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Physical inactivity levels are rising globally. In response, public health investigators have sought to design and implement effective interventions to raise levels of physical activity in populations, communities and individuals. Usually, such interventions are built around theories of behaviour change in which notions of motivation and incentivisation loom large. Drawing on focus group evidence derived in the context of a cluster RCT aimed at increasing levels of physical activity in the workplace, this paper offers a critical look at the factors that seemingly stimulate and sustain that activity. In particular, using novel methods of data analysis we illustrate how motives and motivation – often interpreted as a driving force 'in' individuals – can be more usefully seen as distributed; as a property of systems (incorporating technologies, organisational structures, and social interaction) rather than of persons. In our discussion, we outline various theoretical as well as empirical grounds for making such claims and conclude by drawing out the implications of our work for public health policy and research. [ABSTRACT FROM AUTHOR]

Objective: The physical activity loyalty (PAL) scheme was a cluster randomized controlled trial of a 6-month complex intervention targeting workplace physical activity. Financial incentives were incorporated in an evidence-based behavior change program, including self-regulation techniques. This article examines short-term (<6 months) and long-term (≥6 months) mediation effects on physical activity. Method: Participants included 853 adults (457 intervention, 396 control). Physical activity was objectively assessed using pedometers at baseline and at 6 and 12 months. Hypothesized short-term mediators (e.g., self-efficacy, intentions) were assessed at baseline and 4 weeks. Hypothesized long-term mediators (e.g., habit, intrinsic motivation) were assessed at baseline and 6 months. Mediation models employed the structural equation modeling product-of-coefficients approach. Results: Intervention participants experienced significant decreases in 6-month pedometer steps/day versus controls (b = −336, p =.02), which were partially mitigated by positive indirect effects through 6-month integrated regulation (ab = 94.7, 95% CI [18.7, 204.4]), intrinsic motivation (ab = 59.0, 95% CI [3.09, 154.5]), and habit (ab = 198.7, 95% CI [84.3, 369.9]). There were no between-groups differences in 12-month pedometer steps/day but positive indirect effects through 6-month integrated regulation (ab = 128.0, 95% CI [27.3, 313.2]), planning (ab = 115.0, 95% CI [3.71, 285.5]), and habit (ab = 153.3, 95% CI [39.3, 333.1]). Conclusions: Most examined mediators were nonsignificant, and mediation analyses did not explain decreases in physical activity for interventions versus controls. Results show that, contrary to self-determination theory hypotheses, intrinsic motivation is not necessarily adversely impacted if financial incentives are embedded in a complex intervention. [ABSTRACT FROM AUTHOR]

Background: Investigating participant engagement and nonusage attrition can help identify the likely active ingredients of electronic health interventions. Research on engagement can identify which intervention components predict health outcomes. Research on nonusage attrition is important to make recommendations for retaining participants in future studies.Objective: This study aimed to investigate engagement and nonusage attrition in the Physical Activity Loyalty (PAL) scheme, a 6-month complex physical activity intervention in workplaces in Northern Ireland. The intervention included financial incentives with reward redemption and self-regulation techniques. Specific objectives were (1) to determine whether engagement in specific intervention components predicted physical activity at 6 months, (2) to determine whether engagement in specific intervention components predicted targeted mediators at 6 months, and (3) to investigate predictors of nonusage attrition for participants recording daily activity via the PAL scheme physical activity monitoring system and logging onto the website.Methods: Physical activity was assessed at baseline and 6 months using pedometers (Yamax Digiwalker CW-701, Japan). Markers of engagement and website use, monitoring system use, and reward redemption were collected throughout the scheme. Random-effects generalized least-squares regressions determined whether engagement with specific intervention components predicted 6-month physical activity and mediators. Cox proportional hazards regressions were used to investigate predictors of nonusage attrition (days until first 2-week lapse).Results: A multivariable generalized least-squares regression model (n=230) showed that the frequency of hits on the website's monitoring and feedback component (regression coefficient [b]=50.2; SE=24.5; P=.04) and the percentage of earned points redeemed for financial incentives (b=9.1; SE=3.3; P=.005) were positively related to 6-month pedometer steps per day. The frequency of hits on the discussion forum (b=-69.3; SE=26.6; P=.009) was negatively related to 6-month pedometer steps per day. Reward redemption was not related to levels of more internal forms of motivation. Multivariable Cox proportional hazards regression models identified several baseline predictors associated with nonusage attrition. These included identified regulation (hazard ratio [HR] 0.88, 95% CI 0.81-0.97), recovery self-efficacy (HR 0.88, 95% CI 0.80-0.98), and perceived workplace environment safety (HR 1.07, 95% CI 1.02-1.11) for using the physical activity monitoring system. The EuroQoL health index (HR 0.33, 95% CI 0.12-0.91), financial motivation (HR 0.93, 95% CI 0.87-0.99), and perceived availability of physical activity opportunities in the workplace environment (HR 0.96, 95% CI 0.93-0.99) were associated with website nonusage attrition.Conclusions: Our results provide evidence opposing one of the main hypotheses of self-determination theory by showing that financial rewards are not necessarily associated with decreases in more internal forms of motivation when offered as part of a complex multicomponent intervention. Identifying baseline predictors of nonusage attrition can help researchers to develop strategies to ensure maximum intervention adherence.Trial Registration: ISRCTN Registry ISRCTN17975376; http://www.isrctn.com/ISRCTN17975376 (Archived by WebCite at http://www.webcitation.org/76VGZsZug). [ABSTRACT FROM AUTHOR]