Evidence for the decision.

Functional PCA; clustering stability; complex survey inference

Build participant-level daily curves from NHANES monitor summaries using documented MIMS units, wear/sleep flags and quality checks. Fit functional principal components and a small, stable clustering model. Link same-cycle public demographics and selected examination/laboratory measures by participant ID only as explicitly documented companion files.

Evaluation: Compare rhythm-based segments against total-activity bins. Assess within-person day-to-day reliability, weighted cluster stability and sensitivity to valid-day thresholds. Hold participants together and test transfer between survey cycles; report weighted uncertainty and sample exclusions.

Boundary: Use thresholds appropriate to MIMS, not legacy accelerometer-count cutoffs. These data do not measure gym retention or validate personalized wellness advice; linked health endpoints require compatible survey weights and eligibility.

NHANES Physical Activity Monitor ↗

Temporal CNNs; domain generalization; sensor ablation

Segment PAMAP2 streams with documented overlap and label-transition rules. Compare handcrafted features plus a tree model with a compact temporal convolutional network. Run sensor-location and heart-rate ablations, keeping every window from a participant inside the same outer validation fold.

Evaluation: Use nested leave-one-participant-out validation. Report macro-F1, class-specific confusion, transition-period errors, calibration and per-participant results. Compare sensor subsets under the same protocol and measure latency/model size on stated hardware.

Boundary: Nine participants provide limited evidence about real-world users. Measured inference cost does not directly establish battery life, and the model is an activity benchmark rather than a validated medical monitor.

PAMAP2 Physical Activity Monitoring ↗

Bayesian shrinkage; measurement error; incremental predictive value

Use MMASH's short monitoring period and prespecify only a few interpretable features observed before the morning questionnaire. Compare prior affect and a mean-only baseline with strongly regularized models adding sleep/activity or heart-rate-variability summaries. Keep the outcome timing and signal-quality exclusions explicit.

Evaluation: Use participant-held-out prediction with training-fold preprocessing; report absolute error, interval width and sensitivity to individual participants and priors. Compare modality additions one at a time. Publish an inconclusive or negative result if extra sensors do not improve held-out prediction.

Boundary: The study contains 22 healthy young adult men monitored for roughly one day. It cannot support longitudinal recovery forecasting, broad consumer claims or clinical diagnosis; wide uncertainty is a central finding to communicate.

MMASH ↗