Search

Background: Low objective socioeconomic status (SES) and subjective social status (SSS), one's perceived social rank, are associated with obesity. This association may be due, in part, to social status-related differences in energy expenditure. Experimental studies are needed to assess the extent to which SES and SSS relate to energy expenditure., Objective: Assess the effects of experimentally manipulated social status and SSS on moderate-to-vigorous physical activity (MVPA) and sedentary behaviour., Methods: One hundred thirty-three Hispanic adolescents aged 15-21 were randomized to a high or low social status position, facilitated through a rigged game of Monopoly™. SSS was assessed with MacArthur Scales. Post-manipulation 24-h MVPA and sedentary behaviour were assessed via accelerometry. Analyses were conducted with general linear regression models., Results: Experimentally manipulated social status did not significantly affect the total time spent in MVPA or sedentary behaviour; however, identifying as low SSS was significantly associated with less MVPA (p = 0.0060; 18.76 min less)., Conclusions: Tewnty-four-hour MVPA and sedentary behaviour are not affected by an acute experimental manipulation of social status. However, low SSS, independent of SES, was associated with clinically significant differences in MVPA. SSS may be a better predictor of MVPA than SES among Hispanic adolescents, potentially influencing obesity, and other health-related outcomes., (© 2021 World Obesity Federation.)

Background: Increasing physical activity (PA) levels in adolescents aged 12 to 18 years is associated with prevention of unhealthy weight gain and improvement in cardiovascular fitness. The widespread availability of mobile health (mHealth) and wearable devices offers self-monitoring and motivational features for increasing PA levels and improving adherence to exercise programs., Objective: The aim of this scoping review was to identify the efficacy or effectiveness of mHealth intervention strategies for facilitating PA among adolescents aged 12 to 18 years., Methods: We conducted a systematic search for peer-reviewed studies published between 2008 and 2018 in the following electronic databases: PubMed, Google Scholar, PsychINFO, or SportDiscus. The search terms used included mHealth or "mobile health" or apps, "physical activity" or exercise, children or adolescents or teens or "young adults" or kids, and efficacy or effectiveness. Articles published outside of the date range (July 2008 to October 2018) and non-English articles were removed before abstract review. Three reviewers assessed all abstracts against the inclusion and exclusion criteria. Any uncertainties or differences in opinion were discussed as a group. The inclusion criteria were that the studies should (1) have an mHealth component, (2) target participants aged between 12 and 18 years, (3) have results on efficacy or effectiveness, and (4) assess PA-related outcomes. Reviews, abstracts only, protocols without results, and short message service text messaging-only interventions were excluded. We also extracted potentially relevant papers from reviews. At least 2 reviewers examined all full articles for fit with the criteria and extracted data for analysis. Data extracted from selected studies included study population, study type, components of PA intervention, and PA outcome results., Results: Overall, 126 articles were initially identified. Reviewers pulled 18 additional articles from excluded review papers. Only 18 articles were passed onto full review, and 16 were kept for analysis. The included studies differed in the sizes of the study populations (11-607 participants), locations of the study sites (7 countries), study setting, and study design. Overall, 5 mHealth intervention categories were identified: website, website+wearable, app, wearable+app, and website+wearable+app. The most common measures reported were subjective weekly PA (4/13) and objective daily moderate-to-vigorous PA (5/13) of the 19 different PA outcomes assessed. Furthermore, 5 of 13 studies with a control or comparison group showed a significant improvement in PA outcomes between the intervention group and the control or comparison group. Of those 5 studies, 3 permitted isolation of mHealth intervention components in the analysis., Conclusions: PA outcomes for adolescents improved over time through mHealth intervention use; however, the lack of consistency in chosen PA outcome measures, paucity of significant outcomes via between-group analyses, and the various study designs that prevent separating the effects of intervention components calls into question their true effect., (©Alexandra M Lee, Sarah Chavez, Jiang Bian, Lindsay A Thompson, Matthew J Gurka, Victoria G Williamson, François Modave. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 12.02.2019.)

Objective: Self-monitoring and behavioral feedback are widely used to help people monitor progress toward daily physical activity goals. Little information exists about the optimal dosing parameters for these techniques or if they are interchangeable in digital physical activity interventions. This study used a within-person experimental design to evaluate associations between the frequency of two different prompt types (one for each technique) and daily physical activity. Method: Insufficiently active young adults were assigned monthly physical activity goals and wore smartwatches with activity trackers for 3 months. They received zero to six randomly selected and timed watch-based prompts each day, with individual prompts either providing behavioral feedback or prompting the participant to self-monitor. Results: Physical activity increased significantly over the 3-month period (step counts d= 1.03; moderate-to-vigorous physical activity duration d =0.99). Mixed linear models revealed that daily step counts were positively associated with the frequency of daily self-monitoring prompts up to approximately three prompts/day (d =0.22) after which additional prompts provided minimal or reduced benefit. Daily step counts were not associated with the frequency of behavioral feedback prompts. Daily moderate-to-vigorous physical activity was not associated with the frequency of either prompt. Conclusions: Self-monitoring and behavioral feedback are not interchangeable behavior change techniques in digital physical activity interventions, and only self-monitoring prompts show signs of a dose-response association with physical activity volume. Activity trackers, such as smartwatches and mobile apps, should provide an option to replace behavioral feedback prompts with self-monitoring prompts to promote physical activity among insufficiently active young adults. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity.Purpose: This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions.Methods: Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020.Results: Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration.Conclusions: Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change. [ABSTRACT FROM AUTHOR]