Personalized ML models for predicting panic attacks from Oura ring data in college students (Kairamkonda … R. McGinnis; Front Digit Health 2026, LEMURS)

Secondary analysis of lemurs (2023-24 academic year, 230 days). Full-time first/second-year UVM students wore a provisioned Oura ring continuously and answered daily surveys in four 21-day sprints; a panic attack was an undefined self-report checkbox. Features: Oura daily summaries (sleep latency/staging/efficiency; intensity minutes, calories, METs; HR, HRV, breathing rate and temperature during sleep) over 7-day sliding windows (mean, SD, P5, P95 → 148 features); label = panic attack in the following week.

• Form factor: finger ring (Oura)

Panic attacks (PAs) are unexpected episodes of intense fear and anxiety, accompanied by cognitive and physical symptoms that significantly impair day-to-day functioning. This study proposes a machine learning approach to predict PA risk in the upcoming week in individuals across mental health conditions, enabling timely and potentially preventive mental health intervention. We evaluate the approach in a longitudinal sample of behavioral and physiological data collected using Oura rings from 39 college students over six months. We assessed multiple prediction algorithms applied within both personalized and generalized frameworks, fitting unique models for each participant and pooling data, respectively. Across all 39 participants, personalized models using logistic regression achieved a pooled PR AUC lift of 1.17 (95% CI: 1.05–1.25) over chance-level prediction ( p  = .035), whereas the generalized model did not differ from chance (PR AUC lift of 0.99; 95% CI: 0.90–1.08); in a direct comparison, personalized models outperformed the generalized model (lift ratio=1.18, p  = .047). Performance varied across participants: when personalized models were grouped by unsupervised clustering of test-set PR AUC lift, models clustered as well-fit ( n  = 8) showed a pooled PR AUC lift of 2.26 (95% CI: 1.50–2.89) and models clustered as poor-fit ( n  = 31) a lift of 1.09 (95% CI: 0.98–1.17). Because these groups were defined by test-set performance, they describe observed heterogeneity and require validation in independent data. Together, our findings point towards the potential value of personalized modeling approaches and suggest that passively collected daily measurements of sleep, activity, and physiology may carry signal for predicting PAs, which could ultimately be leveraged to identify individually tailored lifestyle interventions to reduce PA risk.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/

No results from this paper on ChatPPG leaderboards yet. Browse the leaderboards.