Published on 29.03.16 in Vol 18, No 3 (2016): March
Works citing "Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study"
According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.5505):
(note that this is only a small subset of citations)
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Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker SA, McInnis M, Ajilore O, Nelson PC, 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
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Meinlschmidt G, Lee J, Stalujanis E, Belardi A, Oh M, Jung EK, 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
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DeMasi O, Kording K, Recht B, Jan Y. Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE 2017;12(9):e0184604
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Zhang Y, Olenick J, Chang C, Kozlowski SWJ, Hung H. TeamSense. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(3):1
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Mohr DC, Zhang M, Schueller SM. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annual Review of Clinical Psychology 2017;13(1):23
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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
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Doherty K, Balaskas A, Doherty G. The Design of Ecological Momentary Assessment Technologies. Interacting with Computers 2020;32(3):257
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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
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Miller LC, Jeong DC, Wang L, Shaikh SJ, Gillig TK, Godoy CG, Appleby PR, Corsbie-Massay CL, Marsella S, Christensen JL, Read SJ. Systematic Representative Design: A Reply to Commentaries. Psychological Inquiry 2019;30(4):250
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Yim SJ, Lui LM, Lee Y, Rosenblat JD, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho RC, Coles AS, Majeed A, Wong ER, Phan L, Nasri F, McIntyre RS. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602
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Cha J, Voigt-Antons J, Trahms C, O’Sullivan JL, 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)
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Livingston NA, Shingleton R, Heilman ME, 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
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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
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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 JA, 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
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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
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Torous J, Gershon A, Hays R, Onnela J, Baker JT. Digital Phenotyping for the Busy Psychiatrist: Clinical Implications and Relevance. Psychiatric Annals 2019;49(5):196
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Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120
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Barrigón ML, Baca-García E. Current challenges in research on suicide. Revista de Psiquiatría y Salud Mental (English Edition) 2018;11(1):1
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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
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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
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Mulvaney SA, Vaala S, Hood KK, Lybarger C, Carroll R, Williams L, Schmidt DC, Johnson K, Dietrich MS, 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
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Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman MA, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751
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Berrouiguet S, Ramírez D, Barrigón ML, 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
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Ryding FC, Kuss DJ. Passive objective measures in the assessment of problematic smartphone use: A systematic review. Addictive Behaviors Reports 2020;11:100257
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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
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Brietzke E, Hawken ER, Idzikowski M, Pong J, Kennedy SH, Soares CN. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neuroscience & Biobehavioral Reviews 2019;104:223
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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
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Sened H, Lazarus G, Gleason ME, Rafaeli E, Fleeson W, Mõttus R. The Use of Intensive Longitudinal Methods in Explanatory Personality Research. European Journal of Personality 2018;32(3):269
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khan ZF, Alotaibi SR. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. Journal of Healthcare Engineering 2020;2020:1
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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
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Attwood S, Parke H, Larsen J, Morton KL. 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)
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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
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May M, Junghaenel DU, Ono M, Stone AA, Schneider S. Ecological Momentary Assessment Methodology in Chronic Pain Research: A Systematic Review. The Journal of Pain 2018;19(7):699
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Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. 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
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Rashid H, Mendu S, Daniel KE, Beltzer ML, Teachman BA, Boukhechba M, Barnes LE. 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
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Gao Y, Li A, Zhu T, Liu X, Liu X. How smartphone usage correlates with social anxiety and loneliness. PeerJ 2016;4:e2197
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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
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Meinlschmidt G, Tegethoff M, Belardi A, Stalujanis E, Oh M, Jung EK, 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
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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
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Bhattacharya K, Kaski K. Social physics: uncovering human behaviour from communication. Advances in Physics: X 2019;4(1):1527723
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Bader CS, Skurla M, Vahia IV. Technology in the Assessment, Treatment, and Management of Depression. Harvard Review of Psychiatry 2020;28(1):60
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Saeb S, Lattie EG, Kording KP, Mohr DC. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112
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Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 2016;4:e2537
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Hallgren KA, Bauer AM, Atkins DC. Digital technology and clinical decision making in depression treatment: Current findings and future opportunities. Depression and Anxiety 2017;34(6):494
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Berrouiguet S, Barrigón ML, Castroman JL, 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)
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Majumder S, Deen MJ. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164
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Porras-Segovia A, Molina-Madueño RM, Berrouiguet S, López-Castroman J, Barrigón ML, Pérez-Rodríguez MS, Marco JH, 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
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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
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. Using Big Data to study subjective well-being. Current Opinion in Behavioral Sciences 2017;18:28
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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
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Bertz JW, Epstein DH, Preston KL. Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research. Addictive Behaviors 2018;83:5
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Boukhechba M, Daros AR, Fua K, Chow PI, Teachman BA, Barnes LE. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 2018;9-10:192
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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
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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
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Kruger DJ, Duan A, Juhasz D, Phaneuf CV, Sreenivasa V, Saunders CM, Heyblom AM, Sonnega PA, Day ML, Misevich SL. Cell Phone Use Latency in a Midwestern USA University Population. Journal of Technology in Behavioral Science 2017;2(1):56
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Barrigón ML, Baca-García E. Retos actuales en la investigación en suicidio. Revista de Psiquiatría y Salud Mental 2018;11(1):1
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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
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Williams MT, 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
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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
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Fernandes A, Van Lenthe FJ, Vallée J, Sueur C, Chaix B. Linking physical and social environments with mental health in old age: a
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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
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Kumar D, Jeuris S, Bardram JE, Dragoni N. Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications. ACM Transactions on Computing for Healthcare 2021;2(1):1
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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
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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
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Peis I, López-Moríñigo J, Pérez-Rodríguez MM, 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)
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de Vries LP, Baselmans BML, 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
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Poudyal A, van Heerden A, Hagaman A, Islam C, Thapa A, Maharjan SM, Byanjankar P, Kohrt BA. 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
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Stewart MT, Nezich T, Lee JM, Hasson RE, 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
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Sedano-Capdevila A, Porras-Segovia A, Bello HJ, Baca-García E, Barrigon ML. Use of Ecological Momentary Assessment to Study Suicidal Thoughts and Behavior: a Systematic Review. Current Psychiatry Reports 2021;23(7)
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Woolf TB, Goheer A, Holzhauer K, Martinez J, Coughlin JW, Martin L, Zhao D, Song S, Ahmad Y, Sokolinskyi K, Remayeva T, Clark JM, 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
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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
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Currey D, Torous J. Digital phenotyping correlations in larger mental health samples: analysis and replication. BJPsych Open 2022;8(4)
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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
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Langener AM, Stulp G, Kas MJ, Bringmann LF. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Mental Health 2023;10:e42646
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Porras-Segovia A, Díaz-Oliván I, Barrigón ML, Moreno M, Artés-Rodríguez A, Pérez-Rodríguez MM, 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
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Hart A, Reis D, Prestele E, Jacobson NC. 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
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Zhang P, Fonnesbeck C, Schmidt DC, White J, Kleinberg S, Mulvaney SA. 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
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Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes LE, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413
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Schulz PJ, Andersson EM, 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
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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
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Boesen VB, Christoffersen T, Watt T, Borresen SW, 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
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van 't Klooster JJR, Rabago Mayer LM, Klaassen B, Kelders SM. Challenges and opportunities in mobile e-coaching. Frontiers in Digital Health 2024;5
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