Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study
Open accessAbstract
BACKGROUND: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility. OBJECTIVE: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts. METHODS: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, -2 to 2). Meanwhile, with participants' consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model). RESULTS: A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models. CONCLUSIONS: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.
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The affect items were derived from previous personalized sensing studies and a review of affect measurement in previous EMA studies to increase comparability with existing literature.
“We derived these affect items from previous personalized sensing studies [which used the circumplex model of emotion as basis; (69, 70)] and a review of affect measurement in previous EMA studies (71), to increase the comparability with existing literature.”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [70]
Studies in behavioral sensing using smartphones frequently use small samples.
“This is particularly crucial in the field of behavioral sensing using smartphones since studies in this field frequently use small samples, are usually not cross-validated, and failure to replicate has already been observed (Ruwaard et al., 2016).”
From The relationship between mobile phone location sensor data and depressive symptom severity (2016) · cites this paper as Ruwaard et al., 2016
Failure to replicate has already been observed in behavioral sensing using smartphones.
“This is particularly crucial in the field of behavioral sensing using smartphones since studies in this field frequently use small samples, are usually not cross-validated, and failure to replicate has already been observed (Ruwaard et al., 2016).”
From The relationship between mobile phone location sensor data and depressive symptom severity (2016) · cites this paper as Ruwaard et al., 2016
Exploration in small datasets can lead to spurious findings, as seen in the failure to replicate a widely cited paper on phone sensor data to detect mood.
“While exploration is essential to discovery, it is frequently done in small datasets. This can lead to spurious findings, as has recently been seen in the failure to replicate a widely cited paper (Likamwa et al., 2013) on the use of phone sensor data to detect mood (Ruwaard et al., 2016).”
From The relationship between mobile phone location sensor data and depressive symptom severity (2016) · cites this paper as Ruwaard et al., 2016
Studies in behavioral sensing using smartphones are usually not cross-validated.
“This is particularly crucial in the field of behavioral sensing using smartphones since studies in this field frequently use small samples, are usually not cross-validated, and failure to replicate has already been observed (Ruwaard et al., 2016).”
From The relationship between mobile phone location sensor data and depressive symptom severity (2016) · cites this paper as Ruwaard et al., 2016
Asselbergs et al were able to resemble a subject's day-to-day mood level solely based on passively collected data provided by smartphones with 55% to 76% accuracy.
“In another recent explorative study, Asselbergs et al [21] were able to resemble a subject’s day-to-day mood level solely based on passively collected data provided by the smartphones with 55% to 76% accuracy.”
From Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild (2016) · cites this paper as [21]
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- Authors
- Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H.
- Affiliations
- Amsterdam Public Health (NL)
- University of Southern Denmark (DK)
- Corresponding author
- Asselbergs, Joost
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2016-03-29
- Volume
- 18
- Issue
- 3
- Pages
- e72
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4829730
- OpenAlex
- W2321609854
- Semantic Scholar
- 75d969b849b666d3c08f9df23e698fa4b8f3a036
- Cited by
- 192
- References
- 29
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 22 · 2023: 11 · 2022: 14 · 2021: 23 · 2020: 26 · 2019: 22 · 2018: 24 · 2017: 23 · 2016: 9
- FWCI
- 23.73
- 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)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (acceptedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- ecological momentary assessment, unobtrusive assessment, mood prediction, smartphone sensor data, self-monitoring, valence and arousal, student population
- MeSH terms
- Smartphone, Adolescent, Adult, Affect, Data Collection, Depression, Female, Humans, Male, Netherlands, Pilot Projects, Psychometrics, Students, Cell Phone, Young Adult, Self Report, Text Messaging, Accelerometry, Mobile Applications
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