Background Since desk-dominated work environments facilitate sedentary behavior, office workers sit for 66% of their working days and only 8% succeed in interrupting their prolonged periods of sitting within the first 55 minutes. Yet stretches of long and uninterrupted sitting increase the likelihood of several chronic metabolic and cardiovascular diseases. Objective We therefore developed a computer-based app designed to interrupt periods of prolonged sitting among office employees. Methods When developing the intervention, we applied the intervention mapping protocol. This approach for the systematic design of theory and evidence-based behavior change programs consists of 6 steps: creation of a logic model of the problem, creation of a logic model of change, program design, program production, design of an implementation plan, and development of an evaluation plan. Results Working through all 6 steps has resulted in an individually adaptable intervention to reduce sedentary behavior at work. The intervention, UPcomplish, consists of tailored, half-automatized motivational components delivered by a coach. To register sedentary behavior, the VitaBit (VitaBit Software International BV) toolkit, a wearable accelerometry-based monitoring device, is used. Among others, UPcomplish includes personalized goal setting, tailored suggestions to overcome hurdles, and weekly challenges. The VitaBit toolkit supports the participants to monitor their behavior in relation to self-set goals. Conclusions Intervention mapping is a useful protocol not only for the systematic development of a comprehensive intervention to reduce sedentary behavior but also for planning program adherence, program implementation, and program maintenance. It facilitates obtaining the participation of relevant stakeholders at different ecological levels in the development process of the intervention and anticipating facilitators to and barriers of program implementation and maintenance. Trial Registration Netherlands Trial Register NL7503; https://www.trialregister.nl/trial/7503

Sedentary behavior (SB) has detrimental consequences and cannot be compensated for through moderate-to-vigorous physical activity (PA). In order to understand and mitigate SB, tools for measuring and monitoring SB are essential. While current direct-to-customer wearables focus on PA, the VitaBit validated in this study was developed to focus on SB. It was tested in a laboratory and in a free-living condition, comparing it to direct observation and to a current best-practice device, the ActiGraph, on a minute-by-minute basis. In the laboratory, the VitaBit yielded specificity and negative predictive rates (NPR) of above 91.2% for sitting and standing, while sensitivity and precision ranged from 74.6% to 85.7%. For walking, all performance values exceeded 97.3%. In the free-living condition, the device revealed performance of over 72.6% for sitting with the ActiGraph as criterion. While sensitivity and precision for standing and walking ranged from 48.2% to 68.7%, specificity and NPR exceeded 83.9%. According to the laboratory findings, high performance for sitting, standing, and walking makes the VitaBit eligible for SB monitoring. As the results are not transferrable to daily life activities, a direct observation study in a free-living setting is recommended.

Background Sedentary behaviour (SB) affects cardiometabolic health and quality of life (QoL). We examine the effects of UPcomplish, a 12-week data-driven intervention, on SB, QoL and psychosocial determinants among office workers. Methods Participants were recruited via judgement sampling. Five groups starting with time-lags of 7 weeks (n = 142, 96 females) received 14 feedback messages (FBMs) which were tailored to SB patterns, goals and hurdles. Participants received questionnaires at the beginning, middle and end of the intervention and wore an accelerometer measuring SB, operationalized as proportions (compositional data approach, CoDA) and summed squared sitting bouts (SSSB). We used linear mixed-effects models with random intercepts for weeks (between-subjects) and individuals (within-subjects). Results UPcomplish did not reduce SB. Within-subjects compared to baseline, FBM #3 (βCoDA = 0.24, p < .001, 95% CI [0.15, 0.33]; βSSSB = 20.83, p < .001, 95% CI [13.90, 27.28]) and #4 (βCoDA = 0.20, p < .001, 95% CI [0.11, 0.29]; βSSSB = 24.80, p < .001, 95% CI [15.84, 33.76]) increased SB. QoL was unaffected. Perceived susceptibility was lower after FBMs #6 to #8 (βbetween = − 0.66, p = .04, 95% CI [− 1.03, − 0.30]; βwithin = − 0.75, p = .02, 95% CI [− 1.18, − 0.32]). Within-subjects, intentions to sit less were higher after FBMs #1 to #5 (1.14, p = .02, 95% CI [0.61, 1.66]). Improvements in determinants and in SB were not associated, nor were improvements in SB and in QoL. Conclusions Compared to VitaBit only, UPcomplish was not beneficial. Environmental restructuring might be superior, but detailed analyses of moderators of effectiveness are needed.

BACKGROUND Since desk-dominated work environments facilitate sedentary behavior, office workers sit for 66% of their working days and only 8% succeed in interrupting their prolonged periods of sitting within the first 55 minutes. Yet stretches of long and uninterrupted sitting increase the likelihood of several chronic metabolic and cardiovascular diseases. OBJECTIVE We therefore developed a computer-based app designed to interrupt periods of prolonged sitting among office employees. METHODS When developing the intervention, we applied the intervention mapping protocol. This approach for the systematic design of theory and evidence-based behavior change programs consists of 6 steps: creation of a logic model of the problem, creation of a logic model of change, program design, program production, design of an implementation plan, and development of an evaluation plan. RESULTS Working through all 6 steps has resulted in an individually adaptable intervention to reduce sedentary behavior at work. The intervention, UPcomplish, consists of tailored, half-automatized motivational components delivered by a coach. To register sedentary behavior, the VitaBit (VitaBit Software International BV) toolkit, a wearable accelerometry-based monitoring device, is used. Among others, UPcomplish includes personalized goal setting, tailored suggestions to overcome hurdles, and weekly challenges. The VitaBit toolkit supports the participants to monitor their behavior in relation to self-set goals. CONCLUSIONS Intervention mapping is a useful protocol not only for the systematic development of a comprehensive intervention to reduce sedentary behavior but also for planning program adherence, program implementation, and program maintenance. It facilitates obtaining the participation of relevant stakeholders at different ecological levels in the development process of the intervention and anticipating facilitators to and barriers of program implementation and maintenance. CLINICALTRIAL Netherlands Trial Register NL7503; https://www.trialregister.nl/trial/7503 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/14951

Objectives: To examine the bidirectional association of sleep duration with proportions of time spent in physical behaviors among Dutch adolescents. Methods: Adolescents (n = 294, 11–15 years) completed sleep diaries and wore an accelerometer (ActiGraph) over 1 week. With linear mixed-effects models, the authors estimated the association of sleep categories (short, optimal, and long) with the following day’s proportion in physical behaviors. With generalized linear mixed models with binomial distribution, the authors estimated the association of physical behavior proportions on sleep categories. Physical behavior proportions were operationalized using percentages of wearing time and by applying a compositional approach. All analyses were stratified by gender accounting for differing developmental stages. Results: For males (number of observed days: 345, n = 83), short as compared with optimal sleep was associated with the following day’s proportion spent in sedentary (−2.57%, p = .03, 95% confidence interval [CI] [−4.95, −0.19]) and light-intensity activities (1.96%, p = .02, 95% CI [0.27, 3.65]), which was not significant in the compositional approach models. Among females (number of observed days: 427, n = 104), long sleep was associated with the proportions spent in moderate- to vigorous-intensity physical activity (1.69%, p < .001, 95% CI [0.75, 2.64]) and in sedentary behavior (−3.02%, p < .01, 95% CI [−5.09, −0.96]), which was replicated by the compositional approach models. None of the associations between daytime activity and sleep were significant (number of obs.: 844, n = 204). Conclusions: Results indicate partial associations between sleep and the following day’s physical behaviors, and no associations between physical behaviors and the following night’s sleep.