1
|
Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
Collapse
Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
| |
Collapse
|
2
|
Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P, Matcham F, White KM, Oetzmann C, Lamers F, Siddi S, Simblett S, Vairavan S, Myin-Germeys I, Mohr DC, Wykes T, Haro JM, Annas P, Penninx BW, Narayan VA, Hotopf M, Dobson RJ. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis. J Med Internet Res 2024; 26:e55302. [PMID: 38941600 PMCID: PMC11245656 DOI: 10.2196/55302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/22/2024] [Accepted: 03/29/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | | | - Brenda Wjh Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Vaibhav A Narayan
- Janssen Research and Development LLC, Titusville, NJ, United States
- Davos Alzheimer's Collaborative, Geneva, Switzerland
| | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard Jb Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
| |
Collapse
|
3
|
Leaning IE, Ikani N, Savage HS, Leow A, Beckmann C, Ruhé HG, Marquand AF. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neurosci Biobehav Rev 2024; 158:105541. [PMID: 38215802 DOI: 10.1016/j.neubiorev.2024.105541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/23/2023] [Accepted: 01/06/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. METHODS We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. RESULTS Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. DISCUSSION Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.
Collapse
Affiliation(s)
- Imogen E Leaning
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands.
| | - Nessa Ikani
- Department of Developmental Psychology, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, the Netherlands.
| | - Hannah S Savage
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Alex Leow
- Department of Psychiatry, Department of Biomedical Engineering and Department of Computer Science, University of Illinois Chicago, Chicago, United States
| | - Christian Beckmann
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Henricus G Ruhé
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department of Psychiatry, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, Nijmegen, the Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| |
Collapse
|
4
|
Rykov YG, Patterson MD, Gangwar BA, Jabar SB, Leonardo J, Ng KP, Kandiah N. Predicting cognitive scores from wearable-based digital physiological features using machine learning: data from a clinical trial in mild cognitive impairment. BMC Med 2024; 22:36. [PMID: 38273340 PMCID: PMC10809621 DOI: 10.1186/s12916-024-03252-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Continuous assessment and remote monitoring of cognitive function in individuals with mild cognitive impairment (MCI) enables tracking therapeutic effects and modifying treatment to achieve better clinical outcomes. While standardized neuropsychological tests are inconvenient for this purpose, wearable sensor technology collecting physiological and behavioral data looks promising to provide proxy measures of cognitive function. The objective of this study was to evaluate the predictive ability of digital physiological features, based on sensor data from wrist-worn wearables, in determining neuropsychological test scores in individuals with MCI. METHODS We used the dataset collected from a 10-week single-arm clinical trial in older adults (50-70 years old) diagnosed with amnestic MCI (N = 30) who received a digitally delivered multidomain therapeutic intervention. Cognitive performance was assessed before and after the intervention using the Neuropsychological Test Battery (NTB) from which composite scores were calculated (executive function, processing speed, immediate memory, delayed memory and global cognition). The Empatica E4, a wrist-wearable medical-grade device, was used to collect physiological data including blood volume pulse, electrodermal activity, and skin temperature. We processed sensors' data and extracted a range of physiological features. We used interpolated NTB scores for 10-day intervals to test predictability of scores over short periods and to leverage the maximum of wearable data available. In addition, we used individually centered data which represents deviations from personal baselines. Supervised machine learning was used to train models predicting NTB scores from digital physiological features and demographics. Performance was evaluated using "leave-one-subject-out" and "leave-one-interval-out" cross-validation. RESULTS The final sample included 96 aggregated data intervals from 17 individuals. In total, 106 digital physiological features were extracted. We found that physiological features, especially measures of heart rate variability, correlated most strongly to the executive function compared to other cognitive composites. The model predicted the actual executive function scores with correlation r = 0.69 and intra-individual changes in executive function scores with r = 0.61. CONCLUSIONS Our findings demonstrated that wearable-based physiological measures, primarily HRV, have potential to be used for the continuous assessments of cognitive function in individuals with MCI.
Collapse
Affiliation(s)
| | | | | | | | - Jacklyn Leonardo
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| |
Collapse
|
5
|
Sun S, Folarin AA, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Vairavan S, Narayan VA, Annas P, Hotopf M, Dobson RJB. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. J Med Internet Res 2023; 25:e45233. [PMID: 37578823 PMCID: PMC10463088 DOI: 10.2196/45233] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/11/2023] [Accepted: 04/23/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.
Collapse
Affiliation(s)
- Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| | - Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Daniel Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
- The Romanian League for Mental Health, Bucharest, Romania
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Physical Activity and Functional Capacity Research Group, Faculty of Health Care and Social Services, LAB University of Applied Sciences, Lahti, Finland
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley, NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
6
|
Shin J, Bae SM. A Systematic Review of Location Data for Depression Prediction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5984. [PMID: 37297588 PMCID: PMC10252667 DOI: 10.3390/ijerph20115984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
Depression contributes to a wide range of maladjustment problems. With the development of technology, objective measurement for behavior and functional indicators of depression has become possible through the passive sensing technology of digital devices. Focusing on location data, we systematically reviewed the relationship between depression and location data. We searched Scopus, PubMed, and Web of Science databases by combining terms related to passive sensing and location data with depression. Thirty-one studies were included in this review. Location data demonstrated promising predictive power for depression. Studies examining the relationship between individual location data variables and depression, homestay, entropy, and the normalized entropy variable of entropy dimension showed the most consistent and significant correlations. Furthermore, variables of distance, irregularity, and location showed significant associations in some studies. However, semantic location showed inconsistent results. This suggests that the process of geographical movement is more related to mood changes than to semantic location. Future research must converge across studies on location-data measurement methods.
Collapse
Affiliation(s)
- Jaeeun Shin
- Department of psychology, Chung-Ang University, Seoul 06974, Republic of Korea;
| | - Sung Man Bae
- Department of Psychology and Psychotherapy, Dankook University, Cheonan 31116, Republic of Korea
| |
Collapse
|
7
|
Zhang Y, Pratap A, Folarin AA, Sun S, Cummins N, Matcham F, Vairavan S, Dineley J, Ranjan Y, Rashid Z, Conde P, Stewart C, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Rambla CH, Simblett S, Nica R, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Annas P, Narayan VA, Hotopf M, Dobson RJB. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med 2023; 6:25. [PMID: 36806317 PMCID: PMC9938183 DOI: 10.1038/s41746-023-00749-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 01/10/2023] [Indexed: 02/19/2023] Open
Abstract
Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Abhishek Pratap
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Krembil Center for Neuroinformatics, CAMH, Toronto, ON, Canada.
- University of Toronto, Toronto, ON, Canada.
- University of Washington, Seattle, WA, USA.
- Davos Alzheimer's Collaborative, Geneva, Switzerland.
| | - Amos A Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- University College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- Health Data Research UK London, University College London, London, UK
| | - Shaoxiong Sun
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology, University of Sussex, Falmer, East Sussex, UK
| | | | - Judith Dineley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katie M White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alina Ivan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Femke Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Carla Hernández Rambla
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Raluca Nica
- RADAR-CNS Patient Advisory Board, King's College London, London, UK
- The Romanian League for Mental Health, Bucharest, Romania
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Vaibhav A Narayan
- Davos Alzheimer's Collaborative, Geneva, Switzerland
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- University College London, London, UK.
- South London and Maudsley NHS Foundation Trust, London, UK.
- Health Data Research UK London, University College London, London, UK.
| |
Collapse
|
8
|
Kushniruk A, Dawe-Lane E, Siddi S, Lamers F, Simblett S, Riquelme Alacid G, Ivan A, Myin-Germeys I, Haro JM, Oetzmann C, Popat P, Rintala A, Rubio-Abadal E, Wykes T, Henderson C, Hotopf M, Matcham F. Understanding the Subjective Experience of Long-term Remote Measurement Technology Use for Symptom Tracking in People With Depression: Multisite Longitudinal Qualitative Analysis. JMIR Hum Factors 2023; 10:e39479. [PMID: 36701179 PMCID: PMC9945920 DOI: 10.2196/39479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 11/07/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Remote measurement technologies (RMTs) have the potential to revolutionize major depressive disorder (MDD) disease management by offering the ability to assess, monitor, and predict symptom changes. However, the promise of RMT data depends heavily on sustained user engagement over extended periods. In this paper, we report a longitudinal qualitative study of the subjective experience of people with MDD engaging with RMTs to provide insight into system usability and user experience and to provide the basis for future promotion of RMT use in research and clinical practice. OBJECTIVE We aimed to understand the subjective experience of long-term engagement with RMTs using qualitative data collected in a longitudinal study of RMTs for monitoring MDD. The objectives were to explore the key themes associated with long-term RMT use and to identify recommendations for future system engagement. METHODS In this multisite, longitudinal qualitative research study, 124 semistructured interviews were conducted with 99 participants across the United Kingdom, Spain, and the Netherlands at 3-month, 12-month, and 24-month time points during a study exploring RMT use (the Remote Assessment of Disease and Relapse-Major Depressive Disorder study). Data were analyzed using thematic analysis, and interviews were audio recorded, transcribed, and coded in the native language, with the resulting quotes translated into English. RESULTS There were 5 main themes regarding the subjective experience of long-term RMT use: research-related factors, the utility of RMTs for self-management, technology-related factors, clinical factors, and system amendments and additions. CONCLUSIONS The subjective experience of long-term RMT use can be considered from 2 main perspectives: experiential factors (how participants construct their experience of engaging with RMTs) and system-related factors (direct engagement with the technologies). A set of recommendations based on these strands are proposed for both future research and the real-world implementation of RMTs into clinical practice. Future exploration of experiential engagement with RMTs will be key to the successful use of RMTs in clinical care.
Collapse
Affiliation(s)
| | - Erin Dawe-Lane
- Department of Psychology, King's College London, London, United Kingdom
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Femke Lamers
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, Netherlands
| | - Sara Simblett
- Department of Psychology, King's College London, London, United Kingdom
| | - Gemma Riquelme Alacid
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Alina Ivan
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Inez Myin-Germeys
- Center for Contextual Psychiatry, Department of Neurosciences, UK Leuven, Leuven, Belgium
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Carolin Oetzmann
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Priya Popat
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Aki Rintala
- Center for Contextual Psychiatry, Department of Neurosciences, UK Leuven, Leuven, Belgium
| | - Elena Rubio-Abadal
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Til Wykes
- Department of Psychology, King's College London, London, United Kingdom
| | - Claire Henderson
- Health Service & Population Research Department, King's College London, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, King's College London, London, United Kingdom.,School of Psychology, University of Sussex, Falmer, Sussex, United Kingdom
| |
Collapse
|
9
|
Polignano M, Lops P, de Gemmis M, Semeraro G. HELENA: An intelligent digital assistant based on a Lifelong Health User Model. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
10
|
Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Qian L, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Annas P, Hotopf M, Dobson RJB. Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis. JMIR Mhealth Uhealth 2022; 10:e40667. [PMID: 36194451 PMCID: PMC9579931 DOI: 10.2196/40667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/11/2022] [Accepted: 08/26/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Gait is an essential manifestation of depression. However, the gait characteristics of daily walking and their relationships with depression have yet to be fully explored. OBJECTIVE The aim of this study was to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. METHODS We used two ambulatory data sets (N=71 and N=215) with acceleration signals collected by wearable devices and mobile phones, respectively. We extracted 12 daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period. Spearman coefficients and linear mixed-effects models were used to explore the associations between daily-life gait features and depression symptom severity measured by the 15-item Geriatric Depression Scale (GDS-15) and 8-item Patient Health Questionnaire (PHQ-8) self-reported questionnaires. The likelihood-ratio (LR) test was used to test whether daily-life gait features could provide additional information relative to the laboratory gait features. RESULTS Higher depression symptom severity was significantly associated with lower gait cadence of high-performance walking (segments with faster walking speed) over a long-term period in both data sets. The linear regression model with long-term daily-life gait features (R2=0.30) fitted depression scores significantly better (LR test P=.001) than the model with only laboratory gait features (R2=0.06). CONCLUSIONS This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The daily-life gait patterns could provide additional information for predicting depression symptom severity relative to laboratory walking. These findings may contribute to developing clinical tools to remotely monitor mental health in real-world settings.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Linglong Qian
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Yatharth Ranjan
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Callum Stewart
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Heet Sankesara
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Falmer, United Kingdom
| | - Katie M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Carolin Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alina Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | - Sara Siddi
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Aki Rintala
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Inez Myin-Germeys
- Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Josep Maria Haro
- Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit, Amsterdam, Netherlands
- Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
| | | | | | - Matthew Hotopf
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Richard J B Dobson
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
11
|
Matcham F, Carr E, White KM, Leightley D, Lamers F, Siddi S, Annas P, de Girolamo G, Haro JM, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr DC, Narayan VA, Penninx BWHJ, Oetzmann C, Coromina M, Simblett SK, Weyer J, Wykes T, Zorbas S, Brasen JC, Myin-Germeys I, Conde P, Dobson RJB, Folarin AA, Ranjan Y, Rashid Z, Cummins N, Dineley J, Vairavan S, Hotopf M. Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder. J Affect Disord 2022; 310:106-115. [PMID: 35525507 DOI: 10.1016/j.jad.2022.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
Collapse
Affiliation(s)
- F Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - E Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - K M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - D Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - F Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - M Horsfall
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - A Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - G Lavelle
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Q Li
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - D C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
| | - V A Narayan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - B W H J Penninx
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - C Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - M Coromina
- Parc Sanitari Joan de Déu, Barcelona, Spain
| | - S K Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Weyer
- RADAR-CNS Patient Advisory Board
| | - T Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - S Zorbas
- RADAR-CNS Patient Advisory Board
| | | | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - P Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - R J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - A A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Y Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Z Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - N Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Dineley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - S Vairavan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - M Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | | |
Collapse
|
12
|
A systematic review of engagement reporting in remote measurement studies for health symptom tracking. NPJ Digit Med 2022; 5:82. [PMID: 35768544 PMCID: PMC9242990 DOI: 10.1038/s41746-022-00624-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/01/2022] [Indexed: 01/25/2023] Open
Abstract
Remote Measurement Technologies (RMTs) could revolutionise management of chronic health conditions by providing real-time symptom tracking. However, the promise of RMTs relies on user engagement, which at present is variably reported in the field. This review aimed to synthesise the RMT literature to identify how and to what extent engagement is defined, measured, and reported, and to present recommendations for the standardisation of future work. Seven databases (Embase, MEDLINE and PsycINFO (via Ovid), PubMed, IEEE Xplore, Web of Science, and Cochrane Central Register of Controlled Trials) were searched in July 2020 for papers using RMT apps for symptom monitoring in adults with a health condition, prompting users to track at least three times during the study period. Data were synthesised using critical interpretive synthesis. A total of 76 papers met the inclusion criteria. Sixty five percent of papers did not include a definition of engagement. Thirty five percent included both a definition and measurement of engagement. Four synthetic constructs were developed for measuring engagement: (i) engagement with the research protocol, (ii) objective RMT engagement, (iii) subjective RMT engagement, and (iv) interactions between objective and subjective RMT engagement. The field is currently impeded by incoherent measures and a lack of consideration for engagement definitions. A process for implementing the reporting of engagement in study design is presented, alongside a framework for definition and measurement options available. Future work should consider engagement with RMTs as distinct from the wider eHealth literature, and measure objective versus subjective RMT engagement.Registration: This review has been registered on PROSPERO [CRD42020192652].
Collapse
|
13
|
Adler DA, Wang F, Mohr DC, Choudhury T. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLoS One 2022; 17:e0266516. [PMID: 35476787 PMCID: PMC9045602 DOI: 10.1371/journal.pone.0266516] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/23/2022] [Indexed: 11/19/2022] Open
Abstract
Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability.
Collapse
Affiliation(s)
- Daniel A. Adler
- Department of Information Science, Cornell Tech, New York, New York, United States of America
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - David C. Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Tanzeem Choudhury
- Department of Information Science, Cornell Tech, New York, New York, United States of America
| |
Collapse
|
14
|
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] [Abstract] [Key Words] [Grants] [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.
Collapse
Affiliation(s)
- Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust 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
| |
Collapse
|
15
|
Guo Y, Liu X, Wang X, Zhu T, Zhan W. Automatic Decision-Making Style Recognition Method Using Kinect Technology. Front Psychol 2022; 13:751914. [PMID: 35310212 PMCID: PMC8931824 DOI: 10.3389/fpsyg.2022.751914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.
Collapse
Affiliation(s)
- Yu Guo
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoqian Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyang Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Tingshao Zhu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Zhan
- Information Science Research Institute, China Electronics Technology Group Corporation, Beijing, China
| |
Collapse
|
16
|
Rapid-acting antidepressants and the circadian clock. Neuropsychopharmacology 2022; 47:805-816. [PMID: 34837078 PMCID: PMC8626287 DOI: 10.1038/s41386-021-01241-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 09/20/2021] [Accepted: 11/08/2021] [Indexed: 12/13/2022]
Abstract
A growing number of epidemiological and experimental studies has established that circadian disruption is strongly associated with psychiatric disorders, including major depressive disorder (MDD). This association is becoming increasingly relevant considering that modern lifestyles, social zeitgebers (time cues) and genetic variants contribute to disrupting circadian rhythms that may lead to psychiatric disorders. Circadian abnormalities associated with MDD include dysregulated rhythms of sleep, temperature, hormonal secretions, and mood which are modulated by the molecular clock. Rapid-acting antidepressants such as subanesthetic ketamine and sleep deprivation therapy can improve symptoms within 24 h in a subset of depressed patients, in striking contrast to conventional treatments, which generally require weeks for a full clinical response. Importantly, animal data show that sleep deprivation and ketamine have overlapping effects on clock gene expression. Furthermore, emerging data implicate the circadian system as a critical component involved in rapid antidepressant responses via several intracellular signaling pathways such as GSK3β, mTOR, MAPK, and NOTCH to initiate synaptic plasticity. Future research on the relationship between depression and the circadian clock may contribute to the development of novel therapeutic strategies for depression-like symptoms. In this review we summarize recent evidence describing: (1) how the circadian clock is implicated in depression, (2) how clock genes may contribute to fast-acting antidepressants, and (3) the mechanistic links between the clock genes driving circadian rhythms and neuroplasticity.
Collapse
|
17
|
Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Front Psychiatry 2022; 13:895860. [PMID: 35958638 PMCID: PMC9360315 DOI: 10.3389/fpsyt.2022.895860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. OBJECTIVE The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. METHODS We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. RESULTS Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). CONCLUSION The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
Collapse
Affiliation(s)
- Redwan Maatoug
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Antoine Oudin
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Vladimir Adrien
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Bertrand Saudreau
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Département de Psychiatrie de l'Enfant et de l'Adolescent, Assistance Publique des Hôpitaux de Paris (AP-HP), Sorbonne Université, Paris, France
| | - Olivier Bonnot
- CHU de Nantes, Department of Child and Adolescent Psychiatry, Nantes, France.,Pays de la Loire Psychology Laboratory, Nantes, France
| | - Bruno Millet
- Service de Psychiatrie Adulte de la Pitié-Salpêtrière, Institut du Cerveau (ICM), Sorbonne Université, Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France
| | - Florian Ferreri
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Stephane Mouchabac
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France
| | - Alexis Bourla
- iCRIN (Infrastructure for Clinical Research in Neurosciences), Paris Brain Institute (ICM), Sorbonne Université, INSERM, CNRS, Paris, France.,Department of Psychiatry, Sorbonne Université, Hôpital Saint Antoine-Assistance Publique des Hôpitaux de Paris (AP-HP), Paris, France.,INICEA Korian, Paris, France
| |
Collapse
|
18
|
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] [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.
Collapse
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
| |
Collapse
|