Student Perspectives on Digital Phenotyping: The Acceptability of Using Smartphone Data to Assess Mental Health
Open accessAbstract
There is a mental health crisis facing universities internationally. A growing body of interdisciplinary research has successfully demonstrated that using sensor and interaction data from students' smartphones can give insight into stress, depression, mood, suicide risk and more. The approach, which is sometimes termed Digital Phenotyping, has potential to transform how mental health and wellbeing can be monitored and understood. The approach could also transform how interventions are designed, delivered and evaluated. To date, little work has addressed the human and ethical side of digital phenotyping, including how students feel about being monitored. In this paper we report findings from in-depth focus groups, prototyping and interviews with students. We find they are positive about mental health technology, but also that there are multi-layered issues to address if digital phenotyping is to become acceptable. Using an acceptability framework, we set out the key design challenges that need to be addressed.
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User research indicated that students do not like to be monitored and consider passive sensing tracking (eg, collection of location and acceleration data) as too invasive.
“However, this was chosen because user research indicated that students do not like to be monitored and consider passive sensing tracking (eg, collection of location and acceleration data) as too invasive. This is in line with studies examining the acceptability of sensing technology among university students [110,111].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [111]
The Privacy concerns section says participants considered many forms of data collection invasive or sensitive, and Potential for harm reports doubts about monitoring through passive sensing. But the Screen section describes mixed views on location tracking, while the Activity section reports that participants saw walking data as relevant to mental health. The claim overstates these varied views as a general rejection of location and acceleration tracking. (LLM, conf=0.88)
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References 80 · Cited by 87
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- Talayeh Aledavood, Ana Maria Triana Hoyos, Tuomas Alakörkkö, Kimmo Kaski, Jari Saramäki, Erkki Isometsä, and Richard K Darst. 2017. Data collection for mental health studies through digital platforms: requirements and design of a prototype. JMIR research protocols 6, 6 (2017).
- Christopher Frauenberger, Marjo Rauhala, and Geraldine Fitzpatrick. 2017. In-action ethics. Interacting with Computers 29, 2 (2017), 220-236.
- Abhinav Mehrotra, Fani Tsapeli, Robert Hendley, and Mirco Musolesi. 2017. MyTraces: investigating correlation and causation between usersâĂŹ emotional states and mobile phone interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 83.
- Bart Rienties and Bethany Alden Rivers. 2014. Measuring and under- standing learner emotions: Evidence and prospects. Learning Analytics Review 1 (2014), 1-28.
- Robert L Spitzer, Kurt Kroenke, Janet BW Williams, and Bernd Löwe. 2006. A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of internal medicine 166, 10 (2006), 1092-1097.
- Jean M Twenge, Brittany Gentile, C Nathan DeWall, Debbie Ma, Katharine Lacefield, and David R Schurtz. 2010. Birth cohort increases in psychopathology among young Americans, 1938-2007: A cross- temporal meta-analysis of the MMPI. Clinical psychology review 30, 2 (2010), 145-154.
- Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceed- ings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, 3-14.
- Rui Wang, Weichen Wang, Min SH Aung, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A Scherer, et al. 2017. Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 110.
- Authors
- Rooksby, J., Morrison, A., & Murray-Rust, D.
- Affiliations
- Northumbria University (GB)
- University of Glasgow (GB)
- University of Edinburgh (GB)
- Venue
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2019-05-02
- Pages
- 1-14
- OpenAlex
- W2919557631
- Cited by
- 87
- References
- 80
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 18 · 2023: 13 · 2022: 15 · 2021: 13 · 2020: 9 · 2019: 6
- FWCI
- 27.82
- Type
- conference paper
- Language
- en
- Open access
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Northumbria Research Link (Northumbria University) (submittedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Funders
- University of Edinburgh
- Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- digital phenotyping, student mental health, smartphone sensor data, student perceptions, acceptability, mental health technology, suicide risk, well-being monitoring, design challenges
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