Published on in Vol 18, No 3 (2016): March
Journals
- 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
- 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
- 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
- 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
- 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
- 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
- Doherty K, Balaskas A, Doherty G. The Design of Ecological Momentary Assessment Technologies. Interacting with Computers 2020;32(3):257 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
- Barrigón M, Baca-García E. Current challenges in research on suicide. Revista de Psiquiatría y Salud Mental (English Edition) 2018;11(1):1 View
- 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
- 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
- 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
- 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
- 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
- Ryding F, Kuss D. Passive objective measures in the assessment of problematic smartphone use: A systematic review. Addictive Behaviors Reports 2020;11:100257 View
- 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
- 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
- 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
- Sened H, Lazarus G, Gleason M, Rafaeli E, Fleeson W. The Use of Intensive Longitudinal Methods in Explanatory Personality Research. European Journal of Personality 2018;32(3):269 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Gao Y, Li A, Zhu T, Liu X, Liu X. How smartphone usage correlates with social anxiety and loneliness. PeerJ 2016;4:e2197 View
- 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
- 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
- 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
- Bhattacharya K, Kaski K. Social physics: uncovering human behaviour from communication. Advances in Physics: X 2019;4(1):1527723 View
- Bader C, Skurla M, Vahia I. Technology in the Assessment, Treatment, and Management of Depression. Harvard Review of Psychiatry 2020;28(1):60 View
- 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
- 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
- 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
- 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
- Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
- 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
- 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
- Luhmann M. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28 View
- 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
- 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
- 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
- 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
- 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
- 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
- Barrigón M, Baca-García E. Retos actuales en la investigación en suicidio. Revista de Psiquiatría y Salud Mental 2018;11(1):1 View
- 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
- 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
- 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
- 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
- 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
- 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
- . Correction. Journal of the American Academy of Child & Adolescent Psychiatry 2020;59(12):1408 View
- 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
- 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
- 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
- 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
- Krichen M. Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sensors Journal 2021;21(6):7207 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4) View
- 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
- Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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;51(4):455 View
- Torous J, Haim A. Dichotomies in the Development and Implementation of Digital Mental Health Tools. Psychiatric Services 2018;69(12):1204 View
- 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
- van 't Klooster J, Rabago Mayer L, Klaassen B, Kelders S. Challenges and opportunities in mobile e-coaching. Frontiers in Digital Health 2024;5 View
- 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
- 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
- Bilal A, Pagoni K, Iliadis S, Papadopoulos F, Skalkidou A, Öster C. Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study. JMIR Formative Research 2024;8:e53508 View
- de Beurs D, Giltay E, Nuij C, O’Connor R, de Winter R, Kerkhof A, van Ballegooijen W, Riper H. Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms. Behaviour Research and Therapy 2024;178:104572 View
- Burns J, Chen K, Flathers M, Currey D, Macrynikola N, Vaidyam A, Langholm C, Barnett I, Byun A, Lane E, Torous J. Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial. Journal of Medical Internet Research 2024;26:e58502 View
- Bidargaddi N, Leibbrandt R, Paget T, Verjans J, Looi J, Lipschitz J. Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research. DIGITAL HEALTH 2024;10 View
- Allen K, Rodriguez S, Hayani L, Rothenberger S, Moses-Kolko E, Simhan H, Krishnamurti T. Digital phenotyping of depression during pregnancy using self-report data. Journal of Affective Disorders 2024;364:231 View
- Slade C, Benzo R, Washington P. Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55694 View
- Georgel L, Benyoussef A, Berrouiguet S, Guellec D, Carvajal Alegria G, Marhadour T, Jousse-Joulin S, Cochener-Lamard B, Labetoulle M, Gottenberg J, Bourcier T, Nocturne G, Saraux A, Mariette X, Consigny M, Gravey M, Devauchelle-Pensec V, Seror R, Cornec D. Development of a web-based ecological momentary assessment tool to measure day-to-day variability of the symptoms in patients with Sjögren’s disease. RMD Open 2024;10(4):e004526 View
- Hu J, Li C, Ge Y, Yang J, Zhu S, He C. Mapping the Evolution of Digital Health Research: Bibliometric Overview of Research Hotspots, Trends, and Collaboration of Publications in JMIR (1999-2024). Journal of Medical Internet Research 2024;26:e58987 View
- Al-akshar S, Tolulope Ibrahim S, Katapally T, Hochheiser H. How can digital citizen science approaches improve ethical smartphone use surveillance among youth: Traditional surveys versus ecological momentary assessments. PLOS Digital Health 2024;3(11):e0000448 View
- Bosma C, Wojcik C, Haigh E. Evaluating Individual Differences in Emotion Regulation in Response to Sadness Using Digital Phenotyping. Journal of Technology in Behavioral Science 2024 View
- Patel J, Hung C, Katapally T. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review. Psychiatry Research 2025;343:116277 View
- Bouchacourt L, Smith S, Mackert M, Almalki S, Awad G, Barczyk A, Bearman S, Castelli D, Champagne F, de Barbaro K, Garcia S, Johnson K, Kinney K, Lawson K, Nagy Z, Quiñones Camacho L, Rodríguez L, Schnyer D, Thomaz E, Upshaw S, Zhang Y. Strategies to Implement a Community-Based, Longitudinal Cohort Study: The Whole Communities-Whole Health Case Study. JMIR Formative Research 2024;8:e60368 View
Books/Policy Documents
- Schneider F, Reich S, Reinecke L. Permanently Online, Permanently Connected. View
- Becker D. Advances in Information and Communication Networks. View
- Provoost S, Ruwaard J, Neijenhuijs K, Bosse T, Riper H. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. View
- Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
- Khazaal Y. Traité de Réhabilitation Psychosociale. View
- Torous J, Namiri N, Keshavan M. Personalized Psychiatry. View
- Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
- Depp C, Kaufmann C, Granholm E, Thompson W. Experience Sampling in Mental Health Research. View
- Hussain F, Stange J, Langenecker S, McInnis M, Zulueta J, Piscitello A, Cao B, Huang H, Yu P, Nelson P, Ajilore O, Leow A. Digital Phenotyping and Mobile Sensing. View
- Tushar A, Kabir M, Ahmed S. Signal Processing Techniques for Computational Health Informatics. View
- Yu H, Itoh A, Sakamoto R, Shimaoka M, Sano A. Wireless Mobile Communication and Healthcare. View
- Rebolledo M, Eiben A, Bartz-Beielstein T. Applications of Evolutionary Computation. View
- Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
- Beierle F. Integrating Psychoinformatics with Ubiquitous Social Networking. View
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