This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (903) Mobile Health (mhealth) (2982) mHealth for Data Collection and Research (981) mHealth for Wellness, Behavior Change and Prevention (2922) Depression and Mood Disorders; Suicide Prevention (1392) Fitness Trackers and Smart Pedometers/Accelerometers (571)

Published on in Vol 6, No 8 (2018): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9691, first published .
Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Authors of this article:

Darius A Rohani1, 2 Author Orcid Image ;   Maria Faurholt-Jepsen3 Author Orcid Image ;   Lars Vedel Kessing3, 4 Author Orcid Image ;   Jakob E Bardram1, 2 Author Orcid Image
Article Authors Cited by (127) Tweetations (17) Metrics

Journals

  1. Goodday S. The unique utility of digital technology for bipolar disorder. Bipolar Disorders 2020;22(2):197 View
  2. Radhakrishnan K, Kim M, Burgermaster M, Brown R, Xie B, Bray M, Fournier C. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548 View
  3. Gashi S, Di Lascio E, Stancu B, Swain V, Mishra V, Gjoreski M, Santini S. Detection of Artifacts in Ambulatory Electrodermal Activity Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(2):1 View
  4. Jacobson N, Weingarden H, Wilhelm S. Using Digital Phenotyping to Accurately Detect Depression Severity. Journal of Nervous & Mental Disease 2019;207(10):893 View
  5. Torous J, Wisniewski H, Bird B, Carpenter E, David G, Elejalde E, Fulford D, Guimond S, Hays R, Henson P, Hoffman L, Lim C, Menon M, Noel V, Pearson J, Peterson R, Susheela A, Troy H, Vaidyam A, Weizenbaum E, Naslund J, Keshavan M. Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach. Journal of Technology in Behavioral Science 2019;4(2):73 View
  6. Chung M, Leung S, Välimäki M. Use of tracking technology to examine life-space mobility among people with depression: a systematic review protocol. BMJ Open 2020;10(1):e034208 View
  7. Henson P, Barnett I, Keshavan M, Torous J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophrenia 2020;6(1) View
  8. Weingarden H, Matic A, Calleja R, Greenberg J, Harrison O, Wilhelm S. Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial. JMIR mHealth and uHealth 2020;8(6):e16350 View
  9. Rashidisabet H, Thomas P, Ajilore O, Zulueta J, Moore R, Leow A. A systems biology approach to the digital behaviorome. Current Opinion in Systems Biology 2020;20:8 View
  10. Thieme A, Belgrave D, Doherty G. Machine Learning in Mental Health. ACM Transactions on Computer-Human Interaction 2020;27(5):1 View
  11. H. Birk R, Samuel G. Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’. Sociology of Health & Illness 2020;42(8):1873 View
  12. Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman M, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751 View
  13. Fillekes M, Giannouli E, Kim E, Zijlstra W, Weibel R. Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. International Journal of Health Geographics 2019;18(1) View
  14. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
  15. Aubourg T, Demongeot J, Provost H, Vuillerme N. Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study. JMIR mHealth and uHealth 2020;8(2):e12452 View
  16. Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing L, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow P. Smartphones in mental health: a critical review of background issues, current status and future concerns. International Journal of Bipolar Disorders 2020;8(1) View
  17. Van Til K, McInnis M, Cochran A. A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder. Bipolar Disorders 2020;22(2):182 View
  18. Bardram J, Matic A. A Decade of Ubiquitous Computing Research in Mental Health. IEEE Pervasive Computing 2020;19(1):62 View
  19. Lorenz N, Spada J, Sander C, Riedel-Heller S, Hegerl U. Circadian skin temperature rhythms, circadian activity rhythms and sleep in individuals with self-reported depressive symptoms. Journal of Psychiatric Research 2019;117:38 View
  20. Wilhelm S, Weingarden H, Ladis I, Braddick V, Shin J, Jacobson N. Cognitive-Behavioral Therapy in the Digital Age: Presidential Address. Behavior Therapy 2020;51(1):1 View
  21. Tazawa Y, Liang K, Yoshimura M, Kitazawa M, Kaise Y, Takamiya A, Kishi A, Horigome T, Mitsukura Y, Mimura M, Kishimoto T. Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning. Heliyon 2020;6(2):e03274 View
  22. Spinazze P, Rykov Y, Bottle A, Car J. Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol. BMJ Open 2019;9(12):e032255 View
  23. Aubourg T, Demongeot J, Renard F, Provost H, Vuillerme N. Association between social asymmetry and depression in older adults: A phone Call Detail Records analysis. Scientific Reports 2019;9(1) View
  24. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020 View
  25. Torous J, Gershon A, Hays R, Onnela J, Baker J. Digital Phenotyping for the Busy Psychiatrist: Clinical Implications and Relevance. Psychiatric Annals 2019;49(5):196 View
  26. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  27. Busk J, Faurholt-Jepsen M, Frost M, Bardram J, Vedel Kessing L, Winther O. Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach. JMIR mHealth and uHealth 2020;8(4):e15028 View
  28. Zulueta J, Leow A, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. Focus 2020;18(2):175 View
  29. Hilty D, Armstrong C, Edwards-Stewart A, Gentry M, Luxton D, Krupinski E. Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review. Journal of Technology in Behavioral Science 2021;6(2):252 View
  30. Stanislaus S, Vinberg M, Melbye S, Frost M, Busk J, Bardram J, Kessing L, Faurholt-Jepsen M. Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals. International Journal of Bipolar Disorders 2020;8(1) View
  31. Wang Y, Ren X, Liu X, Zhu T. Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study. JMIR mHealth and uHealth 2021;9(1):e19046 View
  32. Goltermann J, Emden D, Leehr E, Dohm K, Redlich R, Dannlowski U, Hahn T, Opel N. Smartphone-Based Self-Reports of Depressive Symptoms Using the Remote Monitoring Application in Psychiatry (ReMAP): Interformat Validation Study. JMIR Mental Health 2021;8(1):e24333 View
  33. Peltonen E, Sharmila P, Opoku Asare K, Visuri A, Lagerspetz E, Ferreira D. When phones get personal: Predicting Big Five personality traits from application usage. Pervasive and Mobile Computing 2020;69:101269 View
  34. Levin H, Egger D, Andres L, Johnson M, Bearman S, de Barbaro K. Sensing everyday activity: Parent perceptions and feasibility. Infant Behavior and Development 2021;62:101511 View
  35. Ebner-Priemer U, Mühlbauer E, Neubauer A, Hill H, Beier F, Santangelo P, Ritter P, Kleindienst N, Bauer M, Schmiedek F, Severus E. Digital phenotyping: towards replicable findings with comprehensive assessments and integrative models in bipolar disorders. International Journal of Bipolar Disorders 2020;8(1) View
  36. Mercier H, Hamner J, Torous J, Onnela J, Taylor J. Digital Phenotyping to Quantify Psychosocial Well-Being Trajectories After Spinal Cord Injury. American Journal of Physical Medicine & Rehabilitation 2020;99(12):1138 View
  37. Peis I, López-Moríñigo J, Pérez-Rodríguez M, Barrigón M, Ruiz-Gómez M, Artés-Rodríguez A, Baca-García E. Actigraphic recording of motor activity in depressed inpatients: a novel computational approach to prediction of clinical course and hospital discharge. Scientific Reports 2020;10(1) View
  38. Craven M, Andrews J, Lang A, Simblett S, Bruce S, Thorpe S, Wykes T, Morriss R, Hollis C. Informing the Development of a Digital Health Platform Through Universal Points of Care: Qualitative Survey Study. JMIR Formative Research 2020;4(11):e22756 View
  39. Goodday S, Geddes J, Friend S. Disrupting the power balance between doctors and patients in the digital era. The Lancet Digital Health 2021;3(3):e142 View
  40. Wu C, Barczyk A, Craddock R, Harari G, Thomaz E, Shumake J, Beevers C, Gosling S, Schnyer D. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health 2021;20:100180 View
  41. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278 View
  42. Koltsova O, Mararitsa L, Terpilovskii M, Sinyavskaya Y. Social signature in an online environment: Stability and cognitive limits. Computers in Human Behavior 2021;122:106856 View
  43. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  44. Vlisides-Henry R, Gao M, Thomas L, Kaliush P, Conradt E, Crowell S. Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk. Frontiers in Psychiatry 2021;12 View
  45. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
  46. Tønning M, Faurholt-Jepsen M, Frost M, Bardram J, Kessing L. Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder. Frontiers in Psychiatry 2021;12 View
  47. MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638 View
  48. Roth C, Papassotiropoulos A, Brühl A, Lang U, Huber C. Psychiatry in the Digital Age: A Blessing or a Curse?. International Journal of Environmental Research and Public Health 2021;18(16):8302 View
  49. Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman M, Rose J. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study. Journal of Medical Internet Research 2021;23(8):e28918 View
  50. Nickels S, Edwards M, Poole S, Winter D, Gronsbell J, Rozenkrants B, Miller D, Fleck M, McLean A, Peterson B, Chen Y, Hwang A, Rust-Smith D, Brant A, Campbell A, Chen C, Walter C, Arean P, Hsin H, Myers L, Marks Jr W, Mega J, Schlosser D, Conrad A, Califf R, Fromer M. Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling. JMIR Mental Health 2021;8(8):e27589 View
  51. Wahl K, Scholl P, Wirth S, Miché M, Häni J, Schülin P, Lieb R. On the automatic detection of enacted compulsive hand washing using commercially available wearable devices. Computers in Biology and Medicine 2022;143:105280 View
  52. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  53. Sheikh-Wu S, Liang Z, Downs C. The Relationship Between Telomeres, Cognition, Mood, and Physical Function: A Systematic Review. Biological Research For Nursing 2023;25(2):227 View
  54. Olsen J, Nicholls N, Caryl F, Mendoza J, Panis L, Dons E, Laeremans M, Standaert A, Lee D, Avila-Palencia I, de Nazelle A, Nieuwenhuijsen M, Mitchell R. Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities. SSM - Population Health 2022;19:101172 View
  55. De La Fabián R, Jiménez-Molina Á, Pizarro Obaid F. A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry. Big Data & Society 2023;10(1) View
  56. Rohani D, Springer A, Hollis V, Bardram J, Whittaker S. Recommending Activities for Mental Health and Well-Being: Insights From Two User Studies. IEEE Transactions on Emerging Topics in Computing 2021;9(3):1183 View
  57. Panicheva P, Mararitsa L, Sorokin S, Koltsova O, Rosso P. Predicting subjective well-being in a high-risk sample of Russian mental health app users. EPJ Data Science 2022;11(1) View
  58. Nunes Vilaza G, Coyle D, Bardram J. Public Attitudes to Digital Health Research Repositories: Cross-sectional International Survey. Journal of Medical Internet Research 2021;23(10):e31294 View
  59. Bisby M, Dear B, Karin E, Fogliati R, Dudeney J, Ryan K, Fararoui A, Nielssen O, Staples L, Kayrouz R, Cross S, Titov N. An open trial of the Things You Do Questionnaire: Changes in daily actions during internet-delivered treatment for depressive and anxiety symptoms. Journal of Affective Disorders 2023;329:483 View
  60. Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4) View
  61. Kathan A, Harrer M, Küster L, Triantafyllopoulos A, He X, Milling M, Gerczuk M, Yan T, Rajamani S, Heber E, Grossmann I, Ebert D, Schuller B. Personalised depression forecasting using mobile sensor data and ecological momentary assessment. Frontiers in Digital Health 2022;4 View
  62. Zhang Y, Folarin A, Sun S, Cummins N, Vairavan S, Qian L, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Associations Between Depression Symptom Severity and Daily-Life Gait Characteristics Derived From Long-Term Acceleration Signals in Real-World Settings: Retrospective Analysis. JMIR mHealth and uHealth 2022;10(10):e40667 View
  63. De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr D, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine 2022;5(1) View
  64. Cao X, Liu X. Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World Journal of Psychiatry 2022;12(10):1287 View
  65. Stein D, Shoptaw S, Vigo D, Lund C, Cuijpers P, Bantjes J, Sartorius N, Maj M. Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration. World Psychiatry 2022;21(3):393 View
  66. Lee U, Jung G, Ma E, Kim J, Kim H, Alikhanov J, Noh Y, Kim H. Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions. IEEE/CAA Journal of Automatica Sinica 2023;10(1):42 View
  67. Girousse E, Vuillerme N. The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(12):e38785 View
  68. Niedermann C, Ostermann T. On Gardening, Ice Cream, Mental Health, and Movement. Journal of Integrative and Complementary Medicine 2022;28(5):373 View
  69. Orr M, MacLeod L, Bagnell A, McGrath P, Wozney L, Meier S. The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. Computers in Human Behavior 2023;140:107603 View
  70. Kamath J, Barriera R, Jain N, Keisari E, Wang B. Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World Journal of Psychiatry 2022;12(3):393 View
  71. Basil G, Sprau A, Ghogawala Z, Yoon J, Wang M. “Houston, we have a problem”: the difficulty of measuring outcomes in spinal surgery. Journal of Neurosurgery: Spine 2021;34(3):537 View
  72. Agarwal A, Ali Z, Shofer F, Xiong R, Hemmons J, Spencer E, Abdel-Rahman D, Sennett B, Delgado M. Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys. JMIR Formative Research 2022;6(3):e31894 View
  73. Thieme A, Hanratty M, Lyons M, Palacios J, Marques R, Morrison C, Doherty G. Designing Human-centered AI for Mental Health: Developing Clinically Relevant Applications for Online CBT Treatment. ACM Transactions on Computer-Human Interaction 2023;30(2):1 View
  74. Kishimoto T, Kinoshita S, Kikuchi T, Bun S, Kitazawa M, Horigome T, Tazawa Y, Takamiya A, Hirano J, Mimura M, Liang K, Koga N, Ochiai Y, Ito H, Miyamae Y, Tsujimoto Y, Sakuma K, Kida H, Miura G, Kawade Y, Goto A, Yoshino F. Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol. Frontiers in Psychiatry 2022;13 View
  75. Matcham F, Carr E, White K, Leightley D, Lamers F, Siddi S, Annas P, de Girolamo G, Haro J, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr D, Narayan V, Penninx B, Oetzmann C, Coromina M, Simblett S, Weyer J, Wykes T, Zorbas S, Brasen J, Myin-Germeys I, Conde P, Dobson R, Folarin A, 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. Journal of Affective Disorders 2022;310:106 View
  76. Flint J. The genetic basis of major depressive disorder. Molecular Psychiatry 2023;28(6):2254 View
  77. Almuteb I, Hua R, Wang Y. Smart insoles review (2008-2021): Applications, potentials, and future. Smart Health 2022;25:100301 View
  78. Knauer J, Terhorst Y, Philippi P, Kallinger S, Eiler S, Kilian R, Waldmann T, Moshagen M, Bader M, Baumeister H. Effectiveness and cost-effectiveness of a web-based routine assessment with integrated recommendations for action for depression and anxiety (RehaCAT+): protocol for a cluster randomised controlled trial for patients with elevated depressive symptoms in rehabilitation facilities. BMJ Open 2022;12(6):e061259 View
  79. Alfalahi H, Khandoker A, Chowdhury N, Iakovakis D, Dias S, Chaudhuri K, Hadjileontiadis L. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Scientific Reports 2022;12(1) View
  80. Laiou P, Kaliukhovich D, Folarin A, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F, Ivan A, Lavelle G, Siddi S, Lamers F, Penninx B, Haro J, Annas P, Cummins N, Vairavan S, Manyakov N, Narayan V, Dobson R, Hotopf M. The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones. JMIR mHealth and uHealth 2022;10(1):e28095 View
  81. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  82. Zhang Y, Pratap A, Folarin A, Sun S, Cummins N, Matcham F, Vairavan S, Dineley J, Ranjan Y, Rashid Z, Conde P, Stewart C, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Rambla C, Simblett S, Nica R, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Annas P, Narayan V, Hotopf M, Dobson R. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. npj Digital Medicine 2023;6(1) View
  83. Sato S, Hiratsuka T, Hasegawa K, Watanabe K, Obara Y, Kariya N, Shinba T, Matsui T. Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices. Sensors 2023;23(8):3867 View
  84. Kim J, Wang B, Kim M, Lee J, Kim H, Roh D, Lee K, Hong S, Lim J, Kim J, Ryan N. Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study. JMIR Formative Research 2023;7:e45991 View
  85. Buda T, Khwaja M, Garriga R, Matic A, Parackal M. Two edges of the screen: Unpacking positive and negative associations between phone use in everyday contexts and subjective well-being. PLOS ONE 2023;18(4):e0284104 View
  86. Niedermann C, Anheyer D, Seeligmüller E, Ostermann T. Traces of health—A landscape design task as a diagnostic aid for detecting mental burden? A qualitative focus group study. Frontiers in Psychology 2023;14 View
  87. Sun S, Folarin A, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Vairavan S, Narayan V, Annas P, Hotopf M, Dobson R. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. Journal of Medical Internet Research 2023;25:e45233 View
  88. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  89. Williams G, Al-Baraikan A, Rademakers F, Ciravegna F, van de Vosse F, Lawrie A, Rothman A, Ashley E, Wilkins M, Lawford P, Omholt S, Wisløff U, Hose D, Chico T, Gunn J, Morris P. Wearable technology and the cardiovascular system: the future of patient assessment. The Lancet Digital Health 2023;5(7):e467 View
  90. Pan W, Deng F, Wang X, Hang B, Zhou W, Zhu T. Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls. Frontiers in Psychiatry 2023;14 View
  91. Deng S, Cheng X, Hu R. Detecting depression and its severity based on social media digital cues. Industrial Management & Data Systems 2023;123(12):3038 View
  92. Wahl K, Scholl P, Miché M, Wirth S, Burchard R, Lieb R. Real-time detection of obsessive-compulsive hand washing with wearables: Research procedure, usefulness and discriminative performance. Journal of Obsessive-Compulsive and Related Disorders 2023;39:100845 View
  93. Terhorst Y, Weilbacher N, Suda C, Simon L, Messner E, Sander L, Baumeister H. Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial. Frontiers in Digital Health 2023;5 View
  94. Walsh A, Naughton G, Sharpe T, Zajkowska Z, Malys M, van Heerden A, Mondelli V. A collaborative realist review of remote measurement technologies for depression in young people. Nature Human Behaviour 2024;8(3):480 View
  95. Karimi F, Amoozgar Z, Reiazi R, Hosseinzadeh M, Rawassizadeh R. Longitudinal analysis of heart rate and physical activity collected from smartwatches. CCF Transactions on Pervasive Computing and Interaction 2024;6(1):18 View
  96. Zhang Y, Folarin A, Dineley J, Conde P, de Angel V, Sun S, Ranjan Y, Rashid Z, Stewart C, Laiou P, Sankesara H, Qian L, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Schuller B, Vairavan S, Wykes T, Haro J, Penninx B, Narayan V, Hotopf M, Dobson R, Cummins N. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. Journal of Affective Disorders 2024;355:40 View
  97. Zierer C, Behrendt C, Lepach-Engelhardt A. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. Journal of Affective Disorders 2024;356:438 View
  98. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Ibrahim O, Rosenberg J, O’Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping Review. SSRN Electronic Journal 2024 View
  99. Rottstädt F, Becker E, Wilz G, Croy I, Baumeister H, Terhorst Y. Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  100. Halabi R, Mulsant B, Alda M, DeShaw A, Hintze A, Husain M, O'Donovan C, Patterson R, Ortiz A. Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder. Journal of Psychiatric Research 2024;174:326 View
  101. Englhardt Z, Ma C, Morris M, Chang C, Xu X, Qin L, McDuff D, Liu X, Patel S, Iyer V. From Classification to Clinical Insights. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(2):1 View
  102. Löchner J, Moshe I, Schiepek G, Schuller B, Schoedel R, Rodgar M, Wac K, Seizer L. Tracking health-related quality of life dynamics: Advances in ambulatory assessment methods. Brain Behavior and Immunity Integrative 2024;6:100061 View
  103. Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  104. Wang W, Chen J, Hu Y, Liu H, Chen J, Gadekallu T, Garg L, Guizani M, Hu X. Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection. International Journal of Cognitive Computing in Engineering 2024;5:307 View
  105. Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e51875 View
  106. Beames J, Han J, Shvetcov A, Zheng W, Slade A, Dabash O, Rosenberg J, O'Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review. Heliyon 2024;10(15):e35472 View
  107. Lamichhane B, Moukaddam N, Sabharwal A. Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions. Scientific Reports 2024;14(1) View
  108. Jilka S, Giacco D. Digital phenotyping: how it could change mental health care and why we should all keep up. Journal of Mental Health 2024;33(4):439 View
  109. Edler J, Winter M, Steinmetz H, Cohrdes C, Baumeister H, Pryss R. Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study. Interactive Journal of Medical Research 2024;13:e53248 View
  110. Terhorst Y, Messner E, Asare K, Montag C, Kannen C, Baumeister H. Which Smartphone-Based Sensing Features Matter in Depression Severity Prediction? Results from an Observation Study. (Preprint). Journal of Medical Internet Research 2023 View
  111. Lee T, Chen C, Chen I, Chen H, Liu C, Wu S, Hsiao C, Kuo P. Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study. Journal of Medical Internet Research 2024;26:e55635 View
  112. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Välimäki M, Hipp K. Advanced Practice in Mental Health Nursing. View
  2. Opoku Asare K, Visuri A, Vega J, Ferreira D. Wireless Mobile Communication and Healthcare. View
  3. Hilty D, Armstrong C, Edwards-Stewart A, Luxton D. Digital Therapeutics for Mental Health and Addiction. View
  4. Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  5. Harrer M, Terhorst Y, Baumeister H, Ebert D. Digitale Gesundheitsinterventionen. View