Published on in Vol 18, No 3 (2016): March

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study

Journals

  1. Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker S, McInnis M, Ajilore O, Nelson P, Ryan K, Leow A. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research 2018;20(7):e241 View
  2. Meinlschmidt G, Lee J, Stalujanis E, Belardi A, Oh M, Jung E, Kim H, Alfano J, Yoo S, Tegethoff M. Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting. Frontiers in Psychology 2016;7 View
  3. DeMasi O, Kording K, Recht B, Jan Y. Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE 2017;12(9):e0184604 View
  4. Zhang Y, Olenick J, Chang C, Kozlowski S, Hung H. TeamSense. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(3):1 View
  5. Mohr D, Zhang M, Schueller S. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology 2017;13(1):23 View
  6. 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
  7. Doherty K, Balaskas A, Doherty G. The Design of Ecological Momentary Assessment Technologies. Interacting with Computers 2020;32(3):257 View
  8. Simor P, Báthori N, Nagy T, Polner B. Poor sleep quality predicts psychotic‐like symptoms: an experience sampling study in young adults with schizotypal traits. Acta Psychiatrica Scandinavica 2019;140(2):135 View
  9. Miller L, Jeong D, Wang L, Shaikh S, Gillig T, Godoy C, Appleby P, Corsbie-Massay C, Marsella S, Christensen J, Read S. Systematic Representative Design: A Reply to Commentaries. Psychological Inquiry 2019;30(4):250 View
  10. Yim S, Lui L, Lee Y, Rosenblat J, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho R, Coles A, Majeed A, Wong E, Phan L, Nasri F, McIntyre R. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602 View
  11. Cha J, Voigt-Antons J, Trahms C, O’Sullivan J, Gellert P, Kuhlmey A, Möller S, Nordheim J. Finding critical features for predicting quality of life in tablet-based serious games for dementia. Quality and User Experience 2019;4(1) View
  12. Livingston N, Shingleton R, Heilman M, Brief D. Self-help Smartphone Applications for Alcohol Use, PTSD, Anxiety, and Depression: Addressing the New Research-Practice Gap. Journal of Technology in Behavioral Science 2019;4(2):139 View
  13. Di Matteo D, Fine A, Fotinos K, Rose J, Katzman M. Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire. JMIR Mental Health 2018;5(3):e56 View
  14. 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
  15. Sultana M, Al-Jefri M, Lee J. Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study. JMIR mHealth and uHealth 2020;8(9):e17818 View
  16. 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
  17. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  18. Barrigón M, Baca-García E. Current challenges in research on suicide. Revista de Psiquiatría y Salud Mental (English Edition) 2018;11(1):1 View
  19. Day J, Freiberg K, Hayes A, Homel R. Towards Scalable, Integrative Assessment of Children’s Self-Regulatory Capabilities: New Applications of Digital Technology. Clinical Child and Family Psychology Review 2019;22(1):90 View
  20. 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
  21. Mulvaney S, Vaala S, Hood K, Lybarger C, Carroll R, Williams L, Schmidt D, Johnson K, Dietrich M, Laffel L. Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns. Diabetes Technology & Therapeutics 2018;20(7):465 View
  22. 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
  23. Berrouiguet S, Ramírez D, Barrigón M, Moreno-Muñoz P, Carmona Camacho R, Baca-García E, Artés-Rodríguez A. Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth 2018;6(12):e197 View
  24. Ryding F, Kuss D. Passive objective measures in the assessment of problematic smartphone use: A systematic review. Addictive Behaviors Reports 2020;11:100257 View
  25. DeMasi O, Feygin S, Dembo A, Aguilera A, Recht B. Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study. JMIR mHealth and uHealth 2017;5(10):e137 View
  26. Brietzke E, Hawken E, Idzikowski M, Pong J, Kennedy S, Soares C. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neuroscience & Biobehavioral Reviews 2019;104:223 View
  27. Bailon C, Damas M, Pomares H, Sanabria D, Perakakis P, Goicoechea C, Banos O. Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods. Sensors 2019;19(15):3430 View
  28. Sened H, Lazarus G, Gleason M, Rafaeli E, Fleeson W, Mõttus R. The Use of Intensive Longitudinal Methods in Explanatory Personality Research. European Journal of Personality 2018;32(3):269 View
  29. 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
  30. Foster S, O’Mealey M, Farmer C, Carvallo M. The impact of snapchat usage on drunkorexia behaviors in college women. Journal of American College Health 2022;70(3):864 View
  31. Attwood S, Parke H, Larsen J, Morton K. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health 2017;17(1) View
  32. Boettcher J, Magnusson K, Marklund A, Berglund E, Blomdahl R, Braun U, Delin L, Lundén C, Sjöblom K, Sommer D, von Weber K, Andersson G, Carlbring P. Adding a smartphone app to internet-based self-help for social anxiety: A randomized controlled trial. Computers in Human Behavior 2018;87:98 View
  33. May M, Junghaenel D, Ono M, Stone A, Schneider S. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. The Journal of Pain 2018;19(7):699 View
  34. 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
  35. Rashid H, Mendu S, Daniel K, Beltzer M, Teachman B, Boukhechba M, Barnes L. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1 View
  36. Gao Y, Li A, Zhu T, Liu X, Liu X. How smartphone usage correlates with social anxiety and loneliness. PeerJ 2016;4:e2197 View
  37. Becker D, van Breda W, Funk B, Hoogendoorn M, Ruwaard J, Riper H. Predictive modeling in e-mental health: A common language framework. Internet Interventions 2018;12:57 View
  38. Meinlschmidt G, Tegethoff M, Belardi A, Stalujanis E, Oh M, Jung E, Kim H, Yoo S, Lee J. Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning. Journal of Affective Disorders 2020;264:430 View
  39. Rickard N, Arjmand H, Bakker D, Seabrook E. Development of a Mobile Phone App to Support Self-Monitoring of Emotional Well-Being: A Mental Health Digital Innovation. JMIR Mental Health 2016;3(4):e49 View
  40. Bhattacharya K, Kaski K. Social physics: uncovering human behaviour from communication. Advances in Physics: X 2019;4(1):1527723 View
  41. Bader C, Skurla M, Vahia I. Technology in the Assessment, Treatment, and Management of Depression. Harvard Review of Psychiatry 2020;28(1):60 View
  42. Saeb S, Lattie E, Kording K, Mohr D. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112 View
  43. Saeb S, Lattie E, Schueller S, Kording K, Mohr D. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 2016;4:e2537 View
  44. Hallgren K, Bauer A, Atkins D. Digital technology and clinical decision making in depression treatment: Current findings and future opportunities. Depression and Anxiety 2017;34(6):494 View
  45. 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
  46. Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
  47. Porras-Segovia A, Molina-Madueño R, Berrouiguet S, López-Castroman J, Barrigón M, Pérez-Rodríguez M, Marco J, Díaz-Oliván I, de León S, Courtet P, Artés-Rodríguez A, Baca-García E. Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study. Journal of Affective Disorders 2020;274:733 View
  48. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild. JMIR mHealth and uHealth 2016;4(3):e111 View
  49. Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28 View
  50. Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and Anxiety 2017;34(6):526 View
  51. Bertz J, Epstein D, Preston K. Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addictive Behaviors 2018;83:5 View
  52. Boukhechba M, Daros A, Fua K, Chow P, Teachman B, Barnes L. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 2018;9-10:192 View
  53. van de Ven P, O’Brien H, Henriques R, Klein M, Msetfi R, Nelson J, Rocha A, Ruwaard J, O’Sullivan D, Riper H. ULTEMAT: A mobile framework for smart ecological momentary assessments and interventions. Internet Interventions 2017;9:74 View
  54. Mikus A, Hoogendoorn M, Rocha A, Gama J, Ruwaard J, Riper H. Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data. Internet Interventions 2018;12:105 View
  55. Kruger D, Duan A, Juhasz D, Phaneuf C, Sreenivasa V, Saunders C, Heyblom A, Sonnega P, Day M, Misevich S. Cell Phone Use Latency in a Midwestern USA University Population. Journal of Technology in Behavioral Science 2017;2(1):56 View
  56. Barrigón M, Baca-García E. Retos actuales en la investigación en suicidio. Revista de Psiquiatría y Salud Mental 2018;11(1):1 View
  57. 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
  58. Williams M, Lewthwaite H, Fraysse F, Gajewska A, Ignatavicius J, Ferrar K. Compliance With Mobile Ecological Momentary Assessment of Self-Reported Health-Related Behaviors and Psychological Constructs in Adults: Systematic Review and Meta-analysis. Journal of Medical Internet Research 2021;23(3):e17023 View
  59. Burchert S, Kerber A, Zimmermann J, Knaevelsrud C, Nater-Mewes R. Screening accuracy of a 14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: Comparison with the PHQ-9 depression screening. PLOS ONE 2021;16(1):e0244955 View
  60. Fernandes A, Van Lenthe F, Vallée J, Sueur C, Chaix B. Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment. Journal of Epidemiology and Community Health 2021;75(5):477 View
  61. Taeger J, Bischoff S, Hagen R, Rak K. Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study. JMIR mHealth and uHealth 2021;9(1):e19346 View
  62. Kumar D, Jeuris S, Bardram J, Dragoni N. Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications. ACM Transactions on Computing for Healthcare 2021;2(1):1 View
  63. . Correction. Journal of the American Academy of Child & Adolescent Psychiatry 2020;59(12):1408 View
  64. Bai R, Xiao L, Guo Y, Zhu X, Li N, Wang Y, Chen Q, Feng L, Wang Y, Yu X, Wang C, Hu Y, Liu Z, Xie H, Wang G. Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study. JMIR mHealth and uHealth 2021;9(3):e24365 View
  65. Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161 View
  66. 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
  67. de Vries L, Baselmans B, Bartels M. Smartphone-Based Ecological Momentary Assessment of Well-Being: A Systematic Review and Recommendations for Future Studies. Journal of Happiness Studies 2021;22(5):2361 View
  68. Krichen M. Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sensors Journal 2021;21(6):7207 View
  69. Rosenthal S, Zhou J, Booth S. Association between mobile phone screen time and depressive symptoms among college students: A threshold effect. Human Behavior and Emerging Technologies 2021;3(3):432 View
  70. Poudyal A, van Heerden A, Hagaman A, Islam C, Thapa A, Maharjan S, Byanjankar P, Kohrt B. What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment. Frontiers in Public Health 2021;9 View
  71. 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
  72. Porras-Segovia A, Cobo A, Díaz-Oliván I, Artés-Rodríguez A, Berrouiguet S, Lopez-Castroman J, Courtet P, Barrigón M, Oquendo M, Baca-García E. Disturbed sleep as a clinical marker of wish to die: A smartphone monitoring study over three months of observation. Journal of Affective Disorders 2021;286:330 View
  73. Buda T, Khwaja M, Matic A. Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
  74. Stewart M, Nezich T, Lee J, Hasson R, Colabianchi N. Using a Mobile Phone App to Analyze the Relationship Between Planned and Performed Physical Activity in University Students: Observational Study. JMIR mHealth and uHealth 2021;9(4):e17581 View
  75. Sedano-Capdevila A, Porras-Segovia A, Bello H, Baca-García E, Barrigon M. Use of Ecological Momentary Assessment to Study Suicidal Thoughts and Behavior: a Systematic Review. Current Psychiatry Reports 2021;23(7) View
  76. Woolf T, Goheer A, Holzhauer K, Martinez J, Coughlin J, Martin L, Zhao D, Song S, Ahmad Y, Sokolinskyi K, Remayeva T, Clark J, Bennett W, Lehmann H. Development of a Mobile App for Ecological Momentary Assessment of Circadian Data: Design Considerations and Usability Testing. JMIR Formative Research 2021;5(7):e26297 View
  77. Ma X, Yang X, Gao J, Xu C. Health Status Prediction with Local-Global Heterogeneous Behavior Graph. ACM Transactions on Multimedia Computing, Communications, and Applications 2021;17(4):1 View
  78. Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4) View
  79. Lee H, Park J, Lee U. A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones. ACM Computing Surveys 2023;55(5):1 View
  80. Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
  81. Williams J, Pykett J. Mental health monitoring apps for depression and anxiety in children and young people: A scoping review and critical ecological analysis. Social Science & Medicine 2022;297:114802 View
  82. 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
  83. 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
  84. Porras-Segovia A, Díaz-Oliván I, Barrigón M, Moreno M, Artés-Rodríguez A, Pérez-Rodríguez M, Baca-García E. Real-world feasibility and acceptability of real-time suicide risk monitoring via smartphones: A 6-month follow-up cohort. Journal of Psychiatric Research 2022;149:145 View
  85. Virginia Anikwe C, Friday Nweke H, Chukwu Ikegwu A, Adolphus Egwuonwu C, Uchenna Onu F, Rita Alo U, Wah Teh Y. Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications 2022;202:117362 View
  86. Hart A, Reis D, Prestele E, Jacobson N. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment. Journal of Medical Internet Research 2022;24(4):e34015 View
  87. Zhang P, Fonnesbeck C, Schmidt D, White J, Kleinberg S, Mulvaney S. Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study. JMIR mHealth and uHealth 2022;10(3):e21959 View
  88. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  89. Ferrás Sexto C, García Y. Los datos georreferenciados con teléfonos móviles para las terapias psicosociales. MEDICA REVIEW. International Medical Humanities Review / Revista Internacional de Humanidades Médicas 2019;7(2):83 View
  90. Burke L, Naylor G. Smartphone App–Based Noncontact Ecological Momentary Assessment With Experienced and Naïve Older Participants: Feasibility Study. JMIR Formative Research 2022;6(3):e27677 View
  91. Schulz P, Andersson E, Bizzotto N, Norberg M. Using Ecological Momentary Assessment to Study the Development of COVID-19 Worries in Sweden: Longitudinal Study. Journal of Medical Internet Research 2021;23(11):e26743 View
  92. Krohn H, Guintivano J, Frische R, Steed J, Rackers H, Meltzer-Brody S. App-Based Ecological Momentary Assessment to Enhance Clinical Care for Postpartum Depression: Pilot Acceptability Study. JMIR Formative Research 2022;6(3):e28081 View
  93. Boesen V, Christoffersen T, Watt T, Borresen S, Klose M, Feldt-Rasmussen U. PlenadrEMA: effect of dual-release versus conventional hydrocortisone on fatigue, measured by ecological momentary assessments: a study protocol for an open-label switch pilot study. BMJ Open 2018;8(1):e019487 View
  94. Woznowski‐Vu A, Martel M, Ahmed S, Sullivan M, Wideman T. Task‐based measures of sensitivity to physical activity predict daily life pain and mood among people living with back pain. European Journal of Pain 2023;27(6):735 View
  95. Varma D, Mualem M, Goodin A, Gurka K, Wen T, Gurka M, Roussos-Ross K. Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers. JMIR Formative Research 2023;7:e44500 View
  96. Knights J, Shen J, Mysliwiec V, DuBois H. Associations of smartphone usage patterns with sleep and mental health symptoms in a clinical cohort receiving virtual behavioral medicine care: a retrospective study. Sleep Advances 2023;4(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. El Dahr Y, Perquier F, Moloney M, Woo G, Dobrin-De Grace R, Carvalho D, Addario N, Cameron E, Roos L, Szatmari P, Aitken M. Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study. JMIR Formative Research 2023;7:e42916 View
  99. Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, Probst T, Langguth B, Pryss R. Predicting the presence of tinnitus using ecological momentary assessments. Scientific Reports 2023;13(1) View
  100. Coppens I, De Pessemier T, Martens L. Connecting physical activity with context and motivation: a user study to define variables to integrate into mobile health recommenders. User Modeling and User-Adapted Interaction 2024;34(1):147 View
  101. Langener A, Bringmann L, Kas M, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. Administration and Policy in Mental Health and Mental Health Services Research 2024 View
  102. Torous J, Haim A. Dichotomies in the Development and Implementation of Digital Mental Health Tools. Psychiatric Services 2018;69(12):1204 View
  103. Langener A, Stulp G, Jacobson N, Costanzo A, Jagesar R, Kas M, Bringmann L. It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data. Advances in Methods and Practices in Psychological Science 2024;7(1) View
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  105. Hagerman C, Onu M, Crane N, Butryn M, Forman E. Psychological and behavioral responses to daily weight gain during behavioral weight loss treatment. Journal of Behavioral Medicine 2024;47(3):492 View
  106. Marcano Belisario J, Doherty K, O'Donoghue J, Ramchandani P, Majeed A, Doherty G, Morrison C, Car J. A bespoke mobile application for the longitudinal assessment of depression and mood during pregnancy: protocol of a feasibility study. BMJ Open 2017;7(5):e014469 View

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