Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment
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Abstract
Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference.
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Passive data collected through smartphones can detect daily ratings of depression and anxiety as well as aspects of well-being.
“Although to our knowledge, no work to date has investigated these data streams using machine learning in the context of an MBI, passive data collected through smartphones can detect daily ratings of depression137 and anxiety138 as well as aspects of well-being139.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [138]
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A common factors perspective on mindfulness-based interventions
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- Authors
- Hart, A., Reis, D., Prestele, E., & Jacobson, N. C.
- Affiliations
- Saarland University (DE)
- University of Koblenz and Landau (DE)
- Dartmouth College (US)
- Corresponding author
- Hart, Alexander
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-04-28
- Volume
- 24
- Issue
- 4
- Pages
- e34015
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC9100543
- OpenAlex
- W4225098391
- Cited by
- 22
- References
- 66
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 11 · 2023: 1 · 2022: 2
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Publications of the UdS (Saarland University) (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft
- Universität des Saarlandes
- National Institute on Drug Abuse (P30DA029926)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- smartphone sensor data, paradata, ecological momentary assessment, passive sensing, personalized machine learning, state inference, work-related rumination, fatigue, mood, arousal, life engagement, sleep quality, workers
- MeSH terms
- Smartphone, Machine Learning, Ecological Momentary Assessment, Humans, Surveys and Questionnaires, Reproducibility of Results
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