Physical inactivity and poor sleep are associated with adverse health outcomes and higher health care costs [1,2]. Wearables are increasingly used to promote healthy behaviors [3,4]. Among WHOOP (WHOOP, Inc) members, more frequent wear has been associated with lower resting heart rate (RHR), higher heart rate variability (HRV), and healthier sleep and activity patterns [5]. Whether attainment of wearable-based sleep and physical activity recommendations is associated with markers of cardiometabolic health and health care use remains unclear [6].

We therefore examined, in a single-employer feasibility study, associations between attainment of WHOOP sleep and physical activity recommendations, cardiometabolic health, and employer-sponsored insurance claims.

Participants (N=80; 20 per quartile) had a mean age of 34.2 SD (SD 8.7) years and a mean BMI of 25.6 (SD 3.9) kg/m²; 18 participants (22.5%) were female. Mean age varied across quartiles (29.8‐36.1 years; Table S1 in Multimedia Appendix 1).

RHR decreased with attainment (Figure 1; J-T, P=.001; −3.6 beats per minute [bpm] per SD, 95% CI −5.4 to −1.8), as did BMI (J-T, P=.005; −1.0 kg/m² per SD, 95% CI −1.8 to −0.2). HRV increased with attainment (J-T, P=.011; +9.1% per SD on the log scale, 95% CI −0.5% to 19.5%). Partial Spearman correlations were significant for RHR (ρ = −0.34, P=.002), HRV (ρ=.25, P=.02), and BMI (ρ = −0.36, P=.001).

Prerevision recalculation reassigned 12 participants (15%) to an adjacent quartile (weighted κ=0.88) with similar effects (Table S2 in Multimedia Appendix 1). Strain-recommendation attainment accounted for the RHR and HRV gradients, whereas Sleep Performance alone was associated with neither; both contributed to the BMI gradient (Table S3 in Multimedia Appendix 1).

PMPY spending was US $62,042 in Q1 compared with US $6739, US $3091, and US $5656 in Q2 to Q4 (mean US $5162), approximately 12-fold higher than the other quarters; Q1 accounted for 80% of the cohort’s US $1.55 million total health care spending (Figure 2). Costs were concentrated in 6 catastrophic Q1 claim months that together comprised 84.9% of Q1 spending. When the largest month (US $307,311) was excluded, Q1’s PMPY spending was US $46,676 (~9-fold higher), and when all 6 of these catastrophic claim months were excluded, Q1’s PMPY spending was US $9394 (~1.8-fold higher).

In this single-employer feasibility study, recommendation attainment was associated with more favorable cardiometabolic profiles, which supported construct validity of the metric, and linking wearable data with insurance claims was feasible at the group level, revealing a heavy concentration of spending in the lowest-attainment quartile.

This study has several limitations. The sample was small, drawn from a single employer, and predominantly male, limiting generalizability. Claims were at the quartile level only, and the cost difference was concentrated in 6 catastrophic claim months, which may reflect a small number of high-cost claimants and without which the Q1 excess fell from 12-fold higher to 1.8-fold higher. Because claim types were unknown, costs may reflect cardiometabolic disease, injury, acute illness, or unrelated expenses, and the cross-sectional design cannot exclude reverse causation, whereby acute or nonpreventable illness may have both reduced attainment and raised costs. Residual confounding by unmeasured baseline health status, medication use, occupational demands, and prior health care use could not be assessed. Finally, the Strain recommendation is calibrated to each member’s recent RHR, HRV, and sleep, so RHR and HRV are not fully independent criterion measures. Because this calibration is within person (recommendations are lower, and attainment easier, after above-baseline RHR nights), it should bias between-person associations toward the null, and the BMI gradient is algorithm independent. These findings are hypothesis generating and should not inform employment or insurance decisions. Nevertheless, this study demonstrates the feasibility of linking wearable-derived attainment metrics with physiology and claims and provides effect sizes to inform larger, longitudinal, multiemployer studies.