Youth may choose to be sedentary rather than physically active.
The purpose of this study was to use behavioral economics methods to investigate how experimental changes in the amount of sedentary behaviors influenced physical activity.
Fifty-eight 8- to 16-year-old youth were studied in a within-subject crossover design with three 3-week phases: baseline, increasing, and decreasing targeted sedentary behaviors by 25% to 50%.
At baseline, boys were more active than girls (518.9 vs. 401.2 accelerometer counts/min, p = .02), and obese youth more sedentary than nonobese youth (240.5 vs. 174.4 min/day, p = .003). During the increase sedentary behavior phase, targeted sedentary behaviors increased by 52.1%, with girls increasing sedentary behaviors more than boys (114.7 vs. 79.8 min/day, p = .04). Physical activity decreased (-48.3 counts/min, p < .01) when sedentary behaviors increased, with obese youth decreasing total and moderate to vigorous physical activity (MVPA) more than nonobese youth (-110.4 vs. 8.9 counts/min, p < .001; -3.3 vs. -.03 % MVPA, p = .013). During the decrease sedentary behavior phase, targeted sedentary behaviors decreased by 55.6% from baseline as nonobese youth increased physical activity, whereas obese youth decreased physical activity (55.8 vs. -48.0 counts/min, p = .042; 1.1 vs. -2.1% MVPA, p = .021). Youth who substituted physical activity when sedentary behaviors were increased had greater standardized body mass index (z-body mass index = 1.4 vs. 0.4, p = .018), whereas youth who substituted physical activity when sedentary behaviors were decreased were less active at baseline (396.1 vs. 513.7 counts/min, p = .035).
Behavioral economics provides a methodology to understand changes in physical activity when sedentary behaviors are modified and to identify factors associated with substitution of physically active for sedentary behaviors.
"Adaptive interventions also offer new opportunities for taking advantage of principles of behavior. A Behavioral Economic approach , – incorporating principles of Operant shaping ,  can be used in eHealth and mobile health (mHealth) technologies to increase physical activity through adaptive goal setting and shaping. Shaping is the process of identifying a final behavioral outcome and slowly moving participants towards that outcome by reinforcing behaviors that are closer and closer approximations to the final outcome. "
[Show abstract][Hide abstract] ABSTRACT: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible.
To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention.
Participants (N = 20) were randomized to one of two 6-month treatments: 1) static intervention (SI) or 2) adaptive intervention (AI). Inactive overweight adults (85% women, M = 36.9±9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data.
A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p<.001) and a group by study phase interaction was observed (p = .017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = .74).
The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a "behavior change technology" that could be incorporated into mHealth technologies and scaled to reach large populations.
PLoS ONE 12/2013; 8(12):e82901. DOI:10.1371/journal.pone.0082901 · 3.23 Impact Factor
"There are many proposed mechanisms by which sedentary behaviors may negatively influence health. For television watching, it was initially proposed that watching television may reduce energy expenditure, by competing with time to engage in physical activity, and increase energy intake, by serving as a cue for eating     "
"This rise in childhood obesity has been associated with reduced levels of physical activity (energy expenditure), increased consumption of food (energy intake), or both    . Sedentary screen behaviors, especially TV watching, are hypothesized to contribute to weight gain by reducing opportunities for energy expenditure and increasing energy intake   . Time spent engaging in TV watching can compete with time spent in other activities that require greater amounts of energy   . "
[Show abstract][Hide abstract] ABSTRACT: Previous research suggests that reducing sedentary screen behaviors may be a strategy for preventing and treating obesity in children. This systematic review describes strategies used in interventions designed to either solely target sedentary screen behaviors or multiple health behaviors, including sedentary screen behaviors. Eighteen studies were included in this paper; eight targeting sedentary screen behaviors only, and ten targeting multiple health behaviors. All studies used behavior modification strategies for reducing sedentary screen behaviors in children (aged 1-12 years). Nine studies only used behavior modification strategies, and nine studies supplemented behavior modification strategies with an electronic device to enhance sedentary screen behaviors reductions. Many interventions (50%) significantly reduced sedentary screen behaviors; however the magnitude of the significant reductions varied greatly (-0.44 to -3.1 h/day) and may have been influenced by the primary focus of the intervention, number of behavior modification strategies used, and other tools used to limit sedentary screen behaviors.
Journal of obesity 01/2012; 2012(2090-0708):379215. DOI:10.1155/2012/379215
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.