Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

Cited by (427)

Items 101–200 of 427

1. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study
   Daniel Di Matteo, Kathryn Fotinos, Sachinthya Lokuge, Geneva E. Mason, Tia Sternat, Martin A. Katzman, Jonathan Rose · 2021 · Journal of Medical Internet Research
2. The New Digital Divide For Digital Biomarkers
   John Blake Torous, Jorge Alberto Rodriguez, Adam C. Powell · 2017 · Digital Biomarkers
3. Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care
   Ada Ng, Rachel Kornfield, Stephen Matthew Schueller, Alyson K. Zalta, Michael B. Brennan, Madhu C Reddy · 2019 · Proceedings of the ACM on Human-Computer Interaction
4. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study
   Yuezhou Zhang, Amos Folarin, Shaoxiong Sun, Nicholas Cummins, Srinivasan Vairavan, Rebecca Bendayan, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart et al. · 2022 · JMIR Mental Health
5. Development of a Questionnaire to Measure the Attitudes of Laypeople, Physicians, and Psychotherapists Toward Telemedicine in Mental Health
   Peter Tonn, Silja C. Reuter, Isabelle Kuchler, Britta Reinke, Lena Hinkelmann, Saskia Stöckigt, Hanna Siemoneit, Nina Schulze · 2017 · JMIR Mental Health
6. “How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic
   Britta Wetzel, Rüdiger C. Pryss, Harald Baumeister, Johanna-Sophie Edler, Ana Sofia Oliveira Gonçalves, Caroline Cohrdes · 2021 · International Journal of Environmental Research and Public Health
7. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study
   Daniel Di Matteo, Kathryn Fotinos, Sachinthya Lokuge, Julia Yu, Tia Sternat, Martin A. Katzman, Jonathan Rose · 2020 · JMIR Formative Research
8. Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype
   Talayeh Aledavood, Ana María Triana, Tuomas Alakörkkö, Kimmo K. Kaski, Jari Saramäki, Erkki Tapio Isometsä, Richard K. Darst · 2017 · JMIR Research Protocols
9. Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression
   Lydia Sequeira, Marco Battaglia, Steve Perrotta, Kathleen Ries Merikangas, John Strauss · 2019 · Journal of the American Academy of Child & Adolescent Psychiatry
10. Considering the therapeutic alliance in digital mental health interventions
    Henson, Philip, Peck, Pamela, Torous, John · 2019 · Harvard Review of Psychiatry
11. Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions
    Aditi Site, Elena Simona Lohan, Outi Hannele Jolanki, Outi Valkama, Rosana Rubio Hernández, Rita Latikka, Daria Alekseeva, Saigopal Vasudevan, Samuel Olaiya Afolaranmi, Aleksandr Ometov et al. · 2022 · Sensors
12. mHealth technology to assess, monitor and treat daily functioning difficulties in people with severe mental illness: A systematic review
    Jameel, Leila, Valmaggia, Lucia, Barnes, Georgina, Cella, Matteo · 2022 · Journal of Psychiatric Research
13. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure
    Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lü, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander C. Russell, Bing Wang · 2018 · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
14. Frequency and Duration of Daily Smartphone Usage in Relation to Personality Traits
    Felix Beierle, Thomas Probst, Mathias Allemand, Johannes Zimmermann, Rüdiger C. Pryss, Patrick K. A. Neff, Winfried Schlee, Stefan Stieger, Sanja Budimir · 2020 · Digital Psychology
15. Validation of accelerometry as a digital phenotyping measure of negative symptoms in schizophrenia
    Gregory P. Strauss, Ian Matthew Raugh, Luyu Zhang, Lauren Luther, Hannah C. Chapman, Daniel N. Allen, Brian W Kirkpatrick, Alex S. Cohen · 2022 · Schizophrenia
16. Effectiveness of Mindfulness-Based Stress Management in The Mental Health of Iranian University Students: A Comparison of Blended Therapy, Face-to-Face Sessions, and mHealth App (Aramgar)
    Somaieh Borjalilu, Mohammad Ali Mazaheri, Alireza Talebpour · 2019 · Iranian Journal of Psychiatry and Behavioral Sciences
17. Remote Patient Monitoring in IBD: Current State and Future Directions
    Ashish Atreja, Emamuzo Obaro Otobo, Karthik Ramireddy, Allyssa Deorocki · 2018 · Current Gastroenterology Reports
18. Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach
    Zhongzhi Xu, Qingpeng Zhang, Wentian Li, Mingyang Li, PAUL S. F. YIP · 2019 · International Journal of Medical Informatics
19. Prediction of Mental Health Treatment Adherence using Machine Learning Algorithms
    Madhvan Bajaj, Priyanshu Rawat, Diksha, Satvik Vats, Vikrant Sharma, Lisa Gopal · 2023 · International Conference on Computational Intelligence, Communication Technology and Networking (CICTN)
20. Use of Computer and Mobile Technologies in the Treatment of Depression
    Judith A. Callan, Jesse H. Wright, Greg J. Siegle, Robert H. Howland, Britney B. Kepler · 2016 · Archives of Psychiatric Nursing
21. Social physics: uncovering human behaviour from communication
    Kunal Bhattacharya, Kimmo K. Kaski · 2018 · Advances in Physics X
22. Prediction of stress levels with LSTM and passive mobile sensors
    Yasin Acikmese, S. Emre Alptekin · 2019 · Procedia Computer Science
23. Automatic depression prediction using Internet traffic characteristics on smartphones
    Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lü, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander C. Russell, Athanasios Bamis, Bing Wang · 2020 · Smart Health
24. Use of Multimodal Technology to Identify Digital Correlates of Violence Among Inpatients With Serious Mental Illness: A Pilot Study
    Dror Ben‐Zeev, Emily A. Scherer, Rachel Brian, Lisa A. Mistler, Andrew Thomas Campbell, Rui Wang · 2017 · Psychiatric Services
25. Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
    Alex W DaSilva, Jeremy F. Huckins, Rui Wang, Weichen Wang, Dylan D. Wagner, Andrew Thomas Campbell · 2018 · JMIR mhealth and uhealth
26. Integrating sleep, neuroimaging, and computational approaches for precision psychiatry
    Andrea N. Goldstein‐Piekarski, Bailey Holt-Gosselin, Kathleen O’Hora, Leanne Maree Williams · 2019 · Neuropsychopharmacology
27. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions
    Peter W. Tuerk, Cindy M. Schaeffer, Joseph F. Mcguire, Margo Adams Larsen, Nicole B. Capobianco, John C. Piacentini · 2019 · Current Psychiatry Reports
28. Social functioning in schizophrenia: Comparing laboratory-based assessment with real-world measures
    Abel, Danielle B., Minor, Kyle S. · 2021 · Journal of Psychiatric Research
29. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review
    Pranav Kulkarni, Reuben Kirkham, R. Mcnaney · 2022 · Italian National Conference on Sensors
30. Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation
    Alex S. Cohen, Zachary B. Rodriguez, Kiara K. Warren, Tovah Cowan, Michael D. Masucci, Ole Edvard Granrud, Terje B. Holmlund, Chelsea Chandler, Peter W. Foltz, Gregory P. Strauss · 2022 · Schizophrenia Bulletin
31. Prospective acceptance of distinct mobile mental health features in psychiatric patients and mental health professionals
    Leonie Hendrikoff, Lana Kambeitz‐Ilankovic, Rüdiger C. Pryss, Fanny Senner, Peter Falkai, Oliver Michael Pogarell, Alkomiet Hasan, Henning Peters · 2018 · Journal of Psychiatric Research
32. Digitalising mental health care: Practical recommendations from the European Psychiatric Association
    Janos L. Kalman, Gerrit Burkhardt, Jerzy Samochowiec, Christian Gebhard, Geert Dom, Miriam John, Özge Kılıç, Tamás Kurimay, Lars Lien, Meryam Schouler‐Ocak et al. · 2023 · European Psychiatry
33. What Drives Young Vietnamese to Use Mobile Health Innovations? Implications for Health Communication and Behavioral Interventions
    Bach Xuan Tran, Melvyn Weibin Zhang, Huong Thi Le, Hinh Duc Nguyen, Long Hoang Nguyen, Quyen Le Nguyen, Tho Dinh Tran, Carl Asher Latkin, Roger Ho · 2018 · JMIR mhealth and uhealth
34. Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review
    Vasco Ponciano, Ivan Miguel Pires, Fernando Ribeiro, Gonçalo Marques, Nuno M. García, Nuno Gonçalo Coelho Costa Pombo, Susanna Spinsante, Eftim Zdravevski · 2020 · Electronics
35. Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects
    Katherine Edgley, Andrew W. Horne, Philippa T. K. Saunders, Athanasios Tsanas · 2023 · Cell Reports Medicine
36. Emotionally adaptive support: a narrative review of affective computing for mental health
    Michelle Schlicher, Yupei Li, Sunil Munthumoduku Krishna Murthy, Qiyang Sun, Björn Wolfgang Schuller · 2025 · Frontiers in Digital Health
37. Speech Landmark Bigrams for Depression Detection from Naturalistic Smartphone Speech
    Zhaocheng Huang, Julien Epps, Dale Joachim · 2019 · IEEE International Conference on Acoustics Speech and Signal Processing
38. AIoT-Based Smart Healthcare in Everyday Lives: Data Collection and Standardization From Smartphones and Smartwatches
    Geonwoo Ji, Jiyoung Woo, Geon Lee, Constantino Msigwa, Denis Bernard, Jaeseok Yun · 2024 · IEEE Internet of Things Journal
39. Smartphone-based passive assessment of mobility in depression: Challenges and opportunities
    Brenna N. Renn, Abhishek Pratap, David C. Atkins, Sean David Mooney, Patricia A. Areán · 2018 · Mental health and physical activity
40. Fusing Location Data for Depression Prediction
    Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lü, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander C. Russell, Athanasios Bamis, Bing Wang · 2018 · IEEE Transactions on Big Data
41. Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study
    Rüdiger C. Pryss, Dennis T. John, Winfried Schlee, Wolff Schlotz, Johannes Schobel, Robin Kraft, Myra Spiliopoulou, Berthold Langguth, Manfred Reichert, Teresa O’Rourke et al. · 2019 · JMIR mhealth and uhealth
42. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis
    Yuezhou Zhang, Amos Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Pauline Conde, Heet Sankesara, Petroula Laiou et al. · 2024 · Journal of Medical Internet Research
43. Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns
    Adam G. Horwitz, Ewa K. Czyz, Nadia Al‐Dajani, Walter Dempsey, Zhuo Zhao, Inbal Billie Nahum-Shani, Srijan Sen · 2022 · Journal of Affective Disorders
44. Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
    Dang, Ting, Spathis, Dimitris, Ghosh, Abhirup, Mascolo, Cecilia · 2023 · Royal Society Open Science
45. Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study
    M. Birnbaum, P. Kulkarni, A. Van Meter, Victor Chen, A. Rizvi, Elizabeth Arenare, M. de Choudhury, J. Kane · 2020 · JMIR Mental Health
46. Digital Phenotyping Using Multimodal Data
    Alex S. Cohen, Christopher Richard Cox, Michael D. Masucci, Thanh P. Le, Tovah Cowan, Lyndon M. Coghill, Terje B. Holmlund, Brita Elvevåg · 2020 · Current Behavioral Neuroscience Reports
47. Using Smartphones to Capture and Combine Self-Reports and Passively Measured Behavior in Social Research
    Florian Keusch, Frederick G. Conrad · 2021 · Journal of Survey Statistics and Methodology
48. High potential but limited evidence: Using voice data from smartphones to monitor and diagnose mood disorders.
    Flora Or, John Blake Torous, Jukka-Pekka “JP” Onnela · 2017 · Psychiatric Rehabilitation Journal
49. Passive social sensing with smartphones: a systematic review
    Heng Zhang, Ahmed Ibrahim, Bijan Parsia, Ellen Poliakoff, Simon Harper · 2022 · Computing
50. Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review
    Joanne R. Beames, Jin Han, Artur Shvetcov, Wu Yi Zheng, Aimy Slade, Omar Dabash, Jodie Rosenberg, Bridianne O’Dea, Suranga Nath Kasthurirathne, Leonard Hoon et al. · 2024 · Heliyon
51. Domain Adaptation for Enhancing Speech-Based Depression Detection in Natural Environmental Conditions Using Dilated CNNs
    Zhaocheng Huang, Julien Epps, Dale Joachim, Brian Stasak, James R. Williamson, Thomas Francis Quatieri · 2020 · Interspeech (USB)
52. Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study
    Johannes Taeger, Stefanie Bischoff, Rudolf Hagen, Kristen Rak · 2020 · JMIR mhealth and uhealth
53. A Systematic Review of Location Data for Depression Prediction
    Jaeeun Shin, Sung‐Man Bae · 2023 · International Journal of Environmental Research and Public Health
54. Remote Assessment of Cognitive Function in Juvenile Neuronal Ceroid Lipofuscinosis (Batten disease)
    Shayne N. Ragbeer, Erika Fullwood Augustine, Jonathan W. Mink, Alyssa R. Thatcher, Amy Vierhile, Heather Adams · 2015 · Journal of Child Neurology
55. A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing
    Orianna DeMasi, Benjamin Recht · 2017 · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
56. Findings From a Trial of the Smartphone and OnLine Usage-based eValuation for Depression (SOLVD) Application: What Do Apps Really Tell Us About Patients with Depression? Concordance Between App-Generated Data and Standard Psychiatric Questionnaires for Depression and Anxiety
    MOUKADDAM, NIDAL, TRUONG, ANH, CAO, JIAN, SHAH, ASIM, SABHARWAL, ASHUTOSH · 2019 · Journal of Psychiatric Practice
57. Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults
    Vasco Ponciano, Ivan Miguel Pires, Fernando Ribeiro, María Vanessa Villasana, Rute Sofia dos Santos Crisóstomo, Maria Canavarro Teixeira, Eftim Zdravevski · 2020 · Sensors
58. That Which Is Measured Improves”: An Integrative Review Of The Role Of Self-monitoring In Symptom Self-management And Behavior Change.
    Chen, Jessica A., Fearey, Eliot, Smith, Ronald E. · 2017 · Journal Of Behavior Therapy And Mental Health
59. Approaches for tailoring between-session mental health therapy activities
    Bruna Oewel, Patricia A. Areán, Elena Agapie · 2024 · CHI Conference on Human Factors in Computing Systems
60. Machine learning for ambulatory applications of neuropsychological testing
    Chelsea Chandler, Peter W. Foltz, Alex S. Cohen, Terje B. Holmlund, Jian Cheng, Jared Bernstein, Elizabeth P. Rosenfeld, Brita Elvevåg · 2020 · Intelligence-Based Medicine
61. Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions
    Jessica Schroeder, Jina Suh, Chelsey R. Wilks, Mary P. Czerwinski, Sean A. Munson, James A. Fogarty, Tim Althoff · 2020 · International Conference on Pervasive Computing Technologies for Healthcare
62. Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress
    Alissa Knight, Niranjan P. Bidargaddi · 2018 · Journal of Affective Disorders
63. Measuring social integration and tie strength with smartphone and survey data
    Agnete Skovlund Dissing, Cynthia M. Lakon, Thomas Alexander Gerds, Naja Hulvej Rod, Rikke Lund · 2018 · PLoS ONE
64. Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables
    Francisco de Arriba-Pérez, Juan Manuel Santos-Gago, Manuel Caeiro Rodríguez, Mateo Ramos Merino · 2019 · Journal of Ambient Intelligence and Humanized Computing
65. Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools
    Katherine Hackett, Tania Giovannetti · 2022 · JMIR Aging
66. The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review
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67. Paradigms for Assessing Hedonic Processing and Motivation in Humans: Relevance to Understanding Negative Symptoms in Psychopathology
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68. The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications
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69. Mobility-Based Smartphone Digital Phenotypes for Unobtrusively Capturing Everyday Cognition, Mood, and Community Life-Space in Older Adults: Feasibility, Acceptability, and Preliminary Validity Study
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70. Analysis of gender and identity issues in depression detection on de-identified speech
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71. Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning
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72. The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health
    Kaylee Payne Kruzan, Ada Ng, Colleen Stiles‐Shields, Emily G. Lattie, David C. Mohr, Madhu C Reddy · 2023 · CHI Conference on Human Factors in Computing Systems
73. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment
    Hart, Alexander, Reis, Dorota, Prestele, Elisabeth, Jacobson, Nicholas C · 2022 · Journal of Medical Internet Research
74. Late-Life Depression: A Role for Accelerometer Technology in Diagnosis and Management
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75. Concurrent and prospective associations between fitbit wearable‐derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms
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76. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods
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77. Towards the use of commercial wrist wearables in education
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78. Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
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79. A multimodal smartphone sensor system for behaviour measurement and health status inference
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80. Ethical and Safety Concerns Regarding the Use of Mental Health–Related Apps in Counseling: Considerations for Counselors
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81. Conditional Entropy: A Potential Digital Marker for Stress
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82. Clinical Utility of Wearable Sensors and Patient-Reported Surveys in Patients With Schizophrenia: Noninterventional, Observational Study
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83. Non-Participation in Smartphone Data Collection Using Research Apps
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84. Mobile Health Technology in Late-Life Mental Illness: A Focused Literature Review
    Yara Moussa, Artin Mahdanian, Ching Yu, Marilyn Segal, Karl Julian Looper, Ipsit Vihang Vahia, Soham Rej · 2017 · American Journal of Geriatric Psychiatry
85. Examining the Correlation Between Depression and Social Behavior on Smartphones Through Usage Metadata: Empirical Study
    Yameng Wang, Xiaotong Ren, Xiaoqian Liu, Tingshao Zhu · 2021 · JMIR mhealth and uhealth
86. A collaborative realist review of remote measurement technologies for depression in young people
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87. Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications
    Salar Jafarlou, Iman Azimi, Jocelyn Lai, Yuning Wang, Sina Labbaf, Brenda Nguyen, Hana Qureshi, Christopher Marcotullio, Jessica L. Borelli, Nikil D. Dutt et al. · 2024 · PLoS ONE
88. Using a smartphone’s magnetic sensor in a low-cost experiment to study the magnetic field due to Helmholtz and anti-Helmholtz coil
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89. Fusing location data for depression prediction
    Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lü, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander C. Russell, Athanasios Bamis, Bing Wang · 2017 · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
90. State affect recognition using smartphone sensing data
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91. Ecological momentary assessment and other digital technologies for capturing daily life in mental health
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92. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review
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93. Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity
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94. Insights: Anwendungsmöglichkeiten von passivem Smartphone-Tracking im therapeutischen Kontext
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95. Recognizing Suicidal Intent in Depressed Population using NLP: A Pilot Study
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96. Social Integration
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97. Insights – future implications of passive smartphone sensing in the therapeutic context
    Messner, Eva-Maria, Sariyska, Rayna, Mayer, Benjamin, Montag, Christian, Kannen, Christopher, Schwerdtfeger, Andreas, Baumeister, Harald · 2022 · Verhaltenstherapie
98. Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform
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99. Digital trace data
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100. You're Making Me Depressed: Leveraging Texts from Contact Subsets to Predict Depression
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