboratory Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Samantha Galluzzi
- Laboratory Alzheimer’s Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sandra Cardoso
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| | | | - Andrew P. Owens
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Sajini Kuruppu
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | | | - Ioulietta Lazarou
- Information Technologies Institute, Center for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Greece
| | - Mara Gkioka
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI–AUTh), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Magda Tsolaki
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI–AUTh), Balkan Center, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ana Diaz
- Alzheimer Europe, Luxembourg, Luxembourg
| | | | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Division of Clinical Geriatrics, Department of Neurobiology, Department of Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Dag Aarsland
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Federica Lucivero
- Ethox and Welcome Centre for Ethics and Humanities, University of Oxford, Oxford, United Kingdom
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | | |
Collapse
|
18
|
Vazquez CG, Eicher C, Huber R, Kronenberg G, Landolt HP, Seifritz E, Poian GD. Uncovering Emotions: A Pilot Study on Classifying Moods in the Valence-Arousal Space using In-the-Wild Passive Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083003 DOI: 10.1109/embc40787.2023.10340513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Mood classification from passive data promises to provide an unobtrusive way to track a person's emotions over time. In this exploratory study, we collected phone sensor data and physiological signals from 8 individuals, including 5 healthy participants and 3 depressed patients, for a maximum of 35 days. Participants were asked to answer a digital questionnaire three times daily, resulting in a total of 334 self-reported mood state samples. Gradient-boosting classification was applied to the collected passive data to categorize 4 mood states in the Valence-Energetic Arousal space. The cross-validation results showed better classification performance compared to a baseline model, which always predicts the majority class. The classifier using passive data had an area under the precision-recall curve of 0.39 (SD = 0.1) while the baseline had 0.26 (SD = 0.03), suggesting the presence of information in the collected features that support the classification process. The model identified the entropy of the heart rate and the average physical activity in the preceding 8 hours, along with the max normal-to-normal (NN) sinus beat interval and the NN low frequency-high frequency ratio during the questionnaire completion, as the most important features in its analysis. Additionally, the time range of data collection was considered a contextual factor.
Collapse
|
19
|
Loukas VS, Kassiotis T, Martinez IL, Koumakis L, Bruinsma J, Pasciuti R, Balatresi M, Tenhunen V, Fiakkas A, Ataliani L, Karanasiou GS, Tsiknakis M, Hilberger H, Bodenler M, Schnalzer B, Huber S, Pirani M, Colombo M, Hanke S, Fotiadis DI. LETHE: A Digital Intervention for Cognitive Decline. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083735 DOI: 10.1109/embc40787.2023.10340897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.
Collapse
|
20
|
Siddi S, Bailon R, Giné-Vázquez I, Matcham F, Lamers F, Kontaxis S, Laporta E, Garcia E, Lombardini F, Annas P, Hotopf M, Penninx BWJH, Ivan A, White KM, Difrancesco S, Locatelli P, Aguiló J, Peñarrubia-Maria MT, Narayan VA, Folarin A, Leightley D, Cummins N, Vairavan S, Ranjan Y, Rintala A, de Girolamo G, Simblett SK, Wykes T, Myin-Germeys I, Dobson R, Haro JM. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity. Psychol Med 2023; 53:3249-3260. [PMID: 37184076 DOI: 10.1017/s0033291723001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
Collapse
Affiliation(s)
- S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - R Bailon
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - I Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - F Matcham
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- School of Psychology, University of Sussex, Falmer, UK
| | - F Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - S Kontaxis
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Laporta
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Garcia
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - M Hotopf
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - A Ivan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - K M White
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Difrancesco
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - P Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - J Aguiló
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - M T Peñarrubia-Maria
- Catalan Institute of Health, Primary Care Research Institute (IDIAP Jordi Gol), CIBERESP, Barcelona, Spain
| | - V A Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - A Folarin
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - D Leightley
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - N Cummins
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Vairavan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Y Ranjan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - A Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - S K Simblett
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - T Wykes
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - R Dobson
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| |
Collapse
|
21
|
Hidd VV, López E, Centellegher S, Roberts SGB, Lepri B, Dunbar RIM. The stability of transient relationships. Sci Rep 2023; 13:6120. [PMID: 37059731 PMCID: PMC10104882 DOI: 10.1038/s41598-023-32206-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 03/24/2023] [Indexed: 04/16/2023] Open
Abstract
In contrast to long-term relationships, far less is known about the temporal evolution of transient relationships, although these constitute a substantial fraction of people's communication networks. Previous literature suggests that ratings of relationship emotional intensity decay gradually until the relationship ends. Using mobile phone data from three countries (US, UK, and Italy), we demonstrate that the volume of communication between ego and its transient alters does not display such a systematic decay, instead showing a lack of any dominant trends. This means that the communication volume of egos to groups of similar transient alters is stable. We show that alters with longer lifetimes in ego's network receive more calls, with the lifetime of the relationship being predictable from call volume within the first few weeks of first contact. This is observed across all three countries, which include samples of egos at different life stages. The relation between early call volume and lifetime is consistent with the suggestion that individuals initially engage with a new alter so as to evaluate their potential as a tie in terms of homophily.
Collapse
Affiliation(s)
- Valentín Vergara Hidd
- Computational and Data Sciences Department, George Mason University, Fairfax, 22030, USA.
| | - Eduardo López
- Computational and Data Sciences Department, George Mason University, Fairfax, 22030, USA
| | - Simone Centellegher
- Fondazione Bruno Kessler, Mobile and Social Computing Lab, Trento, 38123, Italy
| | - Sam G B Roberts
- School of Psychology, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Bruno Lepri
- Fondazione Bruno Kessler, Mobile and Social Computing Lab, Trento, 38123, Italy
| | - Robin I M Dunbar
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 66G, UK
| |
Collapse
|
22
|
González-Pérez A, Matey-Sanz M, Granell C, Diaz-Sanahuja L, Bretón-López J, Casteleyn S. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. J Biomed Inform 2023; 141:104359. [PMID: 37044134 DOI: 10.1016/j.jbi.2023.104359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/10/2023] [Accepted: 04/05/2023] [Indexed: 04/14/2023]
Abstract
In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.
Collapse
Affiliation(s)
- Alberto González-Pérez
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Miguel Matey-Sanz
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Carlos Granell
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| | - Laura Diaz-Sanahuja
- Department of Basic Psychology, Clinical and Psychobiology, Universitat Jaume I, Castellon, 12071, Spain.
| | - Juana Bretón-López
- Department of Basic Psychology, Clinical and Psychobiology, Universitat Jaume I, Castellon, 12071, Spain; CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, 28029, Spain.
| | - Sven Casteleyn
- GEOTEC Research Group, Institute of New Imaging Technologies, Universitat Jaume I, Castellon, 12071, Spain.
| |
Collapse
|
23
|
Hicks JL, Boswell MA, Althoff T, Crum AJ, Ku JP, Landay JA, Moya PML, Murnane EL, Snyder MP, King AC, Delp SL. Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annu Rev Public Health 2023; 44:131-150. [PMID: 36542772 PMCID: PMC10523351 DOI: 10.1146/annurev-publhealth-060220-041643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.
Collapse
Affiliation(s)
- Jennifer L Hicks
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Melissa A Boswell
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Tim Althoff
- Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA
| | - Alia J Crum
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - James A Landay
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Paula M L Moya
- Department of English and the Center for Comparative Studies in Race and Ethnicity, Stanford University, Stanford, California, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Abby C King
- Department of Epidemiology and Population Health, and Department of Medicine (Stanford Prevention Research Center), Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Scott L Delp
- Department of Bioengineering and Department of Mechanical Engineering, Stanford University, Stanford, California, USA
| |
Collapse
|
24
|
Lavalle R, Condominas E, Haro JM, Giné-Vázquez I, Bailon R, Laporta E, Garcia E, Kontaxis S, Alacid GR, Lombardini F, Preti A, Peñarrubia-Maria MT, Coromina M, Arranz B, Vilella E, Rubio-Alacid E, Matcham F, Lamers F, Hotopf M, Penninx BWJH, Annas P, Narayan V, Simblett SK, Siddi S. The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5161. [PMID: 36982069 PMCID: PMC10048808 DOI: 10.3390/ijerph20065161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/03/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression's levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the "new normality", while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them.
Collapse
Affiliation(s)
- Raffaele Lavalle
- Dipartimento di Neuroscienze, Università degli Studi di Torino, 10124 Turin, Italy
| | - Elena Condominas
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Iago Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Raquel Bailon
- Aragón Institute of Engineering Research (I3A), Instituto de Investigación Sanitaria de Aragón (IIS Aragón), University of Zaragoza, 50018 Zaragoza, Spain
- Centros de Investigación Biomédica en Red en el área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Estela Laporta
- Centros de Investigación Biomédica en Red en el área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Ester Garcia
- Centros de Investigación Biomédica en Red en el área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, 08193 Bellaterra, Spain
| | - Spyridon Kontaxis
- Aragón Institute of Engineering Research (I3A), Instituto de Investigación Sanitaria de Aragón (IIS Aragón), University of Zaragoza, 50018 Zaragoza, Spain
- Centros de Investigación Biomédica en Red en el área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Gemma Riquelme Alacid
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Federica Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Antonio Preti
- Dipartimento di Neuroscienze, Università degli Studi di Torino, 10124 Turin, Italy
| | - Maria Teresa Peñarrubia-Maria
- Health Technology Assessment in Primary Care and Mental Health (PRISMA) Research Group, Parc Sanitari Sant Joan de Deu, Institut de Recerca Sant Joan de Deu, 08830 St Boi de Llobregat, Spain
- Unitat de Suport a la Recerca Regió Metropolitana Sud, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
| | - Marta Coromina
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Belén Arranz
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, 43206 Reus, Spain
- Neuriociències i Salut Mental, Institut d’Investigació Sanitària Pere Virgili-CERCA, 43204 Reus, Spain
- Universitat Rovira i Virgili, 43003 Reus, Spain
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Elena Rubio-Alacid
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
- Centro de Investigación Biomédica en Red en Salud Mental, CIBERSAM-Instituto de Salud Carlos III, 28029 Madrid, Spain
| | | | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- School of Psychology, University of Sussex, East Sussex BN1 9QH, UK
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health Program, 1081 BT Amsterdam, The Netherlands
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health Program, 1081 BT Amsterdam, The Netherlands
| | | | - Vaibhav Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ 08560, USA
| | - Sara K. Simblett
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Departament de Medicina, Universitat de Barcelona, 08830 Barcelona, Spain
| | | |
Collapse
|
25
|
Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Form Res 2023; 7:e43296. [PMID: 36881444 PMCID: PMC10031448 DOI: 10.2196/43296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The experience sampling methodology (ESM) has long been considered as the gold standard for gathering data in everyday life. In contrast, current smartphone technology enables us to acquire data that are much richer, more continuous, and unobtrusive than is possible via ESM. Although data obtained from smartphones, known as mobile sensing, can provide useful information, its stand-alone usefulness is limited when not combined with other sources of information such as data from ESM studies. Currently, there are few mobile apps available that allow researchers to combine the simultaneous collection of ESM and mobile sensing data. Furthermore, such apps focus mostly on passive data collection with only limited functionality for ESM data collection. OBJECTIVE In this paper, we presented and evaluated the performance of m-Path Sense, a novel, full-fledged, and secure ESM platform with background mobile sensing capabilities. METHODS To create an app with both ESM and mobile sensing capabilities, we combined m-Path, a versatile and user-friendly platform for ESM, with the Copenhagen Research Platform Mobile Sensing framework, a reactive cross-platform framework for digital phenotyping. We also developed an R package, named mpathsenser, which extracts raw data to an SQLite database and allows the user to link and inspect data from both sources. We conducted a 3-week pilot study in which we delivered ESM questionnaires while collecting mobile sensing data to evaluate the app's sampling reliability and perceived user experience. As m-Path is already widely used, the ease of use of the ESM system was not investigated. RESULTS Data from m-Path Sense were submitted by 104 participants, totaling 69.51 GB (430.43 GB after decompression) or approximately 37.50 files or 31.10 MB per participant per day. After binning accelerometer and gyroscope data to 1 value per second using summary statistics, the entire SQLite database contained 84,299,462 observations and was 18.30 GB in size. The reliability of sampling frequency in the pilot study was satisfactory for most sensors, based on the absolute number of collected observations. However, the relative coverage rate-the ratio between the actual and expected number of measurements-was below its target value. This could mostly be ascribed to gaps in the data caused by the operating system pushing away apps running in the background, which is a well-known issue in mobile sensing. Finally, some participants reported mild battery drain, which was not considered problematic for the assessed participants' perceived user experience. CONCLUSIONS To better study behavior in everyday life, we developed m-Path Sense, a fusion of both m-Path for ESM and Copenhagen Research Platform Mobile Sensing. Although reliable passive data collection with mobile phones remains challenging, it is a promising approach toward digital phenotyping when combined with ESM.
Collapse
Affiliation(s)
- Koen Niemeijer
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Merijn Mestdagh
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Stijn Verdonck
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Kristof Meers
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Peter Kuppens
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
| |
Collapse
|
26
|
Zhang Y, Pratap A, Folarin AA, Sun S, Cummins N, Matcham F, Vairavan S, Dineley J, Ranjan Y, Rashid Z, Conde P, Stewart C, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Rambla CH, Simblett S, Nica R, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Annas P, Narayan VA, Hotopf M, Dobson RJB. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med 2023; 6:25. [PMID: 36806317 PMCID: PMC9938183 DOI: 10.1038/s41746-023-00749-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 01/10/2023] [Indexed: 02/19/2023] Open
Abstract
Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Abhishek Pratap
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Krembil Center for Neuroinformatics, CAMH, Toronto, ON, Canada.
- University of Toronto, Toronto, ON, Canada.
- University of Washington, Seattle, WA, USA.
- Davos Alzheimer's Collaborative, Geneva, Switzerland.
| | - Amos A Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- University College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Shaoxiong Sun
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology, University of Sussex, Falmer, East Sussex, UK
| | | | - Judith Dineley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katie M White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alina Ivan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Femke Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Carla Hernández Rambla
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King's College London, London, UK
- The Romanian League for Mental Health, Bucharest, Romania
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Vaibhav A Narayan
- Davos Alzheimer's Collaborative, Geneva, Switzerland
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- University College London, London, UK.
- South London and Maudsley NHS Foundation Trust, London, UK.
- Health Data Research UK London, University College London, London, UK.
| |
Collapse
|
27
|
Mavragani A, Bruhin LC, Schütz N, Naef AC, Hegi H, Reuse P, Schindler KA, Krack P, Wiest R, Chan A, Nef T, Gerber SM. Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report. JMIR Form Res 2023; 7:e43092. [PMID: 36800219 PMCID: PMC9985000 DOI: 10.2196/43092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals' everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.
Collapse
Affiliation(s)
| | - Lena C Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Narayan Schütz
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,DomoHealth SA, Lausanne, Switzerland
| | - Aileen C Naef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Heinz Hegi
- Department of Sport Science, University of Bern, Bern, Switzerland
| | - Pascal Reuse
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Kaspar A Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| |
Collapse
|
28
|
Kushniruk A, Dawe-Lane E, Siddi S, Lamers F, Simblett S, Riquelme Alacid G, Ivan A, Myin-Germeys I, Haro JM, Oetzmann C, Popat P, Rintala A, Rubio-Abadal E, Wykes T, Henderson C, Hotopf M, Matcham F. Understanding the Subjective Experience of Long-term Remote Measurement Technology Use for Symptom Tracking in People With Depression: Multisite Longitudinal Qualitative Analysis. JMIR Hum Factors 2023; 10:e39479. [PMID: 36701179 PMCID: PMC9945920 DOI: 10.2196/39479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 11/07/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Remote measurement technologies (RMTs) have the potential to revolutionize major depressive disorder (MDD) disease management by offering the ability to assess, monitor, and predict symptom changes. However, the promise of RMT data depends heavily on sustained user engagement over extended periods. In this paper, we report a longitudinal qualitative study of the subjective experience of people with MDD engaging with RMTs to provide insight into system usability and user experience and to provide the basis for future promotion of RMT use in research and clinical practice. OBJECTIVE We aimed to understand the subjective experience of long-term engagement with RMTs using qualitative data collected in a longitudinal study of RMTs for monitoring MDD. The objectives were to explore the key themes associated with long-term RMT use and to identify recommendations for future system engagement. METHODS In this multisite, longitudinal qualitative research study, 124 semistructured interviews were conducted with 99 participants across the United Kingdom, Spain, and the Netherlands at 3-month, 12-month, and 24-month time points during a study exploring RMT use (the Remote Assessment of Disease and Relapse-Major Depressive Disorder study). Data were analyzed using thematic analysis, and interviews were audio recorded, transcribed, and coded in the native language, with the resulting quotes translated into English. RESULTS There were 5 main themes regarding the subjective experience of long-term RMT use: research-related factors, the utility of RMTs for self-management, technology-related factors, clinical factors, and system amendments and additions. CONCLUSIONS The subjective experience of long-term RMT use can be considered from 2 main perspectives: experiential factors (how participants construct their experience of engaging with RMTs) and system-related factors (direct engagement with the technologies). A set of recommendations based on these strands are proposed for both future research and the real-world implementation of RMTs into clinical practice. Future exploration of experiential engagement with RMTs will be key to the successful use of RMTs in clinical care.
Collapse
Affiliation(s)
| | - Erin Dawe-Lane
- Department of Psychology, King's College London, London, United Kingdom
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Femke Lamers
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, Netherlands
| | - Sara Simblett
- Department of Psychology, King's College London, London, United Kingdom
| | - Gemma Riquelme Alacid
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Alina Ivan
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, UK Leuven, Leuven, Belgium
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Carolin Oetzmann
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Priya Popat
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Aki Rintala
- Center for Contextual Psychiatry, Department of Neurosciences, UK Leuven, Leuven, Belgium
| | - Elena Rubio-Abadal
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Til Wykes
- Department of Psychology, King's College London, London, United Kingdom
| | - Claire Henderson
- Health Service & Population Research Department, King's College London, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, King's College London, London, United Kingdom.,School of Psychology, University of Sussex, Falmer, Sussex, United Kingdom
| |
Collapse
|
29
|
de Angel V, Adeleye F, Zhang Y, Cummins N, Munir S, Lewis S, Laporta Puyal E, Matcham F, Sun S, Folarin AA, Ranjan Y, Conde P, Rashid Z, Dobson R, Hotopf M. The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement. JMIR Ment Health 2023; 10:e42866. [PMID: 36692937 PMCID: PMC9906314 DOI: 10.2196/42866] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/10/2022] [Accepted: 11/26/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. OBJECTIVE A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. METHODS A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. RESULTS The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. CONCLUSIONS Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
Collapse
Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Fadekemi Adeleye
- Department of Psychology, King's College London, London, United Kingdom
| | - Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sara Munir
- Lewisham Talking Therapies, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Estela Laporta Puyal
- Biomedical Signal Interpretation and Computational Simulation Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Dobson
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
30
|
Sharing Health Information Using a Blockchain. Healthcare (Basel) 2023; 11:healthcare11020170. [PMID: 36673538 PMCID: PMC9859363 DOI: 10.3390/healthcare11020170] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/26/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both retrospective and prospective research at a low cost. Similarly, the Organisation for Economic Co-operation and Development (OECD) encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper proposes adopting a blockchain framework to enable the transparent sharing of medical information among health entities in a secure environment. We develop a laboratory-based prototype using a design science research methodology (DSRM). This approach has its roots in the sciences of engineering and artificial intelligence, and its primary goal is to create relevant artefacts that add value to the fields in which they are used. We adopt a patient-centric approach, according to which a patient is the owner of their data and may allow hospitals and health professionals access to their data.
Collapse
|
31
|
Denyer H, Ramos-Quiroga JA, Folarin A, Ramos C, Nemeth P, Bilbow A, Woodward E, Whitwell S, Müller-Sedgwick U, Larsson H, Dobson RJ, Kuntsi J. ADHD Remote Technology study of cardiometabolic risk factors and medication adherence (ART-CARMA): a multi-centre prospective cohort study protocol. BMC Psychiatry 2022; 22:813. [PMID: 36539756 PMCID: PMC9764531 DOI: 10.1186/s12888-022-04429-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Emerging evidence points at substantial comorbidity between adult attention deficit hyperactivity disorder (ADHD) and cardiometabolic diseases, but our understanding of the comorbidity and how to manage cardiometabolic disease in adults with ADHD is limited. The ADHD Remote Technology study of cardiometabolic risk factors and medication adherence (ART-CARMA) project uses remote measurement technology to obtain real-world data from daily life to assess the extent to which ADHD medication treatment and physical activity, individually and jointly, may influence cardiometabolic risks in adults with ADHD. Our second main aim is to obtain valuable real-world data on adherence to pharmacological treatment and its predictors and correlates during daily life from adults with ADHD. METHODS ART-CARMA is a multi-site prospective cohort study within the EU-funded collaboration 'TIMESPAN' (Management of chronic cardiometabolic disease and treatment discontinuity in adult ADHD patients) that will recruit 300 adults from adult ADHD waiting lists. The participants will be monitored remotely over a period of 12 months that starts from pre-treatment initiation. Passive monitoring, which involves the participants wearing a wrist-worn device (EmbracePlus) and downloading the RADAR-base Passive App and the Empatica Care App on their smartphone, provides ongoing data collection on a wide range of variables, such as physical activity, sleep, pulse rate (PR) and pulse rate variability (PRV), systolic peaks, electrodermal activity (EDA), oxygen saturation (SpO2), peripheral temperature, smartphone usage including social connectivity, and the environment (e.g. ambient noise, light levels, relative location). By combining data across these variables measured, processes such as physical activity, sleep, autonomic arousal, and indicators of cardiovascular health can be captured. Active remote monitoring involves the participant completing tasks using a smartphone app (such as completing clinical questionnaires or speech tasks), measuring their blood pressure and weight, or using a PC/laptop (cognitive tasks). The ART system is built on the RADAR-base mobile-health platform. DISCUSSION The long-term goal is to use these data to improve the management of cardiometabolic disease in adults with ADHD, and to improve ADHD medication treatment adherence and the personalisation of treatment.
Collapse
Affiliation(s)
- Hayley Denyer
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK.
| | - J Antoni Ramos-Quiroga
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain
- Group of Psychiatry, Mental Health and Addictions, Vall d'Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Catalonia, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Amos Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Carolina Ramos
- Group of Psychiatry, Mental Health and Addictions, Vall d'Hebron Research Institute (VHIR), Barcelona, Catalonia, Spain
| | | | - Andrea Bilbow
- The National Attention Deficit Disorder Information and Support Service, ADDISS, Edgware, Middlesex, UK
| | - Euan Woodward
- European Association for the Study of Obesity - Ireland, Dublin, Ireland
| | | | - Ulrich Müller-Sedgwick
- Adult Neurodevelopmental Service, Health and Community Services, Government of Jersey, St Helier, Jersey
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Richard Jb Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
| |
Collapse
|
32
|
Böttcher S, Vieluf S, Bruno E, Joseph B, Epitashvili N, Biondi A, Zabler N, Glasstetter M, Dümpelmann M, Van Laerhoven K, Nasseri M, Brinkman BH, Richardson MP, Schulze-Bonhage A, Loddenkemper T. Data quality evaluation in wearable monitoring. Sci Rep 2022; 12:21412. [PMID: 36496546 PMCID: PMC9741649 DOI: 10.1038/s41598-022-25949-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.
Collapse
Affiliation(s)
- Sebastian Böttcher
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Solveig Vieluf
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
| | - Elisa Bruno
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Boney Joseph
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Nino Epitashvili
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Andrea Biondi
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Nicolas Zabler
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Martin Glasstetter
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany ,grid.5963.9Department of Microsystems Engineering (IMTEK), University of Freiburg, Freiburg, Germany
| | - Kristof Van Laerhoven
- grid.5836.80000 0001 2242 8751Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mona Nasseri
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA ,grid.266865.90000 0001 2109 4358School of Engineering, University of North Florida, Jacksonville, FL USA
| | - Benjamin H. Brinkman
- grid.66875.3a0000 0004 0459 167XBioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN USA
| | - Mark P. Richardson
- grid.13097.3c0000 0001 2322 6764Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | - Andreas Schulze-Bonhage
- grid.7708.80000 0000 9428 7911Department of Neurosurgery, Epilepsy Center, Medical Center – University of Freiburg, Freiburg, Germany
| | - Tobias Loddenkemper
- grid.38142.3c000000041936754XDivision of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MS USA
| |
Collapse
|
33
|
Sun S, Folarin AA, Zhang Y, Cummins N, Liu S, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Dalla Costa G, Leocani L, Sørensen PS, Magyari M, Guerrero AI, Zabalza A, Vairavan S, Bailon R, Simblett S, Myin-Germeys I, Rintala A, Wykes T, Narayan VA, Hotopf M, Comi G, Dobson RJ. The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107204. [PMID: 36371974 DOI: 10.1016/j.cmpb.2022.107204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/27/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions. METHODS In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS). RESULTS The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT. CONCLUSIONS This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.
Collapse
Affiliation(s)
- Shaoxiong Sun
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Amos A Folarin
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK
| | - Yuezhou Zhang
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicholas Cummins
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Shuo Liu
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Germany
| | - Callum Stewart
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yatharth Ranjan
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petroula Laiou
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Heet Sankesara
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Letizia Leocani
- Vita-Salute University and Experimental Neurophysiology Unit, Institute of Experimental Neurology-INSPE, Scientific Institute San Raffaele, Milan, Italy
| | - Per Soelberg Sørensen
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Department of Neurology, Danish Multiple Sclerosis Center, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ana Isabel Guerrero
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Institut de Recerca, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Raquel Bailon
- Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, Zaragoza, Spain; Centro de Investigacion Biomedica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Inez Myin-Germeys
- Department of Neurosciences, Centre for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Aki Rintala
- Department of Neurosciences, Centre for Contextual Psychiatry, KU Leuven, Leuven, Belgium; Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Giancarlo Comi
- Vita Salute San Raffaele University, Milan, Italy; Casa di Cura Privata del Policlinico, Milan, Italy
| | - Richard Jb Dobson
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Institute of Health Informatics, University College London, London, UK.
| |
Collapse
|
34
|
Siddi S, Giné-Vázquez I, Bailon R, Matcham F, Lamers F, Kontaxis S, Laporta E, Garcia E, Arranz B, Dalla Costa G, Guerrero AI, Zabalza A, Buron MD, Comi G, Leocani L, Annas P, Hotopf M, Penninx BWJH, Magyari M, Sørensen PS, Montalban X, Lavelle G, Ivan A, Oetzmann C, White KM, Difrancesco S, Locatelli P, Mohr DC, Aguiló J, Narayan V, Folarin A, Dobson RJB, Dineley J, Leightley D, Cummins N, Vairavan S, Ranjan Y, Rashid Z, Rintala A, Girolamo GD, Preti A, Simblett S, Wykes T, Myin-Germeys I, Haro JM. Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis. J Clin Med 2022; 11:7163. [PMID: 36498739 PMCID: PMC9738639 DOI: 10.3390/jcm11237163] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/22/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS). METHODS Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse-Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. RESULTS Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods. CONCLUSIONS Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.
Collapse
Affiliation(s)
- Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM (Madrid 28029), Universitat de Barcelona, 08007 Barcelona, Spain
| | - Iago Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM (Madrid 28029), Universitat de Barcelona, 08007 Barcelona, Spain
| | - Raquel Bailon
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50001 Zaragoza, Spain
- Centros de Investigación Biomédica en Red en el Área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Faith Matcham
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
- School of Psychology, University of Sussex, Falmer BN1 9QH, UK
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Spyridon Kontaxis
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, 50001 Zaragoza, Spain
- Centros de Investigación Biomédica en Red en el Área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Estela Laporta
- Centros de Investigación Biomédica en Red en el Área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Esther Garcia
- Centros de Investigación Biomédica en Red en el Área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, 08193 Bellaterra, Spain
| | - Belen Arranz
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM (Madrid 28029), Universitat de Barcelona, 08007 Barcelona, Spain
| | - Gloria Dalla Costa
- Faculty of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
| | - Ana Isabel Guerrero
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Vall d’Hebron Institut de Recerca, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Vall d’Hebron Institut de Recerca, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Mathias Due Buron
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Giancarlo Comi
- Faculty of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Casa Cura Policlinico, 20144 Milan, Italy
| | - Letizia Leocani
- Faculty of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
- Experimental Neurophysiology Unit, Institute of Experimental Neurology-INSPE, Scientific Institute San Raffaele, 20132 Milan, Italy
| | | | - Matthew Hotopf
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Melinda Magyari
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Per S. Sørensen
- Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Xavier Montalban
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Vall d’Hebron Institut de Recerca, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain
| | - Grace Lavelle
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Alina Ivan
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Katie M. White
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Sonia Difrancesco
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Patrick Locatelli
- Department of Engineering and Applied Science, University of Bergamo, 24129 Bergamo, Italy
| | - David C. Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jordi Aguiló
- Centros de Investigación Biomédica en Red en el Área de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, 08193 Bellaterra, Spain
| | - Vaibhav Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ 08560, USA
| | - Amos Folarin
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Richard J. B. Dobson
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Judith Dineley
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Daniel Leightley
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Nicholas Cummins
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Srinivasan Vairavan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ 08560, USA
| | - Yathart Ranjan
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Zulqarnain Rashid
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Aki Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, 7001 Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, 15210 Lahti, Finland
| | - Giovanni De Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy
| | - Antonio Preti
- Dipartimento di Neuroscienze, Università degli Studi di Torino, 10126 Torino, Italy
| | - Sara Simblett
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | - Til Wykes
- Institute of Psychiatry, King’s College London, Psychology and Neuroscience, London SE5 8AF, UK
| | | | - Inez Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, 7001 Leuven, Belgium
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM (Madrid 28029), Universitat de Barcelona, 08007 Barcelona, Spain
| | | |
Collapse
|
35
|
Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Qian L, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Annas P, Hotopf M, Dobson RJB. Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis. JMIR Mhealth Uhealth 2022; 10:e40667. [PMID: 36194451 PMCID: PMC9579931 DOI: 10.2196/40667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/11/2022] [Accepted: 08/26/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. METHODS We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. RESULTS Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). CONCLUSIONS This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Linglong Qian
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
36
|
Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung? PSYCHOLOGISCHE RUNDSCHAU 2022. [DOI: 10.1026/0033-3042/a000609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Digitale Phänotypisierung stellt einen neuen, leistungsstarken Ansatz zur Realisierung psychodiagnostischer Aufgaben in vielen Bereichen der Psychologie und Medizin dar. Die Grundidee besteht aus der Nutzung digitaler Spuren aus dem Alltag, um deren Vorhersagekraft für verschiedenste Anwendungsmöglichkeiten zu überprüfen und zu nutzen. Voraussetzungen für eine erfolgreiche Umsetzung sind elaborierte Smart Sensing Ansätze sowie Big Data-basierte Extraktions- (Data Mining) und Machine Learning-basierte Analyseverfahren. Erste empirische Studien verdeutlichen das hohe Potential, aber auch die forschungsmethodischen sowie ethischen und rechtlichen Herausforderungen, um über korrelative Zufallsbefunde hinaus belastbare Befunde zu gewinnen. Hierbei müssen rechtliche und ethische Richtlinien sicherstellen, dass die Erkenntnisse in einer für Einzelne und die Gesellschaft als Ganzes wünschenswerten Weise genutzt werden. Für die Psychologie als Lehr- und Forschungsdomäne bieten sich durch Digitale Phänotypisierung vielfältige Möglichkeiten, die zum einen eine gelebte Zusammenarbeit verschiedener Fachbereiche und zum anderen auch curriculare Erweiterungen erfordern. Die vorliegende narrative Übersicht bietet eine theoretische, nicht-technische Einführung in das Forschungsfeld der Digitalen Phänotypisierung, mit ersten empirischen Befunden sowie einer Diskussion der Möglichkeiten und Grenzen sowie notwendigen Handlungsfeldern.
Collapse
Affiliation(s)
- Harald Baumeister
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Patricia Garatva
- Abteilung für Klinische Psychologie und Psychotherapie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| | - Rüdiger Pryss
- Institut für Klinische Epidemiologie und Biometrie, Universität Würzburg, Deutschland
| | - Timo Ropinski
- Arbeitsgruppe Visual Computing, Institut für Medieninformatik, Universität Ulm, Deutschland
| | - Christian Montag
- Abteilung für Molekulare Psychologie, Institut für Psychologie und Pädagogik, Universität Ulm, Deutschland
| |
Collapse
|
37
|
Karthan M, Martin R, Holl F, Swoboda W, Kestler HA, Pryss R, Schobel J. Enhancing mHealth data collection applications with sensing capabilities. Front Public Health 2022; 10:926234. [PMID: 36187627 PMCID: PMC9521646 DOI: 10.3389/fpubh.2022.926234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/11/2022] [Indexed: 01/24/2023] Open
Abstract
Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.
Collapse
Affiliation(s)
- Maximilian Karthan
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany,*Correspondence: Maximilian Karthan
| | - Robin Martin
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Felix Holl
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany,Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Walter Swoboda
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, Ulm, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Johannes Schobel
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| |
Collapse
|
38
|
Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder. NPJ Digit Med 2022; 5:133. [PMID: 36057688 PMCID: PMC9440458 DOI: 10.1038/s41746-022-00680-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022] Open
Abstract
The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming popular, given their potential for providing rich data on symptom change and indicators of future state in recurrent conditions such as major depressive disorder (MDD). Understanding recruitment into RMT research is fundamental for improving historically small sample sizes, reducing loss of statistical power, and ultimately producing results worthy of clinical implementation. There is a need for the standardisation of best practices for successful recruitment into RMT research. The current paper reviews lessons learned from recruitment into the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study, a large-scale, multi-site prospective cohort study using RMT to explore the clinical course of people with depression across the UK, the Netherlands, and Spain. More specifically, the paper reflects on key experiences from the UK site and consolidates these into four key recruitment strategies, alongside a review of barriers to recruitment. Finally, the strategies and barriers outlined are combined into a model of lessons learned. This work provides a foundation for future RMT study design, recruitment and evaluation.
Collapse
|
39
|
Numan L, Moazeni M, Oerlemans MI, Aarts E, Van Der Kaaij NP, Asselbergs FW, Van Laake LW. Data-driven monitoring in patients on left ventricular assist device support. Expert Rev Med Devices 2022; 19:677-685. [DOI: 10.1080/17434440.2022.2132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Lieke Numan
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Mehran Moazeni
- Department of Methodology and Statistics, Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, the Netherlands
| | - Marish I.F.J. Oerlemans
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Emmeke Aarts
- Department of Methodology and Statistics, Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, the Netherlands
| | - Niels P. Van Der Kaaij
- Department of Cardiothoracic Surgery, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W. Asselbergs
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, Gower Street, WC1E 6BT, London, UK
- Health Data Research UK and Institute of Health Informatics, University College London, Gower Street, WC1E 6BT, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, the Netherlands
| | - Linda W. Van Laake
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, Gower Street WC1E 6BT, London, UK
| |
Collapse
|
40
|
de Angel V, Lewis S, White KM, Matcham F, Hotopf M. Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians. JMIR Ment Health 2022; 9:e38934. [PMID: 35969448 PMCID: PMC9425163 DOI: 10.2196/38934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Remote measurement technologies, such as smartphones and wearable devices, can improve treatment outcomes for depression through enhanced illness characterization and monitoring. However, little is known about digital outcomes that are clinically meaningful to patients and clinicians. Moreover, if these technologies are to be successfully implemented within treatment, stakeholders' views on the barriers to and facilitators of their implementation in treatment must be considered. OBJECTIVE This study aims to identify clinically meaningful targets for digital health research in depression and explore attitudes toward their implementation in psychological services. METHODS A grounded theory approach was used on qualitative data from 3 focus groups of patients with a current diagnosis of depression and clinicians with >6 months of experience with delivering psychotherapy (N=22). RESULTS Emerging themes on clinical targets fell into the following two main categories: promoters and markers of change. The former are behaviors that participants engage in to promote mental health, and the latter signal a change in mood. These themes were further subdivided into external changes (changes in behavior) or internal changes (changes in thoughts or feelings) and mapped with potential digital sensors. The following six implementation acceptability themes emerged: technology-related factors, information and data management, emotional support, cognitive support, increased self-awareness, and clinical utility. CONCLUSIONS The promoters versus markers of change differentiation have implications for a causal model of digital phenotyping in depression, which this paper presents. Internal versus external subdivisions are helpful in determining which factors are more susceptible to being measured by using active versus passive methods. The implications for implementation within psychotherapy are discussed with regard to treatment effectiveness, service provision, and patient and clinician experience.
Collapse
Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Psychology, University of Bath, Bath, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,School of Psychology, University of Sussex, Falmer, East Sussex, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
41
|
Matcham F, Carr E, White KM, Leightley D, Lamers F, Siddi S, Annas P, de Girolamo G, Haro JM, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr DC, Narayan VA, Penninx BWHJ, Oetzmann C, Coromina M, Simblett SK, Weyer J, Wykes T, Zorbas S, Brasen JC, Myin-Germeys I, Conde P, Dobson RJB, Folarin AA, Ranjan Y, Rashid Z, Cummins N, Dineley J, Vairavan S, Hotopf M. Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder. J Affect Disord 2022; 310:106-115. [PMID: 35525507 DOI: 10.1016/j.jad.2022.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
Collapse
Affiliation(s)
- F Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - E Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - K M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - D Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - F Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - M Horsfall
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - A Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - G Lavelle
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Q Li
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - D C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
| | - V A Narayan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - B W H J Penninx
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - C Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - M Coromina
- Parc Sanitari Joan de Déu, Barcelona, Spain
| | - S K Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Weyer
- RADAR-CNS Patient Advisory Board
| | - T Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - S Zorbas
- RADAR-CNS Patient Advisory Board
| | | | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - P Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - R J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - A A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Y Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Z Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - N Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Dineley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - S Vairavan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - M Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | | |
Collapse
|
42
|
Chen L, Ma X, Chatterjee M, Kortelainen JM, Ahmaniemi T, Maetzler W, Wang P, Zhang D. Fatigue and Sleep Assessment using Digital Sleep Trackers: Insights from a Multi-Device Pilot Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1133-1136. [PMID: 36086529 DOI: 10.1109/embc48229.2022.9870923] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
For the patient community with neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID), fatigue and sleep disturbances stand out as two of the most common and disabling symptoms, which mightily impair patient's quality of life. Traditional questionnaire-oriented approaches to reflect such symptoms suffer from recall bias and poor sensitivity to change. By virtue of multiple sensing modalities at home, IDEA-FAST project aims to identify novel digital endpoints of fatigue and sleep disturbances, that are objective, reliable and sensitive to change. This article presents and discusses results from a pilot study of IDEA-FAST to evaluate the feasibility of capturing sleep and fatigue measures from three sleep trackers. Data collected from 143 participants (age range: 21-82) across 6 disease groups and healthy cohort for a period of 9 months, were investigated using our proposed sensor analytical pipeline. The overall performance reveals that the median coverage rate of sleep trackers ranged from 48.3% to 76.9%. Furthermore, the digital measures obtained from each device, indicated a higher association with sleep related patient reported outcomes (PROs) than fatigue related ones, when taking all participants into account.
Collapse
|
43
|
Dimitriadis I, Mavroudopoulos I, Kyrama S, Toliopoulos T, Gounaris A, Vakali A, Billis A, Bamidis P. Scalable real-time health data sensing and analysis enabling collaborative care delivery. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
44
|
Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In sports feedback systems, digital systems perform tasks such as capturing, analysing and representing data. These systems not only aim to provide athletes and coaches with insights into performances but also help athletes learn new tasks and control movements, for example, to prevent injuries. However, designing mobile feedback systems requires a high level of expertise from researchers and practitioners in many areas. As a solution to this problem, we present Direct Mobile Coaching (DMC) as a design paradigm and model for mobile feedback systems. Besides components for feedback provisioning, the model consists of components for data recording, storage and management. For the evaluation of the model, its features are compared against state-of-the-art frameworks. Furthermore, the capabilities are benchmarked using a review of the literature. We conclude that DMC is capable of modelling all 39 identified systems while other identified frameworks (MobileCoach, Garmin Connect IQ SDK, RADAR) could (at best) only model parts of them. The presented design paradigm/model is applicable for a wide range of mobile feedback systems and equips researchers and practitioners with a valuable tool.
Collapse
|
45
|
Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. J Med Internet Res 2022; 24:e35951. [PMID: 35617003 PMCID: PMC9185357 DOI: 10.2196/35951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/14/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
Collapse
Affiliation(s)
- Ieuan Clay
- Digital Medicine Society, Boston, MA, United States
| | | | | | | | | | | | | | | | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Benjamin Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | | | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| |
Collapse
|
46
|
Abstract
BACKGROUND Digital phenotyping has been defined as the moment-by-moment assessment of an illness state through digital means, promising objective, quantifiable data on psychiatric patients' conditions, and could potentially improve diagnosis and management of mental illness. As it is a rapidly growing field, it is to be expected that new literature is being published frequently. OBJECTIVE We conducted this scoping review to assess the current state of literature on digital phenotyping and offer some discussion on the current trends and future direction of this area of research. METHODS We searched four databases, PubMed, Ovid MEDLINE, PsycINFO and Web of Science, from inception to August 25th, 2021. We included studies written in English that 1) investigated or applied their findings to diagnose psychiatric disorders and 2) utilized passive sensing for management or diagnosis. Protocols were excluded. A narrative synthesis approach was used, due to the heterogeneity and variability in outcomes and outcome types reported. RESULTS Of 10506 unique records identified, we included a total of 107 articles. The number of published studies has increased over tenfold from 2 in 2014 to 28 in 2020, illustrating the field's rapid growth. However, a significant proportion of these (49% of all studies and 87% of primary studies) were proof of concept, pilot or correlational studies examining digital phenotyping's potential. Most (62%) of the primary studies published evaluated individuals with depression (21%), BD (18%) and SZ (23%) (Appendix 1). CONCLUSION There is promise shown in certain domains of data and their clinical relevance, which have yet to be fully elucidated. A consensus has yet to be reached on the best methods of data collection and processing, and more multidisciplinary collaboration between physicians and other fields is needed to unlock the full potential of digital phenotyping and allow for statistically powerful clinical trials to prove clinical utility.
Collapse
Affiliation(s)
- Alex Z R Chia
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore
| | - Melvyn W B Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore City, Singapore.,National Addictions Management Service, Institute of Mental Health, Singapore City, Singapore
| |
Collapse
|
47
|
Kim S, Stanton K, Park Y, Thomas S. A Mobile App for Children With Asthma to Monitor Indoor Air Quality (AirBuddy): Development and Usability Study. JMIR Form Res 2022; 6:e37118. [PMID: 35604753 PMCID: PMC9171598 DOI: 10.2196/37118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/21/2022] [Accepted: 03/31/2022] [Indexed: 11/30/2022] Open
Abstract
Background Indoor air quality is an important environmental factor that triggers and exacerbates asthma, the most common chronic disease in children. A mobile app to monitor indoor air quality could help occupants keep their indoor air quality clean. However, no app is available that allows children to monitor and improve their indoor air quality. Objective Previously, we conducted a series of user-centered design studies to identify user needs and design requirements toward creating a mobile app that helps children with asthma to engage in monitoring and improving indoor air quality as part of their asthma management. Based on the findings from these studies, we created AirBuddy, a child-friendly app that visualizes air quality indoors and outdoors. Methods This paper reports on the findings from a field deployment with 7 pediatric asthma patients, where we evaluated AirBuddy’s usability and usefulness in real-world settings by conducting weekly semistructured interviews for 8 weeks. Results All participants positively responded to the usefulness and usability of AirBuddy, which we believe is thanks to the iterative, user-centered design approach that allowed us to identify and address potential usability issues early on and throughout the design process. Conclusions This project contributes to the field of mHealth app design for children by demonstrating how a user-centered design process can lead to the development of digital devices that are more acceptable and relevant to target users’ needs.
Collapse
Affiliation(s)
- Sunyoung Kim
- Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
| | - Kaitlyn Stanton
- Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
| | - Yunoh Park
- Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
| | - Stephen Thomas
- Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
| |
Collapse
|
48
|
Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. SENSORS 2022; 22:s22103893. [PMID: 35632301 PMCID: PMC9147201 DOI: 10.3390/s22103893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 12/10/2022]
Abstract
Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
Collapse
|
49
|
Andrews JA, Craven MP, Lang AR, Guo B, Morriss R, Hollis C. Making remote measurement technology work in multiple sclerosis, epilepsy and depression: survey of healthcare professionals. BMC Med Inform Decis Mak 2022; 22:125. [PMID: 35525933 PMCID: PMC9077644 DOI: 10.1186/s12911-022-01856-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 04/15/2022] [Indexed: 11/21/2022] Open
Abstract
Background Epilepsy, multiple sclerosis (MS) and depression are long term, central nervous system disorders which have a significant impact on everyday life. Evaluating symptoms of these conditions is problematic and typically involves repeated visits to a clinic. Remote measurement technology (RMT), consisting of smartphone apps and wearables, may offer a way to improve upon existing methods of managing these conditions. The present study aimed to establish the practical requirements that would enable clinical integration of data from patients’ RMT, according to healthcare professionals. Methods This paper reports findings from an online survey of 1006 healthcare professionals currently working in the care of people with epilepsy, MS or depression. The survey included questions on types of data considered useful, how often data should be collected, the value of RMT data, preferred methods of accessing the data, benefits and challenges to RMT implementation, impact of RMT data on clinical practice, and requirement for technical support. The survey was presented on the JISC online surveys platform. Results Among this sample of 1006 healthcare professionals, respondents were positive about the benefits of RMT, with 73.2% indicating their service would be likely or highly likely to benefit from the implementation of RMT in patient care plans. The data from patients’ RMT devices should be made available to all nursing and medical team members and could be reviewed between consultations where flagged by the system. However, results suggest it is also likely that RMT data would be reviewed in preparation for and during a consultation with a patient. Time to review information is likely to be one of the greatest barriers to successful implementation of RMT in clinical practice. Conclusions While further work would be required to quantify the benefits of RMT in clinical practice, the findings from this survey suggest that a wide array of clinical team members treating epilepsy, MS and depression would find benefit from RMT data in the care of their patients. Findings presented could inform the implementation of RMT and other digital interventions in the clinical management of a range of neurological and mental health conditions. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01856-z.
Collapse
Affiliation(s)
- J A Andrews
- NIHR MindTech MedTech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, UK. .,Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.
| | - M P Craven
- NIHR MindTech MedTech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, UK.,Human Factors Research Group, Faculty of Engineering, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - A R Lang
- Human Factors Research Group, Faculty of Engineering, University of Nottingham, Nottingham, UK
| | - B Guo
- ARC-EM, School of Medicine, University of Nottingham, Nottingham, UK
| | - R Morriss
- NIHR MindTech MedTech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, UK.,Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK.,ARC-EM, School of Medicine, University of Nottingham, Nottingham, UK
| | - C Hollis
- NIHR MindTech MedTech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, UK.,Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.,NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | | |
Collapse
|
50
|
Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients. SENSORS 2022; 22:s22093318. [PMID: 35591007 PMCID: PMC9105312 DOI: 10.3390/s22093318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/19/2022] [Accepted: 04/22/2022] [Indexed: 01/15/2023]
Abstract
Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.
Collapse
|