Longitudinal Mood Monitoring and Classification of Self-Reported Mental Health Status Using Feels.ai: Feasibility Study
- 1Internal Medicine, Independent Research, Chicago, USA
Abstract
Purpose
This feasibility study evaluated the use of the Feels.ai (Chicago, Illinois) mobile app to collect longitudinal mood data and characterize emotional and behavioral patterns. A secondary objective was to explore whether app-derived behavioral features could distinguish participants with a self-reported mental health condition from those reporting none. Findings were intended to inform future validation studies focused on predictive modeling and clinical applicability.
Design
This retrospective observational study analyzed de-identified data from 61 college-aged adults who voluntarily enrolled in Feels.ai through social media outreach and recorded mood entries during routine app use. Mental health status was self-reported and was not independently verified through clinical assessment or standardized diagnostic instruments.
Methods
Participants recorded their mood by selecting from a series of emoji-based facial expressions that were internally mapped to a 0-10 distress scale for quantitative analysis. Numeric scores and descriptive labels were not displayed to participants. App-derived features included mood volatility, mean mood score, mood skewness, skipped days, and the day/night activity ratio. An exploratory random forest classifier was developed to distinguish participants reporting anxiety, depression, or both from those reporting none. Model training used repeated five-fold cross-validation with class upsampling. Missing predictor values were median-imputed within each cross-validation fold. Performance was evaluated using discrimination, classification, and calibration metrics. Logistic regression was evaluated as a conventional comparator using the same resampling and preprocessing approach.
Results
Across 61 participants, the average adherence rate was 69.7%, with a mean of 15.6 completed days. The random forest achieved an area under the curve (AUC) of 0.74 (95% CI, 0.62-0.86). At a screening-oriented cutoff of 0.30, sensitivity was 0.88 (95% CI, 0.73-0.97), specificity was 0.44 (95% CI, 0.25-0.65), and balanced accuracy was 0.66. The Brier score was 0.21, with a calibration intercept of 0.04 and a calibration slope of 0.59. Logistic regression achieved an AUC of 0.68, which was not significantly different from that of the random forest model (p = 0.27).
Conclusions
This study demonstrates the feasibility of using Feels.ai for longitudinal mood tracking and deriving behavioral features from routine app use. Exploratory modeling suggests that these features contain signals associated with self-reported mental health status. However, model performance and calibration should be interpreted cautiously given the small sample size, self-reported outcomes, missingness in some longitudinal features, and absence of external validation. Larger, more diverse prospective studies using validated clinical measures are needed to determine the reliability, generalizability, and potential clinical utility of these behavioral features.
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BibTeX
@article{akpalu2026longitudinal,
title = {{Longitudinal Mood Monitoring and Classification of Self-Reported Mental Health Status Using Feels.ai: Feasibility Study}},
author = {Peter Akpalu},
journal = {Cureus},
year = {2026},
month = sep,
doi = {10.7759/cureus.117257},
url = {https://europepmc.org/article/PMC/PMC13723291},
note = {CC BY},
}
RIS
TY - JOUR
TI - Longitudinal Mood Monitoring and Classification of Self-Reported Mental Health Status Using Feels.ai: Feasibility Study
AU - Peter Akpalu
PY - 2026
DA - 2026/09/30
JO - Cureus
DO - 10.7759/cureus.117257
UR - https://europepmc.org/article/PMC/PMC13723291
AB - Purpose This feasibility study evaluated the use of the Feels.ai (Chicago, Illinois) mobile app to collect longitudinal mood data and characterize emotional and behavioral patterns. A secondary objective was to explore whether app-derived behavioral features could distinguish participants with a self-reported mental health condition from those reporting none. Findings were intended to inform future validation studies focused on predictive modeling and clinical applicability. Design This retrospective observational study analyzed de-identified data from 61 college-aged adults who voluntarily enrolled in Feels.ai through social media outreach and recorded mood entries during routine app use. Mental health status was self-reported and was not independently verified through clinical assessment or standardized diagnostic instruments. Methods Participants recorded their mood by selecting from a series of emoji-based facial expressions that were internally mapped to a 0-10 distress scale for quantitative analysis. Numeric scores and descriptive labels were not displayed to participants. App-derived features included mood volatility, mean mood score, mood skewness, skipped days, and the day/night activity ratio. An exploratory random forest classifier was developed to distinguish participants reporting anxiety, depression, or both from those reporting none. Model training used repeated five-fold cross-validation with class upsampling. Missing predictor values were median-imputed within each cross-validation fold. Performance was evaluated using discrimination, classification, and calibration metrics. Logistic regression was evaluated as a conventional comparator using the same resampling and preprocessing approach. Results Across 61 participants, the average adherence rate was 69.7%, with a mean of 15.6 completed days. The random forest achieved an area under the curve (AUC) of 0.74 (95% CI, 0.62-0.86). At a screening-oriented cutoff of 0.30, sensitivity was 0.88 (95% CI, 0.73-0.97), specificity was 0.44 (95% CI, 0.25-0.65), and balanced accuracy was 0.66. The Brier score was 0.21, with a calibration intercept of 0.04 and a calibration slope of 0.59. Logistic regression achieved an AUC of 0.68, which was not significantly different from that of the random forest model (p = 0.27). Conclusions This study demonstrates the feasibility of using Feels.ai for longitudinal mood tracking and deriving behavioral features from routine app use. Exploratory modeling suggests that these features contain signals associated with self-reported mental health status. However, model performance and calibration should be interpreted cautiously given the small sample size, self-reported outcomes, missingness in some longitudinal features, and absence of external validation. Larger, more diverse prospective studies using validated clinical measures are needed to determine the reliability, generalizability, and potential clinical utility of these behavioral features.
SN - 2168-8184
ER -