ObjectivesTo adapt and refine a previously-developed youth-specific algorithm to identify bedrest for use in adults. The algorithm is based on using an automated decision tree (DT) analysis of accelerometry data.DesignHealthy adults (n = 141, 85 females, 19–69 years-old) wore accelerometers on the waist, with a subset also wearing accelerometers on the dominant wrist (n = 45). Participants spent ≈24-h in a whole-room indirect calorimeter equipped with a force-platform floor to detect movement.MethodsMinute-by-minute data from recordings of waist-worn or wrist-worn accelerometers were used to identify bedrest and wake periods. Participants were randomly allocated to development (n = 69 and 23) and validation (n = 72 and 22) groups for waist-worn and wrist-worn accelerometers, respectively. The optimized DT algorithm parameters were block length, threshold, bedrest-start trigger, and bedrest-end trigger. Differences between DT classification and synchronized objective classification by the room calorimeter to bedrest or wake were assessed for sensitivity, specificity, and accuracy using a Receiver Operating Characteristic (ROC) procedure applied to 1-min epochs (n = 92,543 waist; n = 30,653 wrist).ResultsThe optimal algorithm parameter values for block length were 60 and 45 min, thresholds 12.5 and 400 counts/min, bedrest-start trigger 120 and 400 counts/min, and bedrest-end trigger 1,200 and 1,500 counts/min, for the waist and wrist-worn accelerometers, respectively. Bedrest was identified correctly in the validation group with sensitivities of 0.819 and 0.912, specificities of 0.966 and 0.923, and accuracies of 0.755 and 0.859 by the waist and wrist-worn accelerometer, respectively. The DT algorithm identified bedrest/sleep with greater accuracy than a commonly used automated algorithm (Cole-Kripke) for wrist-worn accelerometers (p<0.001).ConclusionsThe adapted DT accurately identifies bedrest in data from accelerometers worn by adults on either the wrist or waist. The automated bedrest/sleep detection DT algorithm for both youth and adults is openly accessible as a package “PhysActBedRest” for the R-computer language.

Substantial productivity increases have been reported when incentives are framed as losses rather than gains. Loss-framed contracts have also been reported to be preferred by workers. The results from our meta-analysis and real-effort experiment challenge these claims. The meta-analysis’ summary effect size of loss framing is a 0.16 SD increase in productivity. Whereas the summary effect size in laboratory experiments is a 0.33 SD, the summary effect size from field experiments is 0.02 SD. We detect evidence of publication biases among laboratory experiments. In a new laboratory experiment that addresses prior design weaknesses, we estimate an effect size of 0.12 SD. This result, in combination with the meta-analysis, suggests that the difference between the effect size estimates in laboratory and field experiments does not stem from the limited external validity of laboratory experiments, but may instead stem from a mix of underpowered laboratory designs and publication biases. Moreover, in our experiment, most workers preferred the gain-framed contract and the increase in average productivity is only detectable in the subgroup of workers (~ 20%) who preferred the loss-framed contracts. Based on the results from our experiment and meta-analysis, we believe that behavioral scientists should better assess preferences for loss-framed contracts and the magnitude of their effects on productivity before advocating for greater use of such contracts among private and public sector actors.

Purpose To adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children. Methods Parents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT's performance with optimized parameters was compared with the visual identification, commonly used Sadeh’s sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group. Results On average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh’s algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P<0.001). Both DT (564±77 min/day) and Sadeh’s algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P<0.001). Conclusions The DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment (“PhysActBedRest”).

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