The effectiveness of physical activity monitoring and distance counseling in an occupational setting – Results from a randomized controlled trial (CoAct)

Abstract

BackgroundLack of physical activity (PA) is a known risk factor for many health conditions. The workplace is a setting often used to promote activity and health. We investigated the effectiveness of an intervention on PA and productivity-related outcomes in an occupational setting.MethodsWe conducted a randomized controlled trial of 12 months duration with two 1:1 allocated parallel groups of insurance company employees. Eligibility criteria included permanent employment and absence of any condition that risked the participant’s health during PA. Subjects in the intervention group monitored their daily PA with an accelerometer, set goals, had access to an online service to help them track their activity levels, and received counseling via telephone or web messages for 12 months. The control group received the results of a fitness test and an information leaflet on PA at the beginning of the study. The intervention’s aim was to increase PA, improve work productivity, and decrease sickness absence. Primary outcomes were PA (measured as MET minutes per week), work productivity (quantity and quality of work; QQ index), and sickness absence (SA) days at 12 months. Participants were assigned to groups using block randomization with a computer-generated scheme. The study was not blinded.ResultsThere were 544 randomized participants, of which 521 were included in the analysis (64% female, mean age 43 years). At 12 months, there was no significant difference in physical activity levels between the intervention group (n = 264) and the control group (n = 257). The adjusted mean difference was −206 MET min/week [95% Bayesian credible interval −540 to 128; negative values favor control group]. There was also no significant difference in the QQ index (−0.5 [−4.4 to 3.3]) or SA days (0.0 [−1.2 to 0.9]). Of secondary outcomes, body weight (0.5 kg [0.0 to 1.0]) and percentage of body fat (0.6% [0.2% to 1.1%]) were slightly higher in the intervention group. An exploratory subgroup analysis revealed no subgroups in which the intervention affected physical activity. No adverse events were reported.ConclusionsThe intervention was not found effective, and this study does not provide support for the effectiveness of the workplace PA intervention used here.Trial registrationClinicalTrials.gov identifier: NCT00994565

A Systematic Review of Internet-Based Worksite Wellness Approaches for Cardiovascular Disease Risk Management: Outcomes, Challenges & Opportunities

“…Among the randomized studies, 16 were largely internet-based[13]–[28], while the other 2 were multi-component studies[29], [30]. Similarly, 2 of the 10 interventions (11 studies) in the pre-post group had multiple components[31], [32], while the others were largely internet-based [12], [33]–[40].…”

The Impact of Interventions that Integrate Accelerometers on Physical Activity and Weight Loss: A Systematic Review

“…Twelve studies reported on outcomes related to physical activity [29–40] and 11 on outcomes related to weight [27–29,31–37,39]. Four trials were conducted with older adults; five with overweight, obese, or sedentary adults; three with participants with a chronic medical illness (e.g., chronic obstructive pulmonary disease, metabolic disease, diabetes); and two with healthy volunteers.…”

Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence.

“…Articles had to include at least one term from each of the filters in the title, abstract, or measure, but did not report sufficient data for us to be able to compute an effect size and this information could not be obtained by emailing the author(s) (e.g., Reijonsaari et al, 2012). Eighty one studies (13%) were excluded because they did not randomly assign adult participants to an intervention condition designed to promote self-monitoring of goal progress (e.g., Graham, Cha, Cobb, Fang, Niaura, & Mushro, 2013; Te Velde, Wind, PerezRodrigo, Klepp, & Brug, 2008).…”

“…Finally, studies had to include a measure of behavior(s) (e.g., step count) or outcome(s) (e.g., levels of glycated haemoglobin [HbA1c], weight) in the wake of the intervention. 1 The sample of studies was generated via a computerized search of social scientific databases (those accessed by Web of Knowledge 2 , as well as UMI Dissertation Abstracts).Three search filters were used, one for randomized control trials (random* AND intervention or random* AND experiment*) 3 , one for progress monitoring designed to reflect different terms for self-monitoring and methods that interventions might use to invoke progress monitoring (monitor* OR progress OR track OR diary OR website OR Personal Digital Assistant OR Phone OR pedometer OR meter OR self-weigh*), and one filter for the dependent variable (goal OR behav* OR outcome OR perform* OR consum*).Articles had to include at least one term from each of the filters in the title, abstract, or measure, but did not report sufficient data for us to be able to compute an effect size and this information could not be obtained by emailing the author(s) (e.g., Reijonsaari et al, 2012). Eighty one studies (13%) were excluded because they did not randomly assign adult participants to an intervention condition designed to promote self-monitoring of goal progress (e.g., Graham, Cha, Cobb, Fang, Niaura, & Mushro, 2013; Te Velde, Wind, PerezRodrigo, Klepp, & Brug, 2008).…”