Published on in Vol 19, No 3 (2017): March
Journals
- Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
- Jacobson N, Chung Y. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572 View
- Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Predicting depressive symptoms using smartphone data. Smart Health 2020;15:100093 View
- Paolillo E, Tang B, Depp C, Rooney A, Vaida F, Kaufmann C, Mausbach B, Moore D, Moore R. Temporal Associations Between Social Activity and Mood, Fatigue, and Pain in Older Adults With HIV: An Ecological Momentary Assessment Study. JMIR Mental Health 2018;5(2):e38 View
- . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020 View
- Wang R, Wang W, daSilva A, Huckins J, Kelley W, Heatherton T, Campbell A. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1 View
- Ike K, de Boer S, Buwalda B, Kas M. Social withdrawal: An initially adaptive behavior that becomes maladaptive when expressed excessively. Neuroscience & Biobehavioral Reviews 2020;116:251 View
- Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Automatic depression prediction using Internet traffic characteristics on smartphones. Smart Health 2020;18:100137 View
- 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
- Calvo R, Dinakar K, Picard R, Christensen H, Torous J. Toward Impactful Collaborations on Computing and Mental Health. Journal of Medical Internet Research 2018;20(2):e49 View
- Friedmann F, Santangelo P, Ebner-Priemer U, Hill H, Neubauer A, Rausch S, Steil R, Müller-Engelmann M, Kleindienst N, Bohus M, Fydrich T, Priebe K, Matsumura K. Life within a limited radius: Investigating activity space in women with a history of child abuse using global positioning system tracking. PLOS ONE 2020;15(5):e0232666 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
- Chow P, Drago F, Kennedy E, Cohn W. A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study. JMIR Cancer 2020;6(1):e15750 View
- Agarwal R, Dugas M, Gao G, Kannan P. Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science 2020;48(1):9 View
- Dejonckheere E, Mestdagh M, Houben M, Rutten I, Sels L, Kuppens P, Tuerlinckx F. Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour 2019;3(5):478 View
- Cai L, Boukhechba M, Gerber M, Barnes L, Showalter S, Cohn W, Chow P. An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients. Smart Health 2020;15:100086 View
- Xu X, Chikersal P, Doryab A, Villalba D, Dutcher J, Tumminia M, Althoff T, Cohen S, Creswell K, Creswell J, Mankoff J, Dey A. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1 View
- 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
- Armstrong C, Ciulla R, Williams S, Micheel L. An Applied Test of Knowledge Translation Methods Using a Mobile Health Solution. Military Medicine 2020;185(Supplement_1):526 View
- Chow P, Showalter S, Gerber M, Kennedy E, Brenin D, Schroen A, Mohr D, Lattie E, Cohn W. Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study. JMIR Research Protocols 2019;8(1):e11452 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
- Chan S, Godwin H, Gonzalez A, Yellowlees P, Hilty D. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12) View
- Nicholas J, Shilton K, Schueller S, Gray E, Kwasny M, Mohr D. The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2019;7(4):e12578 View
- Montag C, Baumeister H, Kannen C, Sariyska R, Meßner E, Brand M. Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology. J 2019;2(2):102 View
- Peleh T, Ike K, Frentz I, Buwalda B, de Boer S, Hengerer B, Kas M. Cross-site Reproducibility of Social Deficits in Group-housed BTBR Mice Using Automated Longitudinal Behavioural Monitoring. Neuroscience 2020;445:95 View
- Harari G, Müller S, Aung M, Rentfrow P. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences 2017;18:83 View
- Watson R, Christensen J. Big data and student engagement among vulnerable youth: A review. Current Opinion in Behavioral Sciences 2017;18:23 View
- Ware S, Yue C, Morillo R, Lu J, Shang C, Kamath J, Bamis A, Bi J, Russell A, Wang B. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(4):1 View
- McQuoid J, Thrul J, Ling P. A geographically explicit ecological momentary assessment (GEMA) mixed method for understanding substance use. Social Science & Medicine 2018;202:89 View
- Sano A, Taylor S, McHill A, Phillips A, Barger L, Klerman E, Picard R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. Journal of Medical Internet Research 2018;20(6):e210 View
- Chan S, Li L, Torous J, Gratzer D, Yellowlees P. Review of Use of Asynchronous Technologies Incorporated in Mental Health Care. Current Psychiatry Reports 2018;20(10) View
- Poudyal A, van Heerden A, Hagaman A, Maharjan S, Byanjankar P, Subba P, Kohrt B. Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal. JMIR Research Protocols 2019;8(8):e14734 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
- Hur J, DeYoung K, Islam S, Anderson A, Barstead M, Shackman A. Social context and the real-world consequences of social anxiety. Psychological Medicine 2020;50(12):1989 View
- Weingarden H, Matic A, Calleja R, Greenberg J, Harrison O, Wilhelm S. Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial. JMIR mHealth and uHealth 2020;8(6):e16350 View
- Garcia-Ceja E, Riegler M, Nordgreen T, Jakobsen P, Oedegaard K, Tørresen J. Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing 2018;51:1 View
- de Moura I, Teles A, Endler M, Coutinho L, da Silva e Silva F. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. Sensors 2020;21(1):86 View
- Chikersal P, Doryab A, Tumminia M, Villalba D, Dutcher J, Liu X, Cohen S, Creswell K, Mankoff J, Creswell J, Goel M, Dey A. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing. ACM Transactions on Computer-Human Interaction 2021;28(1):1 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
- dos Santos Paula L, Barbosa J, Dias L. A model for assisting in the treatment of anxiety disorder. Universal Access in the Information Society 2022;21(2):533 View
- Page-Reeves J, Murray-Krezan C, Regino L, Perez J, Bleecker M, Perez D, Wagner B, Tigert S, Bearer E, Willging C. A randomized control trial to test a peer support group approach for reducing social isolation and depression among female Mexican immigrants. BMC Public Health 2021;21(1) View
- Wu C, Barczyk A, Craddock R, Harari G, Thomaz E, Shumake J, Beevers C, Gosling S, Schnyer D. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health 2021;20:100180 View
- Mei S, Hu Y, Sun M, Fei J, Li C, Liang L, Hu Y. Association between Bullying Victimization and Symptoms of Depression among Adolescents: A Moderated Mediation Analysis. International Journal of Environmental Research and Public Health 2021;18(6):3316 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
- Xu X, Chikersal P, Dutcher J, Sefidgar Y, Seo W, Tumminia M, Villalba D, Cohen S, Creswell K, Creswell J, Doryab A, Nurius P, Riskin E, Dey A, Mankoff J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
- Maharjan S, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt B, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1) View
- Melcher J, Lavoie J, Hays R, D'Mello R, Rauseo-Ricupero N, Camacho E, Rodriguez-Villa E, Wisniewski H, Lagan S, Vaidyam A, Torous J. Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students. Journal of American College Health 2023;71(3):736 View
- Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Fusing Location Data for Depression Prediction. IEEE Transactions on Big Data 2021;7(2):355 View
- Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
- Müller S, Chen X, Peters H, Chaintreau A, Matz S. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports 2021;11(1) View
- Daniel K, Mendu S, Baglione A, Cai L, Teachman B, Barnes L, Boukhechba M. Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life. Anxiety, Stress, & Coping 2022;35(3):298 View
- Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman M, Rose J. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study. Journal of Medical Internet Research 2021;23(8):e28918 View
- Burr C, Morley J, Taddeo M, Floridi L. Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society 2020;1(1):21 View
- Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296 View
- Meyerhoff J, Liu T, Kording K, Ungar L, Kaiser S, Karr C, Mohr D. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(9):e22844 View
- Birtwistle E, Schoedel R, Bemmann F, Wirth A, Sürig C, Stachl C, Bühner M, Niklas F. Mobile sensing in psychological and educational research: Examples from two application fields. International Journal of Testing 2022;22(3-4):264 View
- Keusch F, Conrad F. Using Smartphones to Capture and Combine Self-Reports and Passively Measured Behavior in Social Research. Journal of Survey Statistics and Methodology 2022;10(4):863 View
- Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
- Chikersal P, Venkatesh S, Masown K, Walker E, Quraishi D, Dey A, Goel M, Xia Z. Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping. JMIR Mental Health 2022;9(8):e38495 View
- Henze A. Henry Clerval Scolding Victor Frankenstein: An autoethnographic poem about graduate students and their daemons. McGill Journal of Education 2021;55(3):685 View
- 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
- MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638 View
- LeBaron V, Boukhechba M, Edwards J, Flickinger T, Ling D, Barnes L. Exploring the Use of Wearable Sensors and Natural Language Processing Technology to Improve Patient-Clinician Communication: Protocol for a Feasibility Study. JMIR Research Protocols 2022;11(5):e37975 View
- Flechsenhar A, Kanske P, Krach S, Korn C, Bertsch K. The (un)learning of social functions and its significance for mental health. Clinical Psychology Review 2022;98:102204 View
- Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
- McLeish A, Walker K, Hart J. Changes in Internalizing Symptoms and Anxiety Sensitivity Among College Students During the COVID-19 Pandemic. Journal of Psychopathology and Behavioral Assessment 2022;44(4):1021 View
- Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
- Laiou P, Kaliukhovich D, Folarin A, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F, Ivan A, Lavelle G, Siddi S, Lamers F, Penninx B, Haro J, Annas P, Cummins N, Vairavan S, Manyakov N, Narayan V, Dobson R, Hotopf M. The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones. JMIR mHealth and uHealth 2022;10(1):e28095 View
- 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
- Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
- Brogly C, Shoemaker J, Lizotte D, Kueper J, Bauer M. A Mobile App to Identify Lifestyle Indicators Related to Undergraduate Mental Health (Smart Healthy Campus): Observational App-Based Ecological Momentary Assessment. JMIR Formative Research 2021;5(10):e29160 View
- Abdullah S, Choudhury T. Sensing Technologies for Monitoring Serious Mental Illnesses. IEEE MultiMedia 2018;25(1):61 View
- Zhang Y, Folarin A, Sun S, Cummins N, Vairavan S, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Vilella E, Simblett S, Rintala A, Bruce S, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study. JMIR Mental Health 2022;9(3):e34898 View
- Ware S, Yue C, Morillo R, Shang C, Bi J, Kamath J, Russell A, Song D, Bamis A, Wang B. Automatic depression screening using social interaction data on smartphones. Smart Health 2022;26:100356 View
- SONG C, Sha G, Yao W, YANG L, Bin S. The Influence of Occupational Therapy on College Students’ Home Physical Exercise Behavior and Mental Health Status under the Artificial Intelligence Technology. Occupational Therapy International 2022;2022:1 View
- Bettis A, Burke T, Nesi J, Liu R. Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clinical Psychological Science 2022;10(1):3 View
- Rout A, Nitoslawski S, Ladle A, Galpern P. Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches. Computers, Environment and Urban Systems 2021;90:101705 View
- González-Pérez A, Matey-Sanz M, Granell C, Díaz-Sanahuja L, Bretón-López J, Casteleyn S. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. Journal of Biomedical Informatics 2023;141:104359 View
- Shende C, Sahoo S, Sam S, Patel P, Morillo R, Wang X, Ware S, Bi J, Kamath J, Russell A, Song D, Wang B. Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):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
- Wang Z, Larrazabal M, Rucker M, Toner E, Daniel K, Kumar S, Boukhechba M, Teachman B, Barnes L. Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
- Rajkishan S, Meitei A, Singh A. Role of AI/ML in the study of mental health problems of the students: a bibliometric study. International Journal of System Assurance Engineering and Management 2024;15(5):1615 View
- Stamatis C, Liu T, Meyerhoff J, Meng Y, Cho Y, Karr C, Curtis B, Ungar L, Mohr D. Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety. Internet Interventions 2023;34:100683 View
- Stuijt D, Radanovic I, Kos M, Schoones J, Stuurman F, Exadaktylos V, Bins A, Bosch J, van Oijen M. Smartphone-Based Passive Sensing in Monitoring Patients With Cancer: A Systematic Review. JCO Clinical Cancer Informatics 2023;(7) View
- 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
- 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
- Fernández-Álvarez J, Colombo D, Gómez Penedo J, Pierantonelli M, Baños R, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Mental Health 2024;11:e46593 View
- Song C, Yao L, Chen H, Zhang J, Liu L. The relationship between adverse childhood experiences and depressive symptoms in rural left-behind adolescents: A cross-sectional survey. Heliyon 2024;10(4):e26587 View
- Paersch C, Recher D, Schulz A, Henninger M, Schlup B, Künzler F, Homan S, Kowatsch T, Fisher A, Horn A, Kleim B. Self-Efficacy Effects on Symptom Experiences in Daily Life and Early Treatment Success in Anxiety Patients. Clinical Psychological Science 2024 View
- Segrin C, Jiao J, Cooper R. Social Isolation Mediates the Effects of Negative Emotionality and Resilience on Drinking to Cope and Drinking Alone. Substance Use & Misuse 2024;59(13):1860 View
- Beames J, Han J, Shvetcov A, Zheng W, Slade A, Dabash O, Rosenberg J, O'Dea B, Kasturi S, Hoon L, Whitton A, Christensen H, Newby J. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review. Heliyon 2024;10(15):e35472 View
- Rogoza R. Differential impact of vulnerable isolation and enmity: State social anxiety level, variability and inertia in vulnerable narcissism. Personality and Individual Differences 2025;232:112846 View
- Pavlou K, Manoli A, Benson V, Hadwin J. Eye-movement methodology reveals a shift in attention from threat to neutral stimuli with self-reported symptoms of social anxiety across children, adolescents and adults. Journal of Cognitive Psychology 2024:1 View
- Müller S, Peters H, Matz S, Wang W, Harari G. Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking. European Journal of Personality 2020;34(5):714 View
- Boukhechba M, Baglione A, Barnes L. Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care. Ergonomics in Design: The Quarterly of Human Factors Applications 2020;28(4):18 View
Books/Policy Documents
- Lee J, Lam M, Chiu C. Pervasive Computing Paradigms for Mental Health. View
- Hur J, Stockbridge M, Fox A, Shackman A. Emotion and Cognition. View
- Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. View
- Seidl D. The Geographies of COVID-19. View
- Chemagosi M, Barongo S. Student Stress in Higher Education. View
- Zafeiridi E, Qirtas M, Bantry White E, Pesch D. Bridging the Gap Between AI and Reality. View