1
|
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
|
2
|
Lukka L, Palva JM. The Development of Game-Based Digital Mental Health Interventions: Bridging the Paradigms of Health Care and Entertainment. JMIR Serious Games 2023; 11:e42173. [PMID: 37665624 PMCID: PMC10507521 DOI: 10.2196/42173] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/24/2023] [Accepted: 07/15/2023] [Indexed: 09/05/2023] Open
Abstract
Game elements are increasingly used to improve user engagement in digital mental health interventions, and specific game mechanics may yield therapeutic effects per se and thereby contribute to digital mental health intervention efficacy. However, only a few commercial game-based interventions are available. We suggest that the key challenge in their development reflects the tension between the 2 underlying paradigms, health care and entertainment, which have disparate goals and processes in digital development. We describe 3 approaches currently used to negotiate the 2 paradigms: the gamification of health care software, designing serious games, and purpose shifting existing entertainment games. We advanced an integrative framework to focus attention on 4 key themes in intervention development: target audience, engagement, mechanisms of action, and health-related effectiveness. On each theme, we show how the 2 paradigms contrast and can complement each other. Finally, we consider the 4 interdependent themes through the new product development phases from concept to production. Our viewpoint provides an integrative synthesis that facilitates the research, design, and development of game-based digital mental health interventions.
Collapse
Affiliation(s)
- Lauri Lukka
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, United Kingdom
| |
Collapse
|
3
|
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
|
4
|
Braund TA, O'Dea B, Bal D, Maston K, Larsen M, Werner-Seidler A, Tillman G, Christensen H. Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study. JMIR Ment Health 2023; 10:e44986. [PMID: 37184904 DOI: 10.2196/44986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/24/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. OBJECTIVE In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. METHODS A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. RESULTS Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. CONCLUSIONS Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. TRIAL REGISTRATION Australian and New Zealand Clinical Trial Registry, ACTRN12619000855123; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377664&isReview=true.
Collapse
Affiliation(s)
- Taylor A Braund
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Bridianne O'Dea
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Debopriyo Bal
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Kate Maston
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Mark Larsen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Aliza Werner-Seidler
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| | - Gabriel Tillman
- Institute of Health and Wellbeing, Federation University, Ballarat, Australia
| | - Helen Christensen
- Faculty of Medicine and Health, University of New South Wales, Kensington, Australia
- Black Dog Institute, University of New South Wales, Randwick, Australia
| |
Collapse
|
5
|
Bjella TD, Collier Høegh M, Holmstul Olsen S, Aminoff SR, Barrett E, Ueland T, Icick R, Andreassen OA, Nerhus M, Myhre Ihler H, Hagen M, Busch-Christensen C, Melle I, Lagerberg TV. Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:910533. [PMID: 35935144 PMCID: PMC9354925 DOI: 10.3389/fmedt.2022.910533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionThe illness course of bipolar disorder (BD) is highly heterogeneous with substantial variation between individuals with the same BD subtype and within individuals over time. This heterogeneity is not well-delineated and hampers the development of more targeted treatment. Furthermore, although lifestyle-related behaviors are believed to play a role in the illness course, such mechanisms are poorly understood. To address some of these knowledge gaps, we aimed to develop an app for collection of multi-dimensional longitudinal data on BD-relevant symptoms and lifestyle-related behaviors.MethodsAn app named MinDag was developed at the Norwegian Center for Mental Disorders Research in Oslo, Norway. The app was designed to tap into selected areas: mood, sleep, functioning/activities (social, occupational, physical exercise, leisure), substance use, emotional reactivity, and psychotic experiences. Ethical, security and usability issues were highly prioritized throughout the development and for the final app solution. We conducted beta- and pilot testing to eliminate technical problems and enhance usability and acceptability.ResultsThe final version of MinDag comprises six modules; three which are presented for the user once daily (the Sleep module in the morning and the Mood and Functoning/Activities modules in the evening) and three which are presented once weekly (Substance Use, Emotional Reactivity, and Psychotic Experiences modules). In general, MinDag was well received in both in the beta-testing and the pilot study, and the participants provided valuable feedback that was taken into account in the final development. MinDag is now in use as part of the research protocol at the NORMENT center and in a specialized treatment unit for BD at Oslo University Hospital in Norway.DiscussionWe believe that MinDag will generate unique longitudinal data well suited for capturing the heterogeneity of BD and clarifying important unresolved issues such as how life-style related behavior may influence BD symptoms. Also, the experiences and knowledge derived from the development of MinDag may contribute to improving the security, acceptability, and benefit of digital tools in mental health.
Collapse
Affiliation(s)
- Thomas D. Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- *Correspondence: Thomas D. Bjella
| | - Margrethe Collier Høegh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Stine Holmstul Olsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sofie R. Aminoff
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Elizabeth Barrett
- Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Romain Icick
- INSERM, UMR_S1144, Paris University, Paris, France
- FondaMental Foundation, Créteil, France
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Mari Nerhus
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Special Psychiatry, Division of Mental Health Services, Akershus University Hospital, Lørenskog, Norway
| | - Henrik Myhre Ihler
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Marthe Hagen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Cecilie Busch-Christensen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Melle
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Trine Vik Lagerberg
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|