Mediators of Effects on Physical Activity and Sedentary Time in an Activity Tracker and Behavior Change Intervention for Adolescents: Secondary Analysis of a Cluster Randomized Controlled Trial

Abstract

Background Adolescence is a critical age where steep declines in physical activity and increases in sedentary time occur. Promoting physical activity should therefore be a priority for short- and long-term health benefits. Wearable activity trackers in combination with supportive resources have the potential to influence adolescents’ physical activity levels and sedentary behavior. Examining the pathways through which such interventions work can inform which mediators to target in future studies. Objective The aim of this paper is to examine the impact of the Raising Awareness of Physical Activity (RAW-PA) intervention on potential mediators of behavior change after intervention, and whether these mediated the intervention effects on physical activity and sedentary time at 6-month follow-up. Methods RAW-PA was a 12-week intervention, grounded in social cognitive theory and behavioral choice theory, aimed at increasing physical activity among inactive adolescents through combining a wearable activity tracker with digital resources delivered via a private Facebook group (n=159 complete cases). The targeted potential mediators were identified from previous studies conducted in adolescents and included self-efficacy, peer support, family support, teacher support, self-regulation strategies, barriers, and enjoyment. Outcomes included sedentary time as well as light- and moderate-to-vigorous–intensity physical activity. A series of mixed linear models were used to estimate intervention effects on physical activity and sedentary behavior at follow-up and on potential mediators after intervention and to test whether there were indirect effects of the intervention on physical activity and sedentary behavior via mediators. Results Adolescents in the intervention group (n=75) engaged in higher sedentary time and lower light intensity at 6-month follow-up compared to the wait-list controls (n=84). There were no intervention effects for moderate-to-vigorous–intensity physical activity. The intervention group perceived more barriers to physical activity than the wait-list control group at 6-month follow-up (mean adjusted difference=1.77; 95% CI 0.19-3.34; P=.03). However, indirect effects for each outcome were not statistically significant, indicating that perceived barriers to physical activity did not mediate intervention effects for physical activity or sedentary time. Conclusions RAW-PA did not beneficially impact hypothesized mediators in these inactive adolescents, despite strategies being designed to target them. This suggests that the lack of overall intervention effects on physical activity and sedentary time observed in the RAW-PA study could be due to the limited impact of the intervention on the targeted mediators. Future studies should consider different strategies to target theoretically informed potential mediators and identify intervention strategies that effectively target key mediators to improve physical activity among inactive adolescents. Finally, intervention effects according to level of wearable tracker use or level of engagement with the intervention should be explored. This may provide important insights for designing successful wearable activity tracker interventions. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12616000899448; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716&isReview=true International Registered Report Identifier (IRRID) RR2-10.1186/s12889-016-3945-5

DE-PASS best evidence statement (BESt): determinants of adolescents’ device-based physical activity and sedentary behaviour in settings: a systematic review and meta-analysis

“…The 14 RCTs [53][54][55][56][57][58][59][60][61][62][63][64][65][66] and the one CT [67] included were conducted in three settings, namely school (11 studies), school and family (three studies), and family (one study).…”

“…Thirteen RCTs [53][54][55][56][58][59][60][61][62][63][64][65][66] measured habitual PA/SB, one RCT [57] non-habitual/structured PA/SB (i.e., during physical education), while one CT [67] measured SB in the classroom. Thirteen studies used accelerometers [53-62, 65, 66], one study used pedometers [63] and one used a mobile phone-based pedometer app [64].…”

“…The number of participants in these studies ranged from 40 to 1558 and the intervention duration ranged from four weeks to six months, except for one study including a single physical education session [57]. Three studies [54,58,66] included post-intervention follow-up measures (10-, three-, and six-months post-intervention, respectively). All studies based the design of their interventions on one or more theories and all focused on increasing adolescents' PA and/or decreasing SB and enhancing related psychosocial variables.…”

“…All studies based the design of their interventions on one or more theories and all focused on increasing adolescents' PA and/or decreasing SB and enhancing related psychosocial variables. These studies were implemented during the school schedule either during physical education or in sport-related school programs [53][54][55][56][57][58], while some others [59,60,65,66] involved additional extra-curricular activities (e.g., asking students to participate in after school PAs) (Table 1).…”

“…Thus, the questions about the characteristics of the effective PA interventions and which of the determinants of adolescents' PA/SB are modifiable are still open. Current interventions to promote adolescents' PA are mostly ineffective [8,[62][63][64][65][66][67][68][69][70][71][72][73][74]. Considering the low levels of PA among adolescents [7] that reduced even more during COVID-19 lockdown [86], and the increased levels of SB which has been characterized as a global pandemic [87], the design and implementation of the most effective interventions is urgently needed.…”