Inside Return's model
Built on forty years of research.
Return's model draws on more than forty peer-reviewed papers across seven research domains. Every coefficient — the weight Return gives to sleep, to HRV, to noise, to weekends — is anchored to a citation, not a guess. Here's where the numbers come from.
Research foundations
Seven fields, one model.
Self-Determination Theory
The backbone. Developed by Deci & Ryan over four decades, SDT holds that well-being depends on three psychological needs — autonomy, competence, relatedness. Return's entire model is organized around the first: the sense that what you're doing is yours to choose. The weekend effect (β = +0.60), the contextual category shifts, and the daily emotion term all trace back to this literature.
- Ryan & Deci (2000). Self-Determination Theory and the facilitation of intrinsic motivation.
- Chen et al. (2015). Basic psychological need satisfaction across four cultures.
- Reis et al. (2000). Daily well-being: autonomy, competence, and relatedness.
- Ryan et al. (2010). Weekends, work, and well-being.
- Milyavskaya et al. (2011). Balance across life domains.
- Vansteenkiste et al. (2013). Autonomy support and dependence support.
- Campbell et al. (2021). Within-person variability in autonomy.
- Holding et al. (2020). Daily autonomy and affect.
Sleep Science
Sleep is the strongest physiological predictor in Return's model (β = +0.40). The app uses a multidimensional sleep score — duration, efficiency, blood oxygen, respiratory rate — with the optimal band set at seven to nine hours. Priors come from large cohort studies and consensus recommendations.
- Hirshkowitz et al. (2015). National Sleep Foundation's sleep time duration recommendations.
- Buysse (2014). Sleep health: can we define it? Does it matter?
- Watson et al. (2015). Recommended amount of sleep for a healthy adult.
- Cappuccio et al. (2010). Quantity and quality of sleep and incidence of type 2 diabetes.
- Ohayon et al. (2017). National Sleep Foundation's sleep quality recommendations.
Heart Rate Variability
HRV is the second-strongest physiological predictor (β = +0.30). It reflects autonomic nervous system balance — a reliable biomarker for stress resilience and self-regulation. Return reads SDNN from Apple Watch and includes a one-day lag term (β = +0.20) to capture delayed cognitive and emotional effects.
- Thayer et al. (2012). A meta-analysis of HRV and neuroimaging studies.
- Laborde et al. (2017). HRV and cognitive function: a systematic review.
- Forte et al. (2019). HRV as a marker of self-regulation.
- Kim et al. (2018). Stress and heart rate variability: a meta-analysis.
- Umetani et al. (1998). Normal values of HRV across decades of age.
- Almeida-Santos et al. (2020). HRV reliability.
- Koenig et al. (2016). HRV and personality traits.
Stress & Perceived Control
Physiological stress is computed from resting heart rate, HRV, respiratory rate, and walking heart rate — then weighted and lagged (β = −0.35 same-day, −0.20 previous-day) to capture carry-over. Environmental noise gets its own coefficient (β = −0.25): noise is a known contributor to cortisol release and sleep disruption.
- Brosschot et al. (2016). Generalized unsafety theory of stress: unconscious perseverative cognition.
- Münzel et al. (2024). Environmental noise and cardiovascular health.
Menstrual Cycle & Physiology
Return optionally reads menstrual cycle data from Apple Health to adjust baselines — hormonal cycles measurably affect HRV, sleep, and perceived autonomy, and ignoring them produces biased predictions. This feature is opt-in and, like everything else, runs on-device.
- Schmalenberger et al. (2019). How to study the menstrual cycle: practical tools and recommendations.
Physical Activity & Well-being
The behavioral-autonomy term (β = +0.25) is a composite of exercise minutes, active energy, distance, standing hours, and mindfulness — weighted by their established links to psychological need satisfaction. Noise deducts from the score. The prior is anchored to meta-analyses of physical activity and self-determination.
- Teixeira et al. (2012). Exercise, physical activity, and self-determination theory: a systematic review.
- Hagger & Chatzisarantis (2014). An integrated behavior-change model for physical activity.
Methodology — EMA & Multilevel Modeling
Ecological Momentary Assessment — short, repeated check-ins in daily life — gives Return a within-person time series. The autoregressive term (β = +0.35, AR-1) captures the fact that yesterday's autonomy predicts today's. Ridge regression with a Bayesian prior handles the cold-start problem when you have only a week of data, following Dora's 2024 recommendation for small-sample multilevel models.
- Bolger & Laurenceau (2013). Intensive longitudinal methods.
- Hamaker et al. (2015). Within-person and between-person effects in longitudinal data.
- Dora et al. (2024). Ridge regression in single-person multilevel models.
How the model learns
Three stages. All on your phone.
Return uses a single-person multilevel model with twenty-six features: sixteen core signals, nine missingness indicators (so the model knows when data is imputed), and one intercept. Training happens in three stages as you accumulate data.
Population Prior
Twenty-six coefficients derived from published literature. You get a reasonable prediction from day one — not a personalized one, but not a guess either.
Ridge OLS
After a week, Ridge-regularized regression fits against your own observations. The prior still pulls the model toward published values, but your data starts to bend it. λ = max(0.1, 10/n) adapts to your sample size.
Personal Correction
A second layer (ΔBeta) captures how your coefficients deviate from the population. Out-of-sample temporal validation confirms the model isn't just memorizing your recent past.
No cloud calls. No telemetry. The entire training pipeline — feature extraction, z-standardization, Ridge solve, residual correction, temporal cross-validation — runs locally on your iPhone. Your model belongs to you and lives with you.