Search

Background: Few mobile health resistance-based physical activity interventions have targeted community-dwelling adults. "Ecofit" is a multicomponent intervention that promotes resistance and aerobic activities using smartphone technology, outdoor gyms, and social support. This study evaluated process evaluation outcomes of the ecofit randomized controlled trial: (1) the acceptability and usability of the ecofit smartphone app and app user workouts; (2) perceptions of factors influencing outdoor gym use; and (3) the fidelity, reach, recruitment, and dose received of the ecofit program. Methods: Process data were collected through program evaluation surveys at 3 months, and app usage data were collected via the intervention platform for up to 3 months. Data were analyzed using descriptive statistics. Results: The survey was completed by 57% (n = 69) of eligible participants. The majority (93%) believed the app provided them with sufficient information to perform muscle-strengthening activities. Approximately half (51%) agreed that the goal-setting function encouraged them to complete their workouts, and 42% agreed that the self-assessment helped them monitor progress. "Proximity" to outdoor gyms emerged as the most important factor for choosing locations to workout (mean = 5.5, SD = 1.1). Participants logged a median of 5.5 (interquartile range = 19) workouts and 1 (interquartile range = 1) upper- and lower-body muscular fitness self-assessment. Conclusions: The ecofit app provided participants with sufficient skills to perform unsupervised resistance training exercises using mobile health. Only half of the participants regarded self-assessments and goal setting as useful, suggesting a need for modifications to how these are implemented. Mobile health remains a promising delivery platform to promote unsupervised resistance training, although more research is needed to improve uptake. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of interventions targeting alcohol consumption, drug use and smoking for college/university students.Participants: College/University students. Methods: Studies were eligible if: (1)included students attending universities/colleges; (2)implemented in a university/college setting; (3)aimed to improve at least one of the following behaviors: alcohol and/or drug use and/or smoking; (4)were RCTs. The effect of the interventions on behaviors was determined by the percentage of studies that reported an effect. Due to the heterogeneity of outcomes meta-analysis was not conducted.Results: 88 studies met criteria. University-based interventions were effective for reducing alcohol-related outcomes (drinking patterns, BAC, consequences, problem drinking). Inconsistent findings for drug and smoking were observed. Conclusions: University-based interventions have the potential to improve health for students. While there is a breadth of research examining the efficacy of interventions to reduce alcohol consumption, further research is needed to determine the best approach for addressing smoking and drug use among students.

Objective: To explore young people's perceptions of pedometers and investigate behaviours exhibited while being monitored. Design: Qualitative design using six focus groups with participants (mean age 14.7 years). Setting: Study participants (n = 24) were randomly selected from a previous study of 123 young people aged 14-15 years from three secondary schools in New South Wales (NSW), Australia. Methods: Participants wore pedometers (Yamax CW700) and accelerometers (ActiGraph GT3X+) simultaneously for 7 days. Accelerometer output was used to categorise participants into one of six focus groups (three boys groups and three girls groups): (1) low active (<30 minutes moderate to vigorous physical activity [MVPA]/day), (2) medium active (30-60 minutes MVPA/day), and (3) high active (=60 minutes MVPA/day). Participants were questioned on their perceptions of the monitoring process and the behaviours that they exhibited while wearing pedometers. A hybrid approach to data analysis identified key concepts, which were thematically analysed. Results: The two main themes identified were (1) participants' perceptions of the monitoring process and (2) behaviour exhibited while being monitored. Overall, participants' attitudes towards objective monitoring were positive. A large proportion reported changing their levels of physical activity during the monitoring process, and 87.5% of focus group participants reported shaking their pedometers to increase their step counts. The medium and high active groups reported changing their activity patterns more than the low active groups. Conclusion: Our findings are consistent with previous quantitative studies that suggest reactivity and tampering are commonplace among young people. Pedometers may have more utility as an intervention strategy for increasing activity rather than as a method for assessing habitual activity levels.

Background: The COVID-19 global pandemic greatly impacted lifestyles and physical activity routines. This study explored how the COVID-19 pandemic impacted participant experience and motivation in ecofit, a large, community-based, multicomponent intervention that supports aerobic and resistance-based physical activity using publicly available, outdoor exercise facilities and smartphone technology. Methods: Fifteen participants from the intervention arm of the study were interviewed. Audiotaped telephone interviews were transcribed verbatim and analyzed by a researcher uninvolved in the intervention. Through an inductive thematic approach, data were coded and organized into themes. The quantitative measure of participant smartphone application engagement was used as a classification during analysis. Results: Several themes representing participants' experience of the COVID-19 pandemic in their engagement with ecofit, overall physical activity levels, motivation to exercise, and perception and usage of outdoor gym facilities were identified. Conclusion: The impact of the COVID-19 pandemic on clinical trials is beginning to emerge. Although the present study showed a mixed impact of the pandemic on participation in the ecofit program, the overall implications for the intervention are yet to be determined. Interestingly, although a large review found a negative impact of COVID-19 on physical activity, this sample reported an overall increase. [ABSTRACT FROM AUTHOR]

This study aims to compare the effectiveness, engagement, usability, and acceptability of a web-based, computer-tailored physical activity intervention (provided as video or text) between participants who were matched or mismatched to their self-reported learning style (visual and auditory delivery through video or text-based information). Generalised linear mixed models were conducted to compare time (baseline, 3 months) by group (matched, mismatched) on ActiGraph-GT3X+measured moderate-to-vigorous physical activity (MVPA) and steps. Generalised linear models were used to compare group (matched and mismatched) on session completion, time-on-site, usability, and acceptability. MVPA and steps improved from baseline to 3-months, however this did not differ between participants whose learning styles were matched or mismatched to the intervention they received. Session completion, time-on-site, usability, and acceptability did not differ between matched and mismatched participants. Therefore, aligning intervention delivery format to learning style is unlikely to influence intervention effectiveness or engagement. [ABSTRACT FROM AUTHOR]

The mechanisms of behavior change in youth screen-time interventions are poorly understood. Participants were 361 adolescent boys (12-14 years) participating in the ATLAS obesity prevention trial, evaluated in 14 schools in low-income areas of New South Wales, Australia. Recreational screen-time was assessed at baseline, 8- and 18-months, whereas potential mediators (i.e., motivation to limit screen-time and parental rules) were assessed at baseline, 4- and 18-months. Multi-level mediation analyses followed the intention-to-treat principle and were conducted using a product-of-coefficients test. The intervention had a significant impact on screen-time at both time-points, and on autonomous motivation at 18-months. Changes in autonomous motivation partially mediated the effect on screen-time at 18-months in single and multi-mediator models [AB (95% CI) = -5.49 (-12.13, -.70)]. Enhancing autonomous motivation may be effective for limiting screen-time among adolescent males. Trial registration: Australian New Zealand Clinical Trials Registry No: ACTRN12612000978864., Author(s): Jordan J. Smith [sup.1] , Philip J. Morgan [sup.1] , Chris Lonsdale [sup.2] , Kerry Dally [sup.1] , Ronald C. Plotnikoff [sup.1] , David R. Lubans [sup.1] Author Affiliations: [...]

Purpose: To examine the utility of the theory of planned behavior (TPB) for explaining physical activity (PA) intention and behavior among a large population sample of overweight and obese adolescents (Alberta, Canada), using a web-based survey. Secondary objectives were to examine the mediating effects of the TPB constructs and moderating effects of weight status. Methods: A subsample of 427 overweight and 133 obese participants ("n" = 560), completed a self-administered web-based questionnaire on health and PA behaviors, including assessment of attitude, subjective norm, perceived behavioral control (PBC), and intention to participate in regular PA. Structural equation models were examined using AMOS 17.0. Results: Overall, 62% of the variance in intention was accounted for by attitude, subjective norm, and PBC; whereas 44% of the variance in PA behavior was explained by PBC and intention. When examining the TPB separately in overweight and obese subsamples, 66% and 56% of the variance for PA intention was explained for overweight and obese subsamples, respectively; and 38% and 56% of the variance in PA behavior were accounted for in the overweight and obese subsamples. Overall, attitude was the strongest predictor of PA intention, whereas PBC was the strongest predictor for PA behavior. Intention was not predictive of PA behavior. Conclusions: These results provide partial support for the utility of TPB in explaining PA behavior in a sample of overweight and obese adolescents. In particular, strong associations regarding attitude and PBC were evident across each subsample. These findings have implications for tailoring PA programs in this population. (Contains 3 tables, 3 figures, and 1 note.)

This study involved the design and evaluation of the High-Intensity Interval Training Self-Efficacy Questionnaire (HIIT-SQ). Phase 1: Questionnaire items were developed. Phase 2: Australian adolescents (N = 389, 16.0 ± 0.4 years, 41.10% female) completed the HIIT-SQ, and factorial validity of the measurement model was explored. Phase 3: Adolescents (N = 100, age 12-14 years, 44% female) completed the HIIT-SQ twice (1 week apart) to evaluate test-retest reliability. Confirmatory factor analysis of the final six items (mean = 3.43-6.73, SD = 0.99-25.30) revealed adequate fit, χ2(21) = 21, p = .01, comparative fit index = .99, Tucker-Lewis index = .99, root mean square of approximation = .07, 90% confidence interval [.04, .11]. Factor loading estimates showed that all items were highly related to the factor (estimates range: 0.81-0.90). Intraclass coefficients and typical error values were .99 (95% confidence interval [.99, 1.00]) and .22, respectively. This study provides preliminary evidence for the validity and reliability of scores derived from the HIIT-SQ in adolescents. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to test the hypothesized structural paths in Bandura's social-cognitive theory (SCT) model on adolescent girls' physical activity following a 12-month physical activity and dietary intervention to prevent obesity. Method: We conducted a 12-month follow-up study of 235 adolescent girls ("M[subscript age]" = 13.2 years, "SD" = 0.4) from 12 secondary schools located in low-income communities. At baseline, participants completed SCT scales related to physical activity (i.e., self-efficacy, intention, parental support, and outcome expectations). At baseline and 12-month follow-up (postintervention), participants wore accelerometers for 7 days. Structural equation modeling was used to determine if Time 1 measures predicted physical activity at 12-month follow-up after adjusting for baseline activity. Results: The model explained 28% and 34% of the variance in physical activity and intention, respectively. Model fit indexes indicated the data were a good fit to the model; however, only self-efficacy was associated with physical activity at 12 months. There was no support for intention or outcome expectations as proximal determinants of behavior. Self-efficacy was associated with outcome expectations and parental support; however, only outcome expectations predicted intention. Conclusions: Current findings indicate a large proportion of the variance for physical activity and intention remains unexplained and that the proposed pathways in the SCT model were not fully supported. Future model testing may need to consider augmentation or integration of theoretical models, which may include ecological components if we are to advance our understanding of physical activity behavior in this subgroup of the adolescent population.