Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

TY - JOUR

T1 - Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity

T2 - Retrospective Analysis

AU - RADAR-CNS Consortium

AU - Sun, Shaoxiong

AU - Folarin, Amos A.

AU - Zhang, Yuezhou

AU - Cummins, Nicholas

AU - Garcia-Dias, Rafael

AU - Stewart, Callum

AU - Ranjan, Yatharth

AU - Rashid, Zulqarnain

AU - Conde, Pauline

AU - Laiou, Petroula

AU - Sankesara, Heet

AU - Matcham, Faith

AU - Leightley, Daniel

AU - White, Katie M.

AU - Oetzmann, Carolin

AU - Ivan, Alina

AU - Lamers, Femke

AU - Siddi, Sara

AU - Simblett, Sara

AU - Nica, Raluca

AU - Rintala, Aki

AU - Mohr, David C.

AU - Myin-Germeys, Inez

AU - Wykes, Til

AU - Haro, Josep Maria

AU - Penninx, Brenda W.J.H.

AU - Vairavan, Srinivasan

AU - Narayan, Vaibhav A.

AU - Annas, Peter

AU - Hotopf, Matthew

AU - Dobson, Richard J.B.

N1 - Funding Information: SV and VAN are employees of Janssen Research and Development LLC. PA is employed by the pharmaceutical company H Lundbeck A/S. DCM has accepted honoraria and consulting fees from Otsuka Pharmaceuticals Co, Ltd; Optum Behavioral Health; Centerstone Research Institute; and the One Mind Foundation; has received royalties from Oxford Press; and has an ownership interest in Adaptive Health, Inc. MH is the principal investigator of Remote Assessment of Disease and Relapse–Central Nervous System (RADAR-CNS), a private-public precompetitive consortium that receives funding from Janssen Research and Development LLC, UCB, H Lundbeck A/S, MSD, and Biogen. All other authors declare no other conflicts of interest. Funding Information: This study represents independent research partly funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC) at South London, Maudsley National Health Service (NHS) Foundation Trust, King’s College London, and European Union/European Federation of Pharmaceutical Industries and Associations (EFPIA) Innovative Medicines Initiative (IMI)-2 Joint Undertaking (Remote Assessment of Disease and Relapse–Central Nervous System [RADAR-CNS]: 115902). This communication reflects the views of the RADAR-CNS consortium, and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Participant recruitment in Amsterdam, the Netherlands, was partially accomplished through a Dutch web-based registry that facilitates participant recruitment for neuroscience studies [65]. It is funded by ZonMw-Memorabel (73305095003), a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands (Alzheimer Nederland), and Brain Foundation Netherlands (Hersenstichting). This study has also received support from Health Data Research UK (funded by the UK Medical Research Council [MRC]), Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, Wellcome Trust, and the NIHR University College London Hospitals Biomedical Research Centre. Funding Information: The RADAR-CNS project has received funding from the IMI-2 Joint Undertaking (115902). This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Program and EFPIA. The funding bodies have not been involved in the design of the study, the collection or analysis of data, or the interpretation of data. The views expressed are those of the author or authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The authors thank all members of the RADAR-CNS patient advisory board for their contribution to the device selection procedures and their invaluable advice throughout the study protocol design. This research was reviewed by a team with experience of mental health problems and their carers, who have been specially trained to advise on research proposals and documentation through the Feasibility and Acceptability Support Team for Researchers (FAST-R), a free confidential service in England provided by the NIHR Maudsley BRC via King’s College London and the South London and Maudsley NHS Foundation Trust. The authors thank all Genetic Links to Anxiety and Depression Study volunteers for their participation and gratefully acknowledge the NIHR BioResource, NIHR BioResource Centres, NHS Trusts, and staff for their contribution. The authors also acknowledge NIHR BRC, King’s College London, the South London and Maudsley NHS Trust, and King’s Health Partners. The authors thank the NIHR, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Program. Funding Information: This study represents independent research partly funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC) at South London, Maudsley National Health Service (NHS) Foundation Trust, King’s College London, and European Union/European Federation of Pharmaceutical Industries and Associations (EFPIA) Innovative Medicines Initiative (IMI)-2 Joint Undertaking (Remote Assessment of Disease and Relapse–Central Nervous System [RADAR-CNS]: 115902). This communication reflects the views of the RADAR-CNS consortium, and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Participant recruitment in Amsterdam, the Netherlands, was partially accomplished through a Dutch web-based registry that facilitates participant recruitment for neuroscience studies [65]. It is funded by ZonMw-Memorabel (73305095003), a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands (Alzheimer Nederland), and Brain Foundation Netherlands (Hersenstichting). This study has also received support from Health Data Research UK (funded by the UK Medical Research Council [MRC]), Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, Wellcome Trust, and the NIHR University College London Hospitals Biomedical Research Centre. The RADAR-CNS project has received funding from the IMI-2 Joint Undertaking (115902). This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Program and EFPIA. The funding bodies have not been involved in the design of the study, the collection or analysis of data, or the interpretation of data. The views expressed are those of the author or authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. The authors thank all members of the RADAR-CNS patient advisory board for their contribution to the device selection procedures and their invaluable advice throughout the study protocol design. This research was reviewed by a team with experience of mental health problems and their carers, who have been specially trained to advise on research proposals and documentation through the Feasibility and Acceptability Support Team for Researchers (FAST-R), a free confidential service in England provided by the NIHR Maudsley BRC via King’s College London and the South London and Maudsley NHS Foundation Trust. The authors thank all Genetic Links to Anxiety and Depression Study volunteers for their participation and gratefully acknowledge the NIHR BioResource, NIHR BioResource Centres, NHS Trusts, and staff for their contribution. The authors also acknowledge NIHR BRC, King’s College London, the South London and Maudsley NHS Trust, and King’s Health Partners. The authors thank the NIHR, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Program. CO is supported by the UK MRC (MR/N013700/1) and King’s College London member of the MRC Doctoral Training Partnership in Biomedical Sciences. RJBD is supported by the following: (1) NIHR BRC at the South London and Maudsley NHS Foundation Trust and King’s College London; (2) Health Data Research UK, which is funded by the UK MRC, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart consortium, funded by the IMI-2 Joint Undertaking (116074); this Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Program and EFPIA, and it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and the European Society of Cardiology; (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the NIHR BRC at the South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare; and (7) the NIHR Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust. Funding Information: CO is supported by the UK MRC (MR/N013700/1) and King’s College London member of the MRC Doctoral Training Partnership in Biomedical Sciences. RJBD is supported by the following: (1) NIHR BRC at the South London and Maudsley NHS Foundation Trust and King’s College London; (2) Health Data Research UK, which is funded by the UK MRC, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart consortium, funded by the IMI-2 Joint Undertaking (116074); this Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Program and EFPIA, and it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and the European Society of Cardiology; (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the NIHR BRC at the South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare; and (7) the NIHR Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust. Publisher Copyright: ©Shaoxiong Sun, Amos A Folarin, Yuezhou Zhang, Nicholas Cummins, Rafael Garcia-Dias, Callum Stewart, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Petroula Laiou, Heet Sankesara, Faith Matcham, Daniel Leightley, Katie M White, Carolin Oetzmann, Alina Ivan, Femke Lamers, Sara Siddi, Sara Simblett, Raluca Nica, Aki Rintala, David C Mohr, Inez Myin-Germeys, Til Wykes, Josep Maria Haro, Brenda W J H Penninx, Srinivasan Vairavan, Vaibhav A Narayan, Peter Annas, Matthew Hotopf, Richard J B Dobson, RADAR-CNS Consortium.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - behavioral patterns

KW - depression

KW - digital phenotypes

KW - missing data

KW - mobile health

KW - mobile phone

KW - smartphones

KW - wearable devices

UR - http://www.scopus.com/inward/record.url?scp=85168066275&partnerID=8YFLogxK

U2 - 10.2196/45233

DO - 10.2196/45233

M3 - Article

C2 - 37578823

AN - SCOPUS:85168066275

SN - 1438-8871

VL - 25

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

M1 - e45233

ER -