2018
DOI: 10.1186/s13104-018-3306-9
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The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial

Abstract: ObjectiveA recent meta-analysis surmised pedometers were a useful panacea to independently reduce sedentary time (ST). To further test and expand on this deduction, we analyzed the ability of a consumer-wearable activity tracker to reduce ST and prolonged sedentary bouts (PSB). We originally conducted a 12-month randomized control trial where 800 employees from 13 organizations were assigned to control, activity tracker, or one of two activity tracker plus incentive groups designed to increase step count. The … Show more

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Cited by 25 publications
(19 citation statements)
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“…This was also the case for sedentary behavior outcomes. These behaviors were mostly reported in min/day [ 68 , 89 , 93 , 103 , 104 ], while few studies reported them as prolonged sedentary 30-minute bouts (%/day) [ 100 ] or sedentary activity (<5000 steps per day, %) [ 87 ].…”
Section: Resultsmentioning
confidence: 99%
“…This was also the case for sedentary behavior outcomes. These behaviors were mostly reported in min/day [ 68 , 89 , 93 , 103 , 104 ], while few studies reported them as prolonged sedentary 30-minute bouts (%/day) [ 100 ] or sedentary activity (<5000 steps per day, %) [ 87 ].…”
Section: Resultsmentioning
confidence: 99%
“…Thirty minutes was chosen as the cut-point for dividing prolonged from short bouts of sedentary time as this is a common operational definition used in previous literature (e.g. Diaz et al, 2016 ; Sloan et al, 2018 ) and all participants in the present study accumulated sedentary time in bouts of this length.…”
Section: Methodsmentioning
confidence: 99%
“…Another line of work has applied data-driven approach to understand and predict the effect of interventions on people's health (Sloan et al 2018;Robinson et al 2019;Zeevi et al 2015;Phatak et al 2018). The most traditional method estimates the population-level intervention effect from randomized controlled trial (RCT) data (Sloan et al 2018;Robinson et al 2019). The problem with this method is that not all people respond to the same intervention in the same way.…”
Section: Prediction Of Health Conditionsmentioning
confidence: 99%
“…This granularity makes it difficult for predictive models to gather insights from data. Most prior works (Swartz et al 2012;Phatak et al 2018;Sloan et al 2018;Lee et al 2019) used aggregated step count data in their analysis. In contrast, we were interested to extract much more information explaining the time series dynamics that could be further used for the prediction task.…”
Section: Representation Learningmentioning
confidence: 99%