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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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4
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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5
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Denyer H, Deng Q, Adanijo A, Asherson P, Bilbow A, Folarin A, Groom MJ, Hollis C, Wykes T, Dobson RJ, Kuntsi J, Simblett S. Barriers to and Facilitators of Using Remote Measurement Technology in the Long-Term Monitoring of Individuals With ADHD: Interview Study. JMIR Form Res 2023; 7:e44126. [PMID: 37389932 PMCID: PMC10365629 DOI: 10.2196/44126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/29/2023] [Accepted: 04/13/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Remote measurement technology (RMT) has the potential to address current research and clinical challenges of attention-deficit/hyperactivity disorder (ADHD) symptoms and its co-occurring mental health problems. Despite research using RMT already being successfully applied to other populations, adherence and attrition are potential obstacles when applying RMT to a disorder such as ADHD. Hypothetical views and attitudes toward using RMT in a population with ADHD have previously been explored; however, to our knowledge, there is no previous research that has used qualitative methods to understand the barriers to and facilitators of using RMT in individuals with ADHD following participation in a remote monitoring period. OBJECTIVE We aimed to evaluate the barriers to and facilitators of using RMT in individuals with ADHD compared with a group of people who did not have a diagnosis of ADHD. We also aimed to explore participants' views on using RMT for 1 or 2 years in future studies. METHODS In total, 20 individuals with ADHD and 20 individuals without ADHD were followed up for 10 weeks using RMT that involved active (questionnaires and cognitive tasks) and passive (smartphone sensors and wearable devices) monitoring; 10 adolescents and adults with ADHD and 12 individuals in a comparison group completed semistructured qualitative interviews at the end of the study period. The interviews focused on potential barriers to and facilitators of using RMT in adults with ADHD. A framework methodology was used to explore the data qualitatively. RESULTS Barriers to and facilitators of using RMT were categorized as health-related, user-related, and technology-related factors across both participant groups. When comparing themes that emerged across the participant groups, both individuals with and without ADHD experienced similar barriers and facilitators in using RMT. The participants agreed that RMT can provide useful objective data. However, slight differences between the participant groups were identified as barriers to RMT across all major themes. Individuals with ADHD described the impact that their ADHD symptoms had on participating (health-related theme), commented on the perceived cost of completing the cognitive tasks (user-related theme), and described more technical challenges (technology-related theme) than individuals without ADHD. Hypothetical views on future studies using RMT in individuals with ADHD for 1 or 2 years were positive. CONCLUSIONS Individuals with ADHD agreed that RMT, which uses repeated measurements with ongoing active and passive monitoring, can provide useful objective data. Although themes overlapped with previous research on barriers to and facilitators of engagement with RMT (eg, depression and epilepsy) and with a comparison group, there are unique considerations for people with ADHD, for example, understanding the impact that ADHD symptoms may have on engaging with RMT. Researchers need to continue working with people with ADHD to develop future RMT studies for longer periods.
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Affiliation(s)
- Hayley Denyer
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Qigang Deng
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Abimbola Adanijo
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrea Bilbow
- Attention Deficit Disorder Information and Support Service (ADDISS), Edgware, Middlesex, United Kingdom
| | - Amos Folarin
- The Department of Biostatistics and 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 and King's 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 NHS Foundation Trust, London, United Kingdom
| | - Madeleine J Groom
- School of Medicine, Mental Health & Clinical Neurosciences, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
- NIHR MindTech Healthcare Technology Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Chris Hollis
- NIHR MindTech Healthcare Technology Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Til Wykes
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Jb Dobson
- The Department of Biostatistics and 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 and King's 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 NHS Foundation Trust, London, United Kingdom
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry Centre, 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
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6
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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7
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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8
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Polhemus A, Simblett S, Dawe Lane E, Elliott B, Jilka S, Negbenose E, Burke P, Weyer J, Novak J, Dockendorf MF, Temesi G, Wykes T. Experiences of health tracking in mobile apps for multiple sclerosis: A qualitative content analysis of user reviews. Mult Scler Relat Disord 2023; 69:104435. [PMID: 36493561 DOI: 10.1016/j.msard.2022.104435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 11/21/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Mobile health applications (apps) are promising condition self-management tools for people living with multiple sclerosis (MS). However, most existing apps do not include health tracking features. This gap has been raised as a priority research topic, but the development of new self-management apps will require designers to understand the context and needs of those living with MS. Our aim was to conduct a content analysis of publicly available user reviews of existing MS self-management apps to understand desired features and guide the design of future apps. METHODS We systematically reviewed MS self-management apps which were publicly available in English on the Google Play and iOS app stores. We then conducted sentiment and content analysis of recent user reviews which referenced health tracking and data visualization to understand self-reported experiences and feedback. RESULTS Searches identified 75 unique apps, of which six met eligibility criteria and had reviews. One hundred and thirty-seven user reviews of these apps were eligible, though most were associated with a single app (n=108). Overall, ratings and sentiment scores skewed highly positive (Median [IQR]: Ratings - 5 [4-5], Sentiment scores - 0.70 [0.44-0.86]), though scores of individual apps varied. Content analysis revealed five themes: reasons for app usage, simple user experience, customization and flexibility, feature requests, and technical issues. Reviewers suggested that app customization, interconnectivity, and consolidated access to desired features should be considered in the design of future apps. User ratings weakly correlated with review sentiment scores (ρ = 0.27 [0.11-0.42]). CONCLUSIONS Self-tracking options in MS apps are currently limited, though the apps that offer these functions are considered useful by individuals with MS. Additional qualitative research is required to understand how specific app features and opportunities for personalization should be incorporated into new self-management tools for this population.
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Affiliation(s)
- Ashley Polhemus
- Merck Research Labs Information Technology, Merck Sharp, & Dohme, Kenilworth, NJ, USA; Epidemiology Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland.
| | - Sara Simblett
- Institute of Psychiatry, King's College London, London, UK
| | - Erin Dawe Lane
- Institute of Psychiatry, King's College London, London, UK
| | | | - Sagar Jilka
- Institute of Psychiatry, King's College London, London, UK
| | | | - Patrick Burke
- RADAR-CNS Patient Advisory Board, King's College London, London, UK
| | - Janice Weyer
- RADAR-CNS Patient Advisory Board, King's College London, London, UK
| | - Jan Novak
- Merck Research Labs Information Technology, Merck Sharp, & Dohme, Kenilworth, NJ, USA
| | - Marissa F Dockendorf
- Merck Research Labs Information Technology, Merck Sharp, & Dohme, Kenilworth, NJ, USA
| | - Gergely Temesi
- Merck Research Labs Information Technology, Merck Sharp, & Dohme, Kenilworth, NJ, USA
| | - Til Wykes
- Institute of Psychiatry, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
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- The RADAR-CNS Consortium (www.radar-cns.org)
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9
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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. Comput Methods Programs Biomed 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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.
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10
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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11
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Dawe-Lane E, Mutepua M, Morris D, Odoi CM, Wilson E, Evans J, Pinfold V, Wykes T, Jilka S, Simblett S. Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study. JMIR Ment Health 2022; 9:e31251. [PMID: 35435852 PMCID: PMC9644246 DOI: 10.2196/31251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Communication via technology is regarded as an effective way of maintaining social connection and helping individuals to cope with the psychological impact of social distancing measures during a pandemic. However, there is little information about which factors have influenced increased use of technology to communicate with others during lockdowns and whether this has changed over time. OBJECTIVE The aim of this study is to explore which psychosocial factors (eg, mental health and employment) and pandemic-related factors (eg, shielding and time) influenced an increase in communication via technology during the first lockdown in the United Kingdom. METHODS A cross-sectional, web-based survey was conducted between April and July 2020, examining thoughts, feelings, and behaviors associated with the pandemic, including communicating more using technology (eg, via messaging, phone, or video). We collected sociodemographic information, employment status, mental health service user status, and depression symptoms. We used hierarchical logistic regression to test which factors were associated with communicating more using technology during the lockdown. RESULTS Participants (N=1464) were on average 41.07 (SD 14.61) years old, and mostly women (n=1141; 77.9%), White (n=1265; 86.4%), and employed (n=1030; 70.4%). Participants reported a mild level of depression (mean 9.43, SD 7.02), and were communicating more using technology (n=1164; 79.5%). The hierarchical regression indicated that people who were employed and experiencing lower levels of depression were more likely to report increased communication using technology during a lockdown period of the COVID-19 pandemic, and over time, men communicated more using technology. Increased use of technology to communicate was related to greater communication and the inability to see others due to the social distancing measures enacted during the lockdown. It was not related to a general increase in technology use during the lockdown. CONCLUSIONS Although most participants reported increased use of technology to communicate during a lockdown period of the COVID-19 pandemic, this was more apparent in the employed and those experiencing low levels of depression. Moving forward, we should continue to monitor groups who may have been excluded from the benefits of support and communication using technology.
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Affiliation(s)
- Erin Dawe-Lane
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Magano Mutepua
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Daniel Morris
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Clarissa M Odoi
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Emma Wilson
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Joanne Evans
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | | | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Sagar Jilka
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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12
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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13
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Polhemus A, Simblett S, Dawe-Lane E, Gilpin G, Elliott B, Jilka S, Novak J, Nica R, Temesi G, Wykes T. Health tracking via mobile apps for depression self-management: a qualitative content analysis of user reviews (Preprint). JMIR Hum Factors 2022; 9:e40133. [DOI: 10.2196/40133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/19/2022] [Accepted: 08/06/2022] [Indexed: 11/13/2022] Open
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14
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Matcham F, Leightley D, Siddi S, Lamers F, White K, Annas P, De Girolamo G, Difrancesco S, Haro J, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr D, Narayan V, Oetzmann C, Penninx B, Simblett S, Bruce S, Nica R, Wykes T, Brasen J, Myin-Germeys I, Rintala A, Conde P, Dobson R, Folarin A, Stewart C, Ranjan Y, Rashid Z, Cummins N, Manyakov N, Vairavan S, Hotopf M. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study. Eur Psychiatry 2022. [PMCID: PMC9564033 DOI: 10.1192/j.eurpsy.2022.315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction
Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.
Objectives
To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.
Methods
RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.
Results
A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.
Conclusions
RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.
Disclosure
No significant relationships.
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15
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Gadelrab R, Simblett S, Hook J, Rickwood S, Martinez J, Johnstone M, Flower C, Bourne S, Young A, Macritchie K. Creating a Digital Psychoeducation Programme for bipolar disorder in the COVID-19 pandemic. Eur Psychiatry 2022. [PMCID: PMC9567093 DOI: 10.1192/j.eurpsy.2022.1458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction The Covid-19 pandemic profoundly affected delivery and accessibility of mental health care services at a time when most needed. The OPTIMA Mood Disorder Service, a specialist bipolar disorder service, adapted group psychoeducation programme for delivery on-line. Objectives We report the feasibility of creating a digital psychoeducation programme. Methods The OPTIMA ten session group psychoeducation programme was converted into a ‘Digital’ intervention using video-conferencing. Sessions offered a range of key topics, derived from the initial Barcelona Group Psychoeducation Programme. At the time of writing, OPTIMA had fully completed two 10 session digital courses. Results A total of 12 people (6 in each group) consented to be part of a service evaluation of the digital groups. Just over half of the participants were women (7/12; 58.3%) and one identified as being non-binary (8.3); remaining participants were men. Age of participants ranged from 25 years to 65 years (Mean=42.3; SD=13.1). Data showed a high level of engagement (77%) All participants reported some improvement with a mean Bipolar Self-Efficacy scale (BPSES) post-group score of 105.6 (SD=14.8). At group level, this change was not statistically significant (F (1, 15) = 0.71, p=0.41). At an individual level, two out of five showed a reliable change index >1.96. Conclusions
Delivering a ‘digital’ group psychoeducation programme was possible due to careful planning and programme development. There was good uptake from service users suggesting it is a feasible approach with preliminary evidence of clinical benefit. Disclosure No significant relationships.
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16
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Simblett S, Pennington M, Quaife M, Theochari E, Burke P, Brichetto G, Devonshire J, Lees S, Little A, Pullen A, Stoneman A, Thorpe S, Weyer J, Polhemus A, Novak J, Dawe-Lane E, Morris D, Mutepua M, Odoi C, Wilson E, Wykes T. Key Drivers and Facilitators of the Choice to Use mHealth Technology in People With Neurological Conditions: Observational Study. JMIR Form Res 2022; 6:e29509. [PMID: 35604761 PMCID: PMC9171601 DOI: 10.2196/29509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/21/2021] [Accepted: 01/04/2022] [Indexed: 11/16/2022] Open
Abstract
Background There is increasing interest in the potential uses of mobile health (mHealth) technologies, such as wearable biosensors, as supplements for the care of people with neurological conditions. However, adherence is low, especially over long periods. If people are to benefit from these resources, we need a better long-term understanding of what influences patient engagement. Previous research suggests that engagement is moderated by several barriers and facilitators, but their relative importance is unknown. Objective To determine preferences and the relative importance of user-generated factors influencing engagement with mHealth technologies for 2 common neurological conditions with a relapsing-remitting course: multiple sclerosis (MS) and epilepsy. Methods In a discrete choice experiment, people with a diagnosis of MS (n=141) or epilepsy (n=175) were asked to select their preferred technology from a series of 8 vignettes with 4 characteristics: privacy, clinical support, established benefit, and device accuracy; each of these characteristics was greater or lower in each vignette. These characteristics had previously been emphasized by people with MS and or epilepsy as influencing engagement with technology. Mixed multinomial logistic regression models were used to establish which characteristics were most likely to affect engagement. Subgroup analyses explored the effects of demographic factors (such as age, gender, and education), acceptance of and familiarity with mobile technology, neurological diagnosis (MS or epilepsy), and symptoms that could influence motivation (such as depression). Results Analysis of the responses to the discrete choice experiment validated previous qualitative findings that a higher level of privacy, greater clinical support, increased perceived benefit, and better device accuracy are important to people with a neurological condition. Accuracy was perceived as the most important factor, followed by privacy. Clinical support was the least valued of the attributes. People were prepared to trade a modest amount of accuracy to achieve an improvement in privacy, but less likely to make this compromise for other factors. The type of neurological condition (epilepsy or MS) did not influence these preferences, nor did the age, gender, or mental health status of the participants. Those who were less accepting of technology were the most concerned about privacy and those with a lower level of education were prepared to trade accuracy for more clinical support. Conclusions For people with neurological conditions such as epilepsy and MS, accuracy (ie, the ability to detect symptoms) is of the greatest interest. However, there are individual differences, and people who are less accepting of technology may need far greater reassurance about data privacy. People with lower levels of education value greater clinician involvement. These patient preferences should be considered when designing mHealth technologies.
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Affiliation(s)
- Sara Simblett
- Psychology Department, King's College London, London, United Kingdom
| | - Mark Pennington
- Psychology Department, King's College London, London, United Kingdom
| | - Matthew Quaife
- Health Economics Department, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | | | - Patrick Burke
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
| | - Giampaolo Brichetto
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
- Italian Multiple Sclerosis Society and Foundation, Rome, Italy
| | - Julie Devonshire
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
| | - Simon Lees
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
| | - Ann Little
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
- International Bureau for Epilepsy, Dublin, Ireland
| | - Angie Pullen
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
- Epilepsy Action, Leeds, United Kingdom
| | - Amanda Stoneman
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
- Epilepsy Action, Leeds, United Kingdom
| | - Sarah Thorpe
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
| | - Janice Weyer
- Remote Assessment of Disease and Relapse in Central Nervous System Disorders Patient Advisory Board, King's College London, London, United Kingdom
| | - Ashley Polhemus
- Merck Sharp & Dohme Information Technology, Prague, Czech Republic
| | - Jan Novak
- Psychology Department, King's College London, London, United Kingdom
- Merck Sharp & Dohme Information Technology, Prague, Czech Republic
- Faculty of Science, Charles University, Prague, Czech Republic
| | - Erin Dawe-Lane
- Psychology Department, King's College London, London, United Kingdom
| | - Daniel Morris
- Psychology Department, King's College London, London, United Kingdom
| | - Magano Mutepua
- Psychology Department, King's College London, London, United Kingdom
| | - Clarissa Odoi
- Psychology Department, King's College London, London, United Kingdom
- South London and Maudsley Biomedical Research Centre, London, United Kingdom
| | - Emma Wilson
- Psychology Department, King's College London, London, United Kingdom
| | - Til Wykes
- Psychology Department, King's College London, London, United Kingdom
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17
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Polhemus A, Novak J, Majid S, Simblett S, Morris D, Bruce S, Burke P, Dockendorf MF, Temesi G, Wykes T. Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives. JMIR Ment Health 2022; 9:e25249. [PMID: 35482368 PMCID: PMC9100378 DOI: 10.2196/25249] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 03/29/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. OBJECTIVE The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. METHODS In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. RESULTS We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. CONCLUSIONS When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.
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Affiliation(s)
- Ashley Polhemus
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Zurich, Switzerland.,Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Jan Novak
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Prague, Czech Republic
| | - Shazmin Majid
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Prague, Czech Republic.,School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Morris
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, London, United Kingdom
| | - Patrick Burke
- RADAR-CNS Patient Advisory Board, London, United Kingdom
| | - Marissa F Dockendorf
- Global Digital Analytics and Technologies, Merck, Sharpe, & Dohme, Kenilworth, NJ, United States
| | - Gergely Temesi
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Vienna, Austria
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
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18
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Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Vilella E, Simblett S, Rintala A, Bruce S, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BW, Narayan VA, Annas P, Hotopf M, Dobson RJ. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study. JMIR Ment Health 2022; 9:e34898. [PMID: 35275087 PMCID: PMC8957008 DOI: 10.2196/34898] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/09/2021] [Accepted: 01/12/2022] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. OBJECTIVE We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. METHODS Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. RESULTS This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility. CONCLUSIONS Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.
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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 and King's 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 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
| | | | - Rebecca Bendayan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and 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
| | - 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 Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, 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
| | - Elisabet Vilella
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.,Hospital Universitari Institut Pere Mata, Institute of Health Research Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Aki Rintala
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.,Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Evanston, IL, United States
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, 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 Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, 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 and King's 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 NHS Foundation Trust, London, United Kingdom
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19
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Wakely H, Radakovic R, Bateman A, Simblett S, Fish J, Gracey F. Psychometric Properties of the Revised Dysexecutive Questionnaire in a Non-clinical Population. Front Hum Neurosci 2022; 16:767367. [PMID: 35308604 PMCID: PMC8924056 DOI: 10.3389/fnhum.2022.767367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Aims The aim of this study was to assess the psychometric properties of the revised self-rated version of the Dysexecutive Questionnaire (DEX-R) within a non-clinical sample. Methods The study was hosted online, with 140 participants completing the DEX-R, GAD-2 and PHQ-2. Sixty participants also completed the FrSBe, with 99 additionally completing the DEX-R again 3 weeks later. Correlations with demographic factors and symptoms of anxiety and depression were conducted. Rasch and factor analysis were also used to explore underlying subconstructs. Results The DEX-R correlated highly with the FrSBe, indicating sound concurrent validity. Internal consistency, split-half reliability and test-retest reliability were excellent. Age and symptoms of depression and anxiety correlated with DEX-R scores, with older age associated with less dysexecutive problems. The Rasch analysis confirmed the multidimensionality of the rating scale, and a three-factor structure was found relating to activation-self-regulatory, cognitive and social-emotional processes. Frequencies of responses on DEX-R items varied, many were not fully endorsed indicating specific relevance of most but not all items to patients. Conclusion Interpretations of DEX-R ratings of dysexecutive problems should consider mood and individual variation. Systematic comparison of DEX-R responses between healthy and clinical groups could help identify a suitable cut off for dysexecutive symptoms.
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Affiliation(s)
- Hannah Wakely
- Faculty of Medicine and Health Sciences, Cambridgeshire and Peterborough NHS Foundation Trust, University of East Anglia, Norwich, United Kingdom
| | - Ratko Radakovic
- Faculty of Medicine and Health Sciences, Cambridgeshire and Peterborough NHS Foundation Trust, University of East Anglia, Norwich, United Kingdom
- The Euan MacDonald Centre for Motor Neurone Disease, University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew Bateman
- School of Health and Social Care, University of Essex, Colchester, United Kingdom
| | - Sara Simblett
- Department of Psychology, King’s College London, Institute of Psychiatry, Psychology, and Neuroscience, London, United Kingdom
| | - Jessica Fish
- Department of Clinical Neuropsychology and Clinical Health Psychology, St George’s Hospitals NHS Foundation Trust, London, United Kingdom
- Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Fergus Gracey
- Faculty of Medicine and Health Sciences, Cambridgeshire and Peterborough NHS Foundation Trust, University of East Anglia, Norwich, United Kingdom
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20
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White KM, Matcham F, Leightley D, Carr E, Conde P, Dawe-Lane E, Ranjan Y, Simblett S, Henderson C, Hotopf M. Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial. JMIR Res Protoc 2021; 10:e32653. [PMID: 34932005 PMCID: PMC8734922 DOI: 10.2196/32653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice. OBJECTIVE The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)-base. METHODS We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement). RESULTS Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022. CONCLUSIONS This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice. TRIAL REGISTRATION ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/32653.
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Affiliation(s)
- Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Erin Dawe-Lane
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Claire Henderson
- Health Service & Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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21
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Leightley D, Lavelle G, White KM, Sun S, Matcham F, Ivan A, Oetzmann C, Penninx BWJH, Lamers F, Siddi S, Haro JM, Myin-Germeys I, Bruce S, Nica R, Wickersham A, Annas P, Mohr DC, Simblett S, Wykes T, Cummins N, Folarin AA, Conde P, Ranjan Y, Dobson RJB, Narayan VA, Hotopf M. Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology. BMC Psychiatry 2021; 21:435. [PMID: 34488697 PMCID: PMC8419819 DOI: 10.1186/s12888-021-03434-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown - pre-, during and post - in adults with a recent history of MDD across multiple centres. METHODS This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. RESULTS In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (- 12.16; 95% CI - 18.39 to - 5.92; p < 0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p < 0.001, respectively) as compared to those who were not. CONCLUSIONS We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid - 19 pandemic.
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Affiliation(s)
- Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Grace Lavelle
- 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
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Alina Ivan
- 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
| | | | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Josep Mario Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
- Romanian League for Mental Health, London, UK
| | - Alice Wickersham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - David C. Mohr
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, USA
| | - Sara Simblett
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Til Wykes
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
| | - Nicholas Cummins
- 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 & Wellbeing, University of Augsburg, Augsburg, Germany
| | - Amos Akinola Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, 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
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
| | | | - Mathew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
| | - On behalf of the RADAR-CNS Consortium
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- RADAR-CNS Patient Advisory Board, King’s College London, London, UK
- Romanian League for Mental Health, London, UK
- H. Lundbeck A/S, Copenhagen, Denmark
- Center for Behavioral Intervention Technologies, Northwestern University, Chicago, USA
- King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, UK
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Augsburg, Germany
- South London and Maudsley NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
- Maudsley Biomedical Research Centre, National Institute for Health Research, South London and Maudsley NHS Foundation Trust, London, UK
- Janssen Research and Development, LLC, Titusville, NJ USA
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22
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Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, 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. Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR Mhealth Uhealth 2021; 9:e29840. [PMID: 34328441 PMCID: PMC8367113 DOI: 10.2196/29840] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/18/2021] [Accepted: 05/31/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. OBJECTIVE This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). METHODS The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. RESULTS A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). CONCLUSIONS Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
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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 and King's 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 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
| | - 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
| | - Faith Matcham
- 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 Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, 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
- Center for Contextual Psychiatry, Department of Neurosciences, 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, Evanston, IL, United States
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 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 Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, Netherlands
| | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 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 and King's 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 NHS Foundation Trust, London, United Kingdom
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23
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Zhang Y, Folarin AA, Sun S, Cummins N, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, White KM, Lamers F, Siddi S, Simblett S, Myin-Germeys I, Rintala A, Wykes T, Haro JM, Penninx BW, Narayan VA, Hotopf M, Dobson RJ. Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study. JMIR Mhealth Uhealth 2021; 9:e24604. [PMID: 33843591 PMCID: PMC8076992 DOI: 10.2196/24604] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/07/2020] [Accepted: 02/03/2021] [Indexed: 01/23/2023] Open
Abstract
Background Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
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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.,South London and Maudsley National Health Services 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
| | - Rebecca Bendayan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,South London and Maudsley National Health Services Foundation Trust, 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
| | - Faith Matcham
- 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
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, 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
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Aki Rintala
- Center for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.,Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Til Wykes
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom.,Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 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 Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | | | - Matthew Hotopf
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom.,Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 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.,South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
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24
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Abstract
BACKGROUND Ketamine is a new and promising treatment for depression but comes with challenges to implement because of its potential for abuse. AIMS We sought the views of patients to inform policy and practical decisions about the clinical use of ketamine before large-scale roll-out is considered. METHOD This qualitative study used three focus groups and three validation sessions from 14 patients with prior diagnoses of depression but no experience of ketamine treatment. Focus groups explored their views about clinical use of ketamine and the best way for ketamine to be administered and monitored. The qualitative data were analysed by three service-user researchers using thematic analysis. RESULTS Five themes were generated: changing public perceptions, risks, monitoring, privacy and data protection, and practical aspects. Participants were conscious of the stigma attached to ketamine as a street drug and wanted better public education, and evidence on the safety of ketamine after long-term use. They felt that monitoring was required to provide evidence for ketamine's safe use and administration, but there were concerns about the misuse of this information. Practical aspects included discussions about treatment duration, administration and accessibility (for example who would receive it, under what criteria and how). CONCLUSIONS Patients are enthusiastic about ketamine treatment but need more information before national roll-out. The wider societal impact of ketamine treatment also needs to be considered and patients need to be part of any future roll-out to ensure its success.
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Affiliation(s)
- Sagar Jilka
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, UK
| | - Clarissa Mary Odoi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, UK
| | - Emma Wilson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Sazan Meran
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Sara Simblett
- Institute of Psychiatry, Psychology & Neuroscience, King's College London; and South London and Maudsley NHS Foundation Trust, UK
| | - Til Wykes
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, UK
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25
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Bruno E, Biondi A, Böttcher S, Vértes G, Dobson R, Folarin A, Ranjan Y, Rashid Z, Manyakov N, Rintala A, Myin-Germeys I, Simblett S, Wykes T, Stoneman A, Little A, Thorpe S, Lees S, Schulze-Bonhage A, Richardson M. Remote Assessment of Disease and Relapse in Epilepsy: Protocol for a Multicenter Prospective Cohort Study. JMIR Res Protoc 2020; 9:e21840. [PMID: 33325373 PMCID: PMC7773514 DOI: 10.2196/21840] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/06/2020] [Accepted: 10/20/2020] [Indexed: 01/20/2023] Open
Abstract
Background In recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT. Objective The primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities. Methods People with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server. Results The RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022. Conclusions RADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders. International Registered Report Identifier (IRRID) PRR1-10.2196/21840
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Affiliation(s)
- Elisa Bruno
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Andrea Biondi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Gergely Vértes
- Epilepsy Seizure Detection - Neurology UCB Pharma, Brussels, Belgium
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Amos Folarin
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Nikolay Manyakov
- Feasibility Advanced Analytics, Clinical Insights and Experience, Janssen Research and Development, Beerse, Belgium
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.,Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - Sara Simblett
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Til Wykes
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Amanda Stoneman
- The RADAR-CNS patient advisory board, King's College London, UK, London, United Kingdom
| | - Ann Little
- The RADAR-CNS patient advisory board, King's College London, UK, London, United Kingdom
| | - Sarah Thorpe
- The RADAR-CNS patient advisory board, King's College London, UK, London, United Kingdom
| | - Simon Lees
- The RADAR-CNS patient advisory board, King's College London, UK, London, United Kingdom
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Mark Richardson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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26
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Sun S, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, Cummins N, Matcham F, Dalla Costa G, Simblett S, Leocani L, Lamers F, Sørensen PS, Buron M, Zabalza A, Guerrero Pérez AI, Penninx BW, Siddi S, Haro JM, Myin-Germeys I, Rintala A, Wykes T, Narayan VA, Comi G, Hotopf M, Dobson RJ. Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19. J Med Internet Res 2020; 22:e19992. [PMID: 32877352 PMCID: PMC7527031 DOI: 10.2196/19992] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/20/2020] [Accepted: 07/26/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. OBJECTIVE We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. METHODS We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. RESULTS We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more. CONCLUSIONS RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.
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Affiliation(s)
- Shaoxiong Sun
- The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- The Department of Biostatistics and 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
| | - Yatharth Ranjan
- The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Augsburg, Germany
| | - Faith Matcham
- The Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Gloria Dalla Costa
- Neurorehabilitation Unit and Institute of Experimental Neurology, University Vita Salute San Raffaele, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Sara Simblett
- The Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Letizia Leocani
- Neurorehabilitation Unit and Institute of Experimental Neurology, University Vita Salute San Raffaele, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | - Per Soelberg Sørensen
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mathias Buron
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Ana Isabel Guerrero Pérez
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Universitat de Barcelona, Barcelona, Spain
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Universitat de Barcelona, Barcelona, Spain
| | - Inez Myin-Germeys
- Centre for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Aki Rintala
- Centre for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- The Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
| | | | - Giancarlo Comi
- Institute of Experimental Neurology, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy
| | - Matthew Hotopf
- The Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
| | - Richard Jb Dobson
- The Department of Biostatistics and 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
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Simblett S, Matcham F, Curtis H, Greer B, Polhemus A, Novák J, Ferrao J, Gamble P, Hotopf M, Narayan V, Wykes T. Patients' Measurement Priorities for Remote Measurement Technologies to Aid Chronic Health Conditions: Qualitative Analysis. JMIR Mhealth Uhealth 2020; 8:e15086. [PMID: 32519975 PMCID: PMC7315360 DOI: 10.2196/15086] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 10/31/2019] [Accepted: 12/15/2019] [Indexed: 01/19/2023] Open
Abstract
Background Remote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide the opportunity for real-world assessment and intervention that will streamline clinical input for years to come. In order to establish the benefits of this approach, we need to operationalize what is expected in terms of a successful measurement. We focused on three clinical long-term conditions where a novel case has been made for the benefits of RMT: major depressive disorder (MDD), multiple sclerosis (MS), and epilepsy. Objective The aim of this study was to conduct a consultation exercise on the clinical end point or outcome measurement priorities for RMT studies, drawing on the experiences of people with chronic health conditions. Methods A total of 24 participants (16/24 women, 67%), ranging from 28 to 65 years of age, with a diagnosis of one of three chronic health conditions―MDD, MS, or epilepsy―took part in six focus groups. A systematic thematic analysis was used to extract themes and subthemes of clinical end point or measurement priorities. Results The views of people with MDD, epilepsy, and MS differed. Each group highlighted unique measurements of importance, relevant to their specific needs. Although there was agreement that remote measurement could be useful for tracking symptoms of illness, some symptoms were specific to the individual groups. Measuring signs of wellness was discussed more by people with MDD than by people with MS and epilepsy. However, overlap did emerge when considering contextual factors, such as life events and availability of support (MDD and epilepsy) as well as ways of coping (epilepsy and MS). Conclusions This is a unique study that puts patients’ views at the forefront of the design of a clinical study employing novel digital resources. In all cases, measuring symptom severity is key; people want to know when their health is getting worse. Second, symptom severity needs to be placed into context. A holistic approach that, in some cases, considers signs of wellness as well as illness, should be the aim of studies employing RMT to understand the health of people with chronic conditions.
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Affiliation(s)
- Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Hannah Curtis
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ben Greer
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ashley Polhemus
- Merck Research Labs IT, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Jan Novák
- Merck Research Labs IT, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Jose Ferrao
- Merck Research Labs IT, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Peter Gamble
- Merck Research Labs IT, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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- RADAR-CNS, London, United Kingdom
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28
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Polhemus AM, Novák J, Ferrao J, Simblett S, Radaelli M, Locatelli P, Matcham F, Kerz M, Weyer J, Burke P, Huang V, Dockendorf MF, Temesi G, Wykes T, Comi G, Myin-Germeys I, Folarin A, Dobson R, Manyakov NV, Narayan VA, Hotopf M. Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study. JMIR Mhealth Uhealth 2020; 8:e16043. [PMID: 32379055 PMCID: PMC7243134 DOI: 10.2196/16043] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 01/02/2020] [Accepted: 01/24/2020] [Indexed: 12/18/2022] Open
Abstract
Background Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. Objective To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. Methods The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. Results The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. Conclusions The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.
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Affiliation(s)
- Ashley Marie Polhemus
- Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic.,Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Jan Novák
- Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic.,Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Prague, Czech Republic
| | - Jose Ferrao
- Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Marta Radaelli
- Neurology Services, San Raffaele Hospital Multiple Sclerosis Centre, Milan, Italy
| | - Patrick Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Maximilian Kerz
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Janice Weyer
- Patient Advisory Board, Remote Assessment of Disease and Relapse Research Program, King's College London, London, United Kingdom
| | - Patrick Burke
- Patient Advisory Board, Remote Assessment of Disease and Relapse Research Program, King's College London, London, United Kingdom
| | - Vincy Huang
- Merck Research Labs Information Technology, Merck Sharpe & Dohme, Singapore, Singapore
| | - Marissa Fallon Dockendorf
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co, Inc, Kenilworth, NJ, United States
| | - Gergely Temesi
- Merck Research Labs Information Technology, Merck Sharpe & Dohme, Prague, Czech Republic
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Giancarlo Comi
- Neurology Services, San Raffaele Hospital Multiple Sclerosis Centre, Milan, Italy
| | - Inez Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Amos Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Vaibhav A Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, United States
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
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29
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Rintala A, Matcham F, Radaelli M, Locafaro G, Simblett S, Barattieri di San Pietro C, Bulgari V, Burke P, Devonshire J, Weyer J, Wykes T, Comi G, Hotopf M, Myin-Germeys I. Emotional outcomes in clinically isolated syndrome and early phase multiple sclerosis: a systematic review and meta-analysis. J Psychosom Res 2019; 124:109761. [PMID: 31443806 DOI: 10.1016/j.jpsychores.2019.109761] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To study depression, anxiety, suicide risk, and emotional health-related quality of life (HRQoL) in people with clinically isolated syndrome (CIS) and in early phase multiple sclerosis (MS). METHODS A systematic literature review was conducted with inclusion criteria of observational studies on outcomes of depression, anxiety, suicide risk, and emotional HRQoL in CIS and within five years since diagnosis of MS. Studies were screened using the Preferred Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, and study quality was determined for included studies. Meta-analysis and meta-regression were performed if applicable. RESULTS Fifty-one studies were included in the systematic review. In early phase MS, meta-analyses of the Hospital Anxiety Depression Scale (HADS) indicated prevalence levels of 17% (95% confidence interval (CI): 9 to 25%; p < .001) for depressive and 35% (95% CI: 28 to 41%; p < .001) for anxiety symptoms. Meta-regression analyses revealed an increase in mean HADS-D and HADS-A associated with larger sample size, and higher HADS-D mean with increased study quality. Similar depressive and anxiety symptoms were observed in CIS, and increased suicide risk and low emotional HRQoL was associated with depressive symptoms in early phase MS. The methodological quality of the studies was considered fair. CONCLUSIONS Findings suggest that mild-to-moderate symptoms of depression and anxiety might be prevalent in CIS and in early phase MS. Future research on both clinical populations are needed, especially longitudinal monitoring of emotional outcomes.
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Affiliation(s)
- A Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.
| | - F Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - M Radaelli
- Department of Neurology, San Raffaele Hospital, Milan, Italy.
| | - G Locafaro
- Department of Neurology, San Raffaele Hospital, Milan, Italy
| | - S Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - C Barattieri di San Pietro
- Psychiatric Epidemiology and Evaluation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Dipartimento di Psicologia, Università di Milano-Bicocca, Milan, Italy.
| | - V Bulgari
- Psychiatric Epidemiology and Evaluation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - P Burke
- The Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) Patient Advisory Board.
| | - J Devonshire
- The Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) Patient Advisory Board.
| | - J Weyer
- The Remote Assessment of Disease and Relapse in Central Nervous System Disorders (RADAR-CNS) Patient Advisory Board
| | - T Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - G Comi
- Department of Neurology, San Raffaele Hospital, Milan, Italy.
| | - M Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - I Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.
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Simblett S, Matcham F, Siddi S, Bulgari V, Barattieri di San Pietro C, Hortas López J, Ferrão J, Polhemus A, Haro JM, de Girolamo G, Gamble P, Eriksson H, Hotopf M, Wykes T. Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis. JMIR Mhealth Uhealth 2019; 7:e11325. [PMID: 30698535 PMCID: PMC6372936 DOI: 10.2196/11325] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 10/01/2018] [Accepted: 10/03/2018] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts. OBJECTIVE This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. METHODS Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. RESULTS Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience, and ease of use was noted across all countries. CONCLUSIONS Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools.
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Affiliation(s)
- Sara Simblett
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley National Health Service Foundation Trust, King's College London, London, United Kingdom
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Centro de Investigacion Biomedica en Red CIBERSAM, Madrid, Spain.,Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
| | - Viola Bulgari
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Chiara Barattieri di San Pietro
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Jorge Hortas López
- Research Department, QITERIA Investigación Social Aplicada, Madrid, Spain
| | - José Ferrão
- Information Technology Department, MSD Czech Republic, Prague, Czech Republic
| | - Ashley Polhemus
- Information Technology Department, MSD Czech Republic, Prague, Czech Republic
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Centro de Investigacion Biomedica en Red CIBERSAM, Madrid, Spain.,Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain
| | | | - Peter Gamble
- Information Technology Department, MSD Czech Republic, Prague, Czech Republic
| | - Hans Eriksson
- Clinical Development, Depression and Paediatrics, H Lundbeck A/S, Copenhagen, Denmark
| | - Matthew Hotopf
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley National Health Service Foundation Trust, King's College London, London, United Kingdom
| | - Til Wykes
- Institute of Psychology, Psychiatry and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley National Health Service Foundation Trust, King's College London, London, United Kingdom
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31
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Greer B, Robotham D, Simblett S, Curtis H, Griffiths H, Wykes T. Digital Exclusion Among Mental Health Service Users: Qualitative Investigation. J Med Internet Res 2019; 21:e11696. [PMID: 30626564 PMCID: PMC6329420 DOI: 10.2196/11696] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 09/17/2018] [Accepted: 09/18/2018] [Indexed: 11/17/2022] Open
Abstract
Background Access to internet-enabled technology and Web-based services has grown exponentially in recent decades. This growth potentially excludes some communities and individuals with mental health difficulties, who face a heightened risk of digital exclusion. However, it is unclear what factors may contribute to digital exclusion in this population. Objective To explore in detail the problems of digital exclusion in mental health service users and potential facilitators to overcome them. Methods We conducted semistructured interviews with 20 mental health service users who were deemed digitally excluded. We recruited the participants from a large secondary mental health provider in South London, United Kingdom. We employed thematic analysis to identify themes and subthemes relating to historical and extant reasons for digital exclusion and methods of overcoming it. Results There were three major themes that appeared to maintain digital exclusion: a perceived lack of knowledge, being unable to access the necessary technology and services owing to personal circumstances, and the barriers presented by mental health difficulties. Specific facilitators for overcoming digital exclusion included intrinsic motivation and a personalized learning format that reflects the individual’s unique needs and preferences. Conclusions Multiple factors contribute to digital exclusion among mental health service users, including material deprivation and mental health difficulties. This means that efforts to overcome digital exclusion must address the multiple deprivations individuals may face in the offline world in addition to their individual mental health needs. Additional facilitators include fostering an intrinsic motivation to overcome digital exclusion and providing a personalized learning format tailored to the individual’s knowledge gaps and preferred learning style.
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Affiliation(s)
- Ben Greer
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Dan Robotham
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,The McPin Foundation, London, United Kingdom
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Hannah Curtis
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Helena Griffiths
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, Wykes T, Richardson MP. Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals. Epilepsy Behav 2018; 85:141-149. [PMID: 29940377 DOI: 10.1016/j.yebeh.2018.05.044] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 05/25/2018] [Accepted: 05/28/2018] [Indexed: 11/19/2022]
Abstract
PURPOSE In recent years, digital technology and wearable devices applied to seizure detection have progressively become available. In this study, we investigated the perspectives of people with epilepsy (PWE), caregivers (CG), and healthcare professionals (HP). We were interested in their current use of digital technology as well as their willingness to use wearables to monitor seizures. We also explored the role of factors influencing engagement with technology, including demographic and clinical characteristics, data confidentiality, need for technical support, and concerns about strain or increased workload. METHODS An online survey drawing on previous data collected via focus groups was constructed and distributed via a web link. Using logistic regression analyses, demographic, clinical, and other factors identified to influence engagement with technology were correlated with reported use and willingness to use digital technology and wearables for seizure tracking. RESULTS Eighty-seven surveys were completed, fifty-two (59.7%) by PWE, 13 (14.4%) by CG, and 22 (25.3%) by HP. Responders were familiar with multiple digital technologies, including the Internet, smartphones, and personal computers, and the use of digital services was similar to the UK average. Moreover, age and disease-related factors did not influence access to digital technology. The majority of PWE were willing to use a wearable device for long-term seizure tracking. However, only a limited number of PWE reported current regular use of wearables, and nonusers attributed their choice to uncertainty about the usefulness of this technology in epilepsy care. People with epilepsy envisaged the possibility of understanding their condition better through wearables and considered, with caution, the option to send automatic emergency calls. Despite concerns around accuracy, data confidentiality, and technical support, these factors did not limit PWE's willingness to use digital technology. Caregivers appeared willing to provide support to PWE using wearables and perceived a reduction of their workload and anxiety. Healthcare professionals identified areas of application for digital technologies in their clinical practice, pending an appropriate reorganization of the clinical team to share the burden of data reviewing and handling. CONCLUSIONS Unlike people who have other chronic health conditions, PWE appeared not to be at risk of digital exclusion. This study highlighted a great interest in the use of wearable technology across epilepsy service users, carers, and healthcare professionals, which was independent of demographic and clinical factors and outpaced data security and technology usability concerns.
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Affiliation(s)
- Elisa Bruno
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, 5 Cutcombe Road, London SE5 9RX, UK
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychology, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Alexandra Lang
- NIHR Mental Health MedTech Co-operative, Division of Psychiatry and Applied Psychology, Faculty of Medicine, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham NG7 2TU, UK
| | - Andrea Biondi
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, 5 Cutcombe Road, London SE5 9RX, UK
| | - Clarissa Odoi
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychology, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department Presurgical Diagnostics, Faculty of Medicine, Medical Center, University of Freiburg, Breisacher Strasse 86b, 79110 Freiburg, Germany
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychology, King's College London, De Crespigny Park, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Mark P Richardson
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College London, 5 Cutcombe Road, London SE5 9RX, UK; Centre for Epilepsy, King's College Hospital, Denmark Hill, London SE5 9RS, UK.
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33
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Simblett S, Greer B, Matcham F, Curtis H, Polhemus A, Ferrão J, Gamble P, Wykes T. Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings. J Med Internet Res 2018; 20:e10480. [PMID: 30001997 PMCID: PMC6062692 DOI: 10.2196/10480] [Citation(s) in RCA: 151] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 05/09/2018] [Accepted: 05/10/2018] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Remote measurement technology refers to the use of mobile health technology to track and measure change in health status in real time as part of a person's everyday life. With accurate measurement, remote measurement technology offers the opportunity to augment health care by providing personalized, precise, and preemptive interventions that support insight into patterns of health-related behavior and self-management. However, for successful implementation, users need to be engaged in its use. OBJECTIVE Our objective was to systematically review the literature to update and extend the understanding of the key barriers to and facilitators of engagement with and use of remote measurement technology, to guide the development of future remote measurement technology resources. METHODS We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines involving original studies dating back to the last systematic review published in 2014. We included studies if they met the following entry criteria: population (people using remote measurement technology approaches to aid management of health), intervention (remote measurement technology system), comparison group (no comparison group specified), outcomes (qualitative or quantitative evaluation of the barriers to and facilitators of engagement with this system), and study design (randomized controlled trials, feasibility studies, and observational studies). We searched 5 databases (MEDLINE, IEEE Xplore, EMBASE, Web of Science, and the Cochrane Library) for articles published from January 2014 to May 2017. Articles were independently screened by 2 researchers. We extracted study characteristics and conducted a content analysis to define emerging themes to synthesize findings. Formal quality assessments were performed to address risk of bias. RESULTS A total of 33 studies met inclusion criteria, employing quantitative, qualitative, or mixed-methods designs. Studies were conducted in 10 countries, included male and female participants, with ages ranging from 8 to 95 years, and included both active and passive remote monitoring systems for a diverse range of physical and mental health conditions. However, they were relatively short and had small sample sizes, and reporting of usage statistics was inconsistent. Acceptability of remote measurement technology according to the average percentage of time used (64%-86.5%) and dropout rates (0%-44%) was variable. The barriers and facilitators from the content analysis related to health status, perceived utility and value, motivation, convenience and accessibility, and usability. CONCLUSIONS The results of this review highlight gaps in the design of studies trialing remote measurement technology, including the use of quantitative assessment of usage and acceptability. Several processes that could facilitate engagement with this technology have been identified and may drive the development of more person-focused remote measurement technology. However, these factors need further testing through carefully designed experimental studies. TRIAL REGISTRATION International Prospective Register of Systematic Reviews (PROSPERO) CRD42017060644; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=60644 (Archived by WebCite at http://www.webcitation.org/70K4mThTr).
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Affiliation(s)
- Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, Psychology, King's College London, London, United Kingdom
| | - Ben Greer
- Institute of Psychiatry, Psychology and Neuroscience, Psychology, King's College London, London, United Kingdom
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, Psychology, King's College London, London, United Kingdom
| | - Hannah Curtis
- Institute of Psychiatry, Psychology and Neuroscience, Psychology, King's College London, London, United Kingdom
| | | | - José Ferrão
- MSD IT Global Innovation Center, Prague, Czech Republic
| | - Peter Gamble
- MSD IT Global Innovation Center, Prague, Czech Republic
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, Psychology, King's College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health, South London and Maudsley NHS Foundation Trust, London, United Kingdom
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Simblett S, Birch J, Matcham F, Yaguez L, Morris R. A Systematic Review and Meta-Analysis of e-Mental Health Interventions to Treat Symptoms of Posttraumatic Stress. JMIR Ment Health 2017; 4:e14. [PMID: 28526672 PMCID: PMC5451639 DOI: 10.2196/mental.5558] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Revised: 01/30/2017] [Accepted: 03/05/2017] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a stress disorder characterized by unwanted intrusive re-experiencing of an acutely distressing, often life-threatening, event, combined with symptoms of hyperarousal, avoidance, as well as negative thoughts and feelings. Evidence-based psychological interventions have been developed to treat these symptoms and reduce distress, the majority of which were designed to be delivered face-to-face with trained therapists. However, new developments in the use of technology to supplement and extend health care have led to the creation of e-Mental Health interventions. OBJECTIVE Our aim was to assess the scope and efficacy of e-Mental Health interventions to treat symptoms of PTSD. METHODS The following databases were systematically searched to identify randomized controlled trials of e-Mental Health interventions to treat symptoms of PTSD as measured by standardized and validated scales: the Cochrane Library, MEDLINE, EMBASE, and PsycINFO (in March 2015 and repeated in November 2016). RESULTS A total of 39 studies were found during the systematic review, and 33 (N=3832) were eligible for meta-analysis. The results of the primary meta-analysis revealed a significant improvement in PTSD symptoms, in favor of the active intervention group (standardized mean difference=-0.35, 95% confidence interval -0.45 to -0.25, P<.001, I2=81%). Several sensitivity and subgroup analyses were performed suggesting that improvements in PTSD symptoms remained in favor of the active intervention group independent of the comparison condition, the type of cognitive behavioral therapy-based intervention, and the level of guidance provided. CONCLUSIONS This review demonstrates an emerging evidence base supporting e-Mental Health to treat symptoms of PTSD.
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Affiliation(s)
- Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychology, King's College London, London, United Kingdom
| | - Jennifer Birch
- Springfield University Hospital, South West London and St Georges Mental Health NHS Trust, London, United Kingdom
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Lidia Yaguez
- King's College Hospital, Clinical Neuropsychology Department, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Robin Morris
- King's College Hospital, Clinical Neuropsychology Department, King's College Hospital NHS Foundation Trust, London, United Kingdom
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Affiliation(s)
- Sara Simblett
- a Department of Psychology , Institute of Psychiatry, Psychology and Neuroscience , London , UK
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Gargalas S, Weeks R, Khan-Bourne N, Shotbolt P, Simblett S, Ashraf L, Doyle C, Bancroft V, David AS. Incidence and outcome of functional stroke mimics admitted to a hyperacute stroke unit. J Neurol Neurosurg Psychiatry 2017; 88:2-6. [PMID: 26319438 DOI: 10.1136/jnnp-2015-311114] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 07/28/2015] [Accepted: 08/10/2015] [Indexed: 11/04/2022]
Abstract
BACKGROUND Some patients admitted to acute stroke units are diagnosed as stroke mimics. A minority have a functional neurological disorder ('functional mimics'). AIMS To determine the incidence of functional stroke mimics admitted to a hyperacute stroke unit (HASU); to compare their clinical characteristics with medical mimics and stroke cases and obtain information about outcomes. METHODS Patients admitted to the King's College Hospital HASU between 2011 and 2012 were analysed. Data were obtained from the Stroke Improvement National Audit Programme (SINAP) database. Expert consensus diagnosis was used to classify functional mimics. Follow-up information was obtained from a retrospective case series in primary care over the year following discharge. RESULTS 1165 patients were admitted to the HASU; 904 patients with stroke (77.6%), 163 medical mimics (14%) and 98 functional mimics (8.4%). Functional mimics were significantly more likely to be female (63.3%) versus 49.7% medical mimics and 45.5% stroke, and younger (mean age (SD)) 49.1 (18.8) than medical mimic (63.5 years (16.7)) and stroke cases (71 years (15.5)). Weakness and slurred speech were the commonest presentations of functional mimics and diagnostic MRI was used more often. Clinician recorded visual and speech symptoms and neglect were significantly more frequent in patients with stroke than either mimic group. Of the 68 functional mimics on whom follow-up information was obtained, 40 (59%) were referred to another service most often for a psychologically-based intervention. CONCLUSIONS Functional stroke mimics are an important subgroup admitted to acute stroke services and have a distinct demographic and clinical profile. Their outcomes are poorly monitored. Services should be developed to better diagnose and manage these patients.
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Affiliation(s)
- Sergios Gargalas
- Department of Stroke Medicine, King's College Hospital NHS Foundation Trust, London, UK
| | - Robert Weeks
- Department of Stroke Medicine, King's College Hospital NHS Foundation Trust, London, UK
| | - Najma Khan-Bourne
- Department of Stroke Medicine, King's College Hospital NHS Foundation Trust, London, UK
| | - Paul Shotbolt
- Department of Neuropsychiatry, South London & Maudsley NHS Foundation Trust, London, UK
| | - Sara Simblett
- Department of Neuropsychiatry, South London & Maudsley NHS Foundation Trust, London, UK
| | - Leah Ashraf
- Department of Medicine, King's College London, London, UK
| | - Claire Doyle
- Department of Stroke Medicine, King's College Hospital NHS Foundation Trust, London, UK
| | - Victoria Bancroft
- Department of Stroke Medicine, King's College Hospital NHS Foundation Trust, London, UK
| | - Anthony S David
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
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Mohr C, Simblett S. Creativity seems more consistently related to your social position (profession) than to your schizotypal thoughts. Personality and Individual Differences 2014. [DOI: 10.1016/j.paid.2013.07.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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