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Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Based on these findings and the literature on passive sensing, usability, and ethics, we make the following recommendations for future research on passive sensing in mental health.

Tjeerd W Boonstra, Jennifer Nicholas, Quincy JJ Wong, Frances Shaw, Samuel Townsend, Helen Christensen

J Med Internet Res 2018;20(7):e10131

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Patient Willingness to Consent to Mobile Phone Data Collection for Mental Health Apps: Structured Questionnaire

Daniel Di Matteo, Alexa Fine, Kathryn Fotinos, Jonathan Rose, Martin Katzman

JMIR Ment Health 2018;5(3):e56

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Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study

Many studies have explored how smartphone-sensing data can be used as a predictor for depression and mental health [44-47], but very few studies have applied passive sensing to predict symptoms of depression among people with diabetes, thereby enabling improved

Archana Sarda, Suresh Munuswamy, Shubhankar Sarda, Vinod Subramanian

JMIR Mhealth Uhealth 2019;7(1):e11041

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Accuracy of Consumer Wearable Heart Rate Measurement During an Ecologically Valid 24-Hour Period: Intraindividual Validation Study

Benjamin W Nelson, Nicholas B Allen

JMIR Mhealth Uhealth 2019;7(3):e10828

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Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

Daniel Di Matteo, Wendy Wang, Kathryn Fotinos, Sachinthya Lokuge, Julia Yu, Tia Sternat, Martin A Katzman, Jonathan Rose

JMIR Form Res 2021;5(1):e22723

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Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Daniel A Adler, Dror Ben-Zeev, Vincent W-S Tseng, John M Kane, Rachel Brian, Andrew T Campbell, Marta Hauser, Emily A Scherer, Tanzeem Choudhury

JMIR Mhealth Uhealth 2020;8(8):e19962

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