Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
Open accessAbstract
What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.
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Previous studies focused on only mobile phone usage or on phone usage plus wearable sensor data only during sleep and have not taken advantage of 24/7 multimodal phone plus wearable data during wake and sleep to understand behaviors and physiology for long-term study of self-reported stress and mental health.
“These previous studies focused on only mobile phone usage or on phone usage plus wearable sensor data only during sleep and have not taken advantage of 24/7 multimodal phone + wearable data during wake and sleep to understand behaviors and physiology for long-term study of self-reported stress and mental health.”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [14]
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Experimental — accuracy not yet measuredSano et al showed 75% and 90% accuracy for 5-day and 1-month high or low stress recognition using wearable sensor and mobile phone data.
“Sano et al also investigated 5-day self-reported high or low stress recognition (N=18) and 1-month high or low stress recognition (N=66) using wearable sensor and mobile phone data; they showed 75% and 90% accuracy using leave-one participant-out or 10-fold cross-validation, respectively [13,14].”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [14]
The Introduction describes a five-day, 18-participant stress-classification study and the current approximately 30-day, 66-participant study. The Analysis section describes leave-one-participant-out validation for the month-long classifications, not the claimed 10-fold validation. The passages in our copy do not establish the specific 75% and 90% stress-recognition accuracies. (LLM, conf=0.88)
Experimental — accuracy not yet measuredConsistent with previous studies, personality types were one of the most influential and statistically significant factors for self-reported stress and mental health in this college population.
“Consistent with previous studies [7,13,14], personality types were one of the most influential and statistically significant factors for self-reported stress and mental health in this college population.”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [14]
The Results and discussions section (page unavailable) says personality types helped classify high/low self-reported stress (PSS) and mental health (MCS) in the undergraduate sample, and that personality information alone classified those groups with over 80% accuracy. It does not report statistical significance for personality traits or establish that they were among the most influential factors overall. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe combination of wearable sensor and mobile phone data has also been used to study self-reported stress in daily life.
“The combination of wearable sensor and mobile phone data has also been used to study self-reported stress in daily life [11-14].”
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- Authors
- Sano, A., Phillips, A. J., Yu, A. Z., McHill, A. W., Taylor, S., Jaques, N., Czeisler, C. A., Klerman, E. B., & Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Brigham and Women's Hospital (US)
- ORCID
- Venue
- 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
- Publisher
- IEEE
- Published
- 2015-06
- Volume
- 2015
- Pages
- 1-6
- ISSN
- 2376-8886 (ISSN-L)
- PMC
- PMC5431072
- OpenAlex
- W1906543112
- Cited by
- 236
- References
- 12
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 15 · 2023: 13 · 2022: 57 · 2021: 20 · 2020: 35 · 2019: 29 · 2018: 19 · 2017: 17 · 2016: 10 · 2015: 3
- FWCI
- 6.70
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Wearable and Implantable Body Sensor Networks (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- National Institute on Aging (P01AG009975)
- National Heart, Lung, and Blood Institute (K24HL105664, K99HL119618, R01HL114088, R00HL119618, T32HL007901)
- National Institute of General Medical Sciences (R01GM105018)
- National Center for Research Resources (UL1RR025758)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- academic performance, sleep quality, perceived stress, mental health, personality traits, wearable sensors, mobile phone data, physiological monitoring, Pittsburgh Sleep Quality Index, Perceived Stress Scale, SF-12, GPA prediction, behavioral patterns, machine learning classification
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