Published on in Vol 19, No 7 (2017): July

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Journals

  1. Yang Y, Ryu G, Park C, Yeom I, Shim K, Choi M. Mood and Stress Evaluation of Adult Patients With Moyamoya Disease in Korea: Ecological Momentary Assessment Method Using a Mobile Phone App. JMIR mHealth and uHealth 2020;8(5):e17034 View
  2. Magee J, Adut S, Brazill K, Warnick S. Mobile App Tools for Identifying and Managing Mental Health Disorders in Primary Care. Current Treatment Options in Psychiatry 2018;5(3):345 View
  3. khan Z, Alotaibi S. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. Journal of Healthcare Engineering 2020;2020:1 View
  4. Dhainaut J, Huot L, Bouchara Pomar V, Dubray C, Augé P, Barthélémy P, Belghiti J, Bureau S, Cassagnes J, Deblois S, Di Palma M, Dorsay G, Duchossoy L, Durand-Salmon F, Escudier T, Fiorini M, Franc S, Gelpi O, Laporte S, Lavallée E, Lethiec F, Meunier J, Peyret O, Samalin L, Vicaut E, de Saint-Exupéry E, Bouley A. Utilisation des objets connectés en recherche clinique. Therapies 2018;73(1):41 View
  5. Faurholt-Jepsen M, Bauer M, Kessing L. Smartphone-based objective monitoring in bipolar disorder: status and considerations. International Journal of Bipolar Disorders 2018;6(1) View
  6. Yang Y, Ryu G, Choi M. Methodological Strategies for Ecological Momentary Assessment to Evaluate Mood and Stress in Adult Patients Using Mobile Phones: Systematic Review. JMIR mHealth and uHealth 2019;7(4):e11215 View
  7. Van Meter A, Birnbaum M, Rizvi A, Kane J. Online help-seeking prior to diagnosis: Can web-based resources reduce the duration of untreated mood disorders in young people?. Journal of Affective Disorders 2019;252:130 View
  8. Rocha N, Rodrigues dos Santos M, Cerqueira M, Queirós A. Mobile Health to Support Ageing in Place. International Journal of E-Health and Medical Communications 2019;10(3):1 View
  9. 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
  10. Tønning M, Kessing L, Bardram J, Faurholt-Jepsen M. Methodological Challenges in Randomized Controlled Trials on Smartphone-Based Treatment in Psychiatry: Systematic Review. Journal of Medical Internet Research 2019;21(10):e15362 View
  11. Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, Folarin A, Haro J, Kerz M, Lamers F, Li Q, Manyakov N, Mohr D, Myin-Germeys I, Narayan V, BWJH P, Ranjan Y, Rashid Z, Rintala A, Siddi S, Simblett S, Wykes T, Hotopf M. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry 2019;19(1) View
  12. Tazawa Y, Wada M, Mitsukura Y, Takamiya A, Kitazawa M, Yoshimura M, Mimura M, Kishimoto T. Actigraphy for evaluation of mood disorders: A systematic review and meta-analysis. Journal of Affective Disorders 2019;253:257 View
  13. de la Cámara C, Lobo A. The assessment of self-esteem: a psychiatric perspective. International Psychogeriatrics 2020;32(2):161 View
  14. 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
  15. Scherr S, Goering M. Is a Self-Monitoring App for Depression a Good Place for Additional Mental Health Information? Ecological Momentary Assessment of Mental Help Information Seeking among Smartphone Users. Health Communication 2020;35(8):1004 View
  16. Lewczuk K, Gorowska M, Li Y, Gola M. Mobile Internet Technologies, Ecological Momentary Assessment, and Intervention—Poison and Remedy for New Online Problematic Behaviors in ICD-11. Frontiers in Psychiatry 2020;11 View
  17. Yang Y, Ryu G, Delespaul P, Choi M. Psychometric Properties of the Korean Version of the PsyMate Scale Using a Smartphone App: Ecological Momentary Assessment Study. JMIR mHealth and uHealth 2020;8(7):e17926 View
  18. Berrouiguet S, Le Moal V, Guillodo É, Le Floch A, Lenca P, Billot R, Walter M. Prévention du suicide et santé connectée. médecine/sciences 2018;34(8-9):730 View
  19. Leonard N, Casarjian B, Fletcher R, Prata C, Sherpa D, Kelemen A, Rajan S, Salaam R, Cleland C, Gwadz M. Theoretically-Based Emotion Regulation Strategies Using a Mobile App and Wearable Sensor Among Homeless Adolescent Mothers: Acceptability and Feasibility Study. JMIR Pediatrics and Parenting 2018;1(1):e1 View
  20. 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
  21. Jungmann S, Klan T, Kuhn S, Jungmann F. Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users. JMIR Formative Research 2019;3(4):e13863 View
  22. Kim J, Nguyen T, Gipson S, Shin A, Torous J. Smartphone Apps for Autism Spectrum Disorder—Understanding the Evidence. Journal of Technology in Behavioral Science 2018;3(1):1 View
  23. Berrouiguet S, Barrigón M, Castroman J, Courtet P, Artés-Rodríguez A, Baca-García E. Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol. BMC Psychiatry 2019;19(1) View
  24. Baldofski S, Kohls E, Bauer S, Becker K, Bilic S, Eschenbeck H, Kaess M, Moessner M, Salize H, Diestelkamp S, Voß E, Rummel-Kluge C. Efficacy and cost-effectiveness of two online interventions for children and adolescents at risk for depression (E.motion trial): study protocol for a randomized controlled trial within the ProHEAD consortium. Trials 2019;20(1) View
  25. Bakker D, Rickard N. Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission. Australian Psychologist 2019;54(4):245 View
  26. Fernández-Sotos P, Fernández-Caballero A, González P, Aparicio A, Martínez-Gras I, Torio I, Dompablo M, García-Fernández L, Santos J, Rodriguez-Jimenez R. Digital Technology for Internet Access by Patients With Early-Stage Schizophrenia in Spain: Multicenter Research Study. Journal of Medical Internet Research 2019;21(4):e11824 View
  27. Yin H, Wardenaar K, Wang Y, Wang N, Chen W, Zhang Y, Xu G, Schoevers R. Mobile Mental Health Apps in China: Systematic App Store Search. Journal of Medical Internet Research 2020;22(7):e14915 View
  28. Ernst J, Broemer L. Digital unterstützte Modelle in der Patientenbeobachtung. Forum 2018;33(3):190 View
  29. Rohani D, Faurholt-Jepsen M, Kessing L, Bardram J. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 2018;6(8):e165 View
  30. Sequeira L, Battaglia M, Perrotta S, Merikangas K, Strauss J. Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression. Journal of the American Academy of Child & Adolescent Psychiatry 2019;58(9):841 View
  31. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020 View
  32. Jacobson N, Summers B, Wilhelm S. Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors. Journal of Medical Internet Research 2020;22(5):e16875 View
  33. 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
  34. Briffault X, Morgiève M, Courtet P. From e-Health to i-Health: Prospective Reflexions on the Use of Intelligent Systems in Mental Health Care. Brain Sciences 2018;8(6):98 View
  35. Gründahl M, Deckert J, Hein G. Three Questions to Consider Before Applying Ecological Momentary Interventions (EMI) in Psychiatry. Frontiers in Psychiatry 2020;11 View
  36. 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
  37. Jawad I, Watson S, Haddad P, Talbot P, McAllister-Williams R. Medication nonadherence in bipolar disorder: a narrative review. Therapeutic Advances in Psychopharmacology 2018;8(12):349 View
  38. Sequeira L, Perrotta S, LaGrassa J, Merikangas K, Kreindler D, Kundur D, Courtney D, Szatmari P, Battaglia M, Strauss J. Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: A scoping review. Journal of Affective Disorders 2020;265:314 View
  39. Torous J, Larsen M, Depp C, Cosco T, Barnett I, Nock M, Firth J. Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps. Current Psychiatry Reports 2018;20(7) View
  40. Hidalgo-Mazzei D, Llach C, Vieta E. mHealth in affective disorders: hype or hope? A focused narrative review. International Clinical Psychopharmacology 2020;35(2):61 View
  41. Dhainaut J, Huot L, Pomar V, Dubray C, Augé P, Barthélémy P, Belghiti J, Bureau S, Cassagnes J, Deblois S, Di Palma M, Dorsay G, Duchossoy L, Durand-Salmon F, Escudier T, Fiorini M, Franc S, Gelpi O, Laporte S, Lavallée E, Lethiec F, Meunier J, Peyret O, Samalin L, Vicaut E, de Saint-Exupéry E, Bouley A. Using connected objects in clinical research. Therapies 2018;73(1):53 View
  42. Bardram J, Matic A. A Decade of Ubiquitous Computing Research in Mental Health. IEEE Pervasive Computing 2020;19(1):62 View
  43. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  44. Schmand B. Why are neuropsychologists so reluctant to embrace modern assessment techniques?. The Clinical Neuropsychologist 2019;33(2):209 View
  45. Hartmann R, Sander C, Lorenz N, Böttger D, Hegerl U. Utilization of Patient-Generated Data Collected Through Mobile Devices: Insights From a Survey on Attitudes Toward Mobile Self-Monitoring and Self-Management Apps for Depression. JMIR Mental Health 2019;6(4):e11671 View
  46. Antosik-Wójcinska A, Chojnacka M, Dominiak M, Święcicki Ł. The use of smartphones in the management of bipolar disorder- mobile apps and voice analysis in monitoring of mental state and phase change detection. European Neuropsychopharmacology 2019;29:S528 View
  47. 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
  48. 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
  49. Patoz M, Hidalgo-Mazzei D, Blanc O, Verdolini N, Pacchiarotti I, Murru A, Zukerwar L, Vieta E, Llorca P, Samalin L. Patient and physician perspectives of a smartphone application for depression: a qualitative study. BMC Psychiatry 2021;21(1) View
  50. Miley E, Schaeffler F, Beck J, Eichner M, Jannetts S. Secure account-based data capture with smartphones – preliminary results from a study of articulatory precision in clinical depression. Linguistics Vanguard 2021;7(s1) View
  51. Rabin J, Davidson B, Giacobbe P, Hamani C, Cohn M, Illes J, Lipsman N. Neuromodulation for major depressive disorder: innovative measures to capture efficacy and outcomes. The Lancet Psychiatry 2020;7(12):1075 View
  52. Morton E, Torous J, Murray G, Michalak E. Using apps for bipolar disorder – An online survey of healthcare provider perspectives and practices. Journal of Psychiatric Research 2021;137:22 View
  53. Maatoug R, Peiffer-Smadja N, Delval G, Brochu T, Pitrat B, Millet B. Ecological Momentary Assessment Using Smartphones in Patients With Depression: Feasibility Study. JMIR Formative Research 2021;5(2):e14179 View
  54. 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
  55. Izumi K, Minato K, Shiga K, Sugio T, Hanashiro S, Cortright K, Kudo S, Fujita T, Sado M, Maeno T, Takebayashi T, Mimura M, Kishimoto T. Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design. Frontiers in Psychiatry 2021;12 View
  56. 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
  57. Müller L, De Rooy D. Digital biomarkers for the prediction of mental health in aviation personnel. BMJ Health & Care Informatics 2021;28(1):e100335 View
  58. Tonti S, Marzolini B, Bulgheroni M. Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study. JMIR Biomedical Engineering 2021;6(2):e15417 View
  59. Emden D, Goltermann J, Dannlowski U, Hahn T, Opel N. Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptoms. Journal of Affective Disorders 2021;294:652 View
  60. Scotti Requena S, Sterling M, Elphinston R, Ritchie C, Robins S, R Armfield N. Development and use of mobile messaging for individuals with musculoskeletal pain conditions: a scoping review protocol. BMJ Open 2021;11(7):e048964 View
  61. 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
  62. Flanagan O, Chan A, Roop P, Sundram F. Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review. JMIR mHealth and uHealth 2021;9(9):e24352 View
  63. Folkersma W, Veerman V, Ornée D, Oldehinkel A, Alma M, Bastiaansen J. Patients' experience of an ecological momentary intervention involving self-monitoring and personalized feedback for depression. Internet Interventions 2021;26:100436 View
  64. Orsolini L, Pompili S, Salvi V, Volpe U. A Systematic Review on TeleMental Health in Youth Mental Health: Focus on Anxiety, Depression and Obsessive-Compulsive Disorder. Medicina 2021;57(8):793 View
  65. Rashid M, Askari M, Chen C, Liang Y, Shu K, Cinar A. Artificial Intelligence Algorithms for Treatment of Diabetes. Algorithms 2022;15(9):299 View
  66. Torous J, Bucci S, Bell I, Kessing L, Faurholt‐Jepsen M, Whelan P, Carvalho A, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021;20(3):318 View
  67. 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
  68. 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
  69. Wang Y, Lyu H, Tian X, Lang B, Wang X, St Clair D, Wu R, Zhao J. The similar eye movement dysfunction between major depressive disorder, bipolar depression and bipolar mania. The World Journal of Biological Psychiatry 2022;23(9):689 View
  70. Eisner E, Berry N, Morris R, Emsley R, Haddock G, Machin M, Hassan L, Bucci S. Exploring engagement with the CBT-informed Actissist smartphone application for early psychosis. Journal of Mental Health 2023;32(3):643 View
  71. Eagle T, Mehrotra A, Sharma A, Zuniga A, Whittaker S. "Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  72. 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
  73. White K, Williamson C, Bergou N, Oetzmann C, de Angel V, Matcham F, Henderson C, Hotopf M. A systematic review of engagement reporting in remote measurement studies for health symptom tracking. npj Digital Medicine 2022;5(1) View
  74. Singh M, Malmon A, Horne L, Felten O. Addressing burgeoning unmet needs in college mental health. Journal of American College Health 2022:1 View
  75. Dhinagaran D, Martinengo L, Ho M, Joty S, Kowatsch T, Atun R, Tudor Car L. Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework. JMIR mHealth and uHealth 2022;10(10):e38740 View
  76. Chiauzzi E, Wicks P. Beyond the Therapist’s Office: Merging Measurement-Based Care and Digital Medicine in the Real World. Digital Biomarkers 2021;5(2):176 View
  77. Splinter B, Saadah N, Chavannes N, Kiefte-de Jong J, Aardoom J. Optimizing the Acceptability, Adherence, and Inclusiveness of the COVID Radar Surveillance App: Qualitative Study Using Focus Groups, Thematic Content Analysis, and Usability Testing. JMIR Formative Research 2022;6(9):e36003 View
  78. Balcombe L, De Leo D. Human-Computer Interaction in Digital Mental Health. Informatics 2022;9(1):14 View
  79. Neumayr C, Voderholzer U, Schlegl S. Psych-APP-Therapie: Smartphonebasierte Interventionen in der Psychotherapie – Eine systematische Übersichtsarbeit. Verhaltenstherapie 2021;31(3):182 View
  80. Rifkin-Zybutz R, Turner N, Derges J, Bould H, Sedgewick F, Gooberman-Hill R, Linton M, Moran P, Biddle L. Digital Technology Use and Mental Health Consultations: Survey of the Views and Experiences of Clinicians and Young People. JMIR Mental Health 2023;10:e44064 View
  81. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  82. 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
  83. Tseng Y, Lin E, Wu C, Huang H, Chen P. Associations among smartphone app-based measurements of mood, sleep and activity in bipolar disorder. Psychiatry Research 2022;310:114425 View
  84. Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interventions 2022;28:100518 View
  85. Badesha K, Wilde S, Dawson D. Mental health mobile app use to manage psychological difficulties: an umbrella review. Mental Health Review Journal 2022;27(3):241 View
  86. Highland D, Zhou G. A review of detection techniques for depression and bipolar disorder. Smart Health 2022;24:100282 View
  87. Langener A, Stulp G, Kas M, Bringmann L. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Mental Health 2023;10:e42646 View
  88. de Angel V, Lewis S, White K, Matcham F, Hotopf M. Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians. JMIR Mental Health 2022;9(8):e38934 View
  89. 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
  90. Senaratne H, Oviatt S, Ellis K, Melvin G. A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment. ACM Transactions on Computing for Healthcare 2022;3(4):1 View
  91. MOUKADDAM N, TRUONG A, CAO J, SHAH A, SABHARWAL A. Findings From a Trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) Application: What Do Apps Really Tell Us About Patients with Depression? Concordance Between App-Generated Data and Standard Psychiatric Questionnaires for Depression and Anxiety. Journal of Psychiatric Practice 2019;25(5):365 View
  92. Jacobson N, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013 View
  93. 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
  94. Arora V, Sahoo J, Gupta M, Joshi A. Aiding clinical decision-making at the individual and community level using mobile sensor data – A study protocol for an experimental design (Preprint). JMIR Research Protocols 2022 View
  95. Khozouie N, Malekhoseini R. Pregnancy healthcare monitoring system: A review. Smart Health 2024;31:100433 View
  96. Punturieri C, Duncan W, Greenstein D, Shandler G, Zarate C, Evans J. An exploration of actigraphy in the context of ketamine and treatment‐resistant depression. International Journal of Methods in Psychiatric Research 2024;33(1) View
  97. 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
  98. Yao X, Ma S, Wu Y, Li D. So said, so done? The role of commitment in activity-based check-in discontinuance on APPs. Technological Forecasting and Social Change 2023;194:122675 View
  99. Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Formative Research 2023;7:e47380 View
  100. Maccaro A, Pagliara S, Zarro M, Piaggio D, Abdulsalami F, Su W, Haleem M, Pecchia L. Ethics and biomedical engineering for well-being: a cocreation study of remote services for monitoring and support. Scientific Reports 2023;13(1) View
  101. Nadal C, Earley C, Enrique A, Sas C, Richards D, Doherty G. Patient Acceptance of Self-Monitoring on a Smartwatch in a Routine Digital Therapy: A Mixed-Methods Study. ACM Transactions on Computer-Human Interaction 2024;31(1):1 View
  102. 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
  103. Wang B, Li M, Haihambo N, Qiu Z, Sun M, Guo M, Zhao X, Han C. Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB). Journal of Affective Disorders 2024;355:254 View
  104. Lee J, Kim M, Hwang S, Lee K, Park J, Shin T, Lim H, Urtnasan E, Chung M, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024;14(6):e073290 View
  105. Shin J, Bae S. Use of voice features from smartphones for monitoring depressive disorders: Scoping review. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Hegerl U, Dogan E, Oehler C, Sander C, Stöber F. Gesundheit digital. View
  2. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  3. Spedding M, Chibanda D. Global Mental Health and Psychotherapy. View
  4. Fernandez-Álvarez J, Díaz-García A, Colombo D, Botella C, Cipresso P, Riva G. Comprehensive Clinical Psychology. View
  5. Rozgonjuk D, Elhai J, Hall B. Digital Phenotyping and Mobile Sensing. View
  6. Briffault X. Traité de bioéthique. View
  7. Kolenik T. Integrating Artificial Intelligence and IoT for Advanced Health Informatics. View
  8. Anmella G, Hidalgo-Mazzei D, Vieta E. Digital Mental Health. View
  9. Sammouri W, Hamdan A, Sataloff R, Hawkshaw M. Traits of Civilization and Voice Disorders. View
  10. Rocha N, Rodrigues dos Santos M, Cerqueira M, Queirós A. Research Anthology on Supporting Healthy Aging in a Digital Society. View