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BackgroundTo date, many online health behavior programs developed by researchers have not been translated at scale. To inform translational efforts, health researchers must work with marketing experts to design cost-effective marketing campaigns. It is important to understand the characteristics of end users of a given health promotion program and identify key market segments. ObjectiveThis study aimed to describe the characteristics of the adopters of Active Team, a gamified online social networking physical activity app, and identify potential market segments to inform future research translation efforts. MethodsParticipants (N=545) were Australian adults aged 18 to 65 years who responded to general advertisements to join a randomized controlled trial (RCT) evaluating the Active Team app. At baseline they provided demographic (age, sex, education, marital status, body mass index, location of residence, and country of birth), behavioral (sleep, assessed by the Pittsburgh Quality Sleep Index) and physical activity (assessed by the Active Australia Survey), psychographic information (health and well-being, assessed by the PERMA [Positive Emotion, Engagement, Relationships, Meaning, Achievement] Profile; depression, anxiety and stress, assessed by the Depression, Anxiety, and Stress Scale [DASS-21]; and quality of life, assessed by the 12-Item Short Form Health Survey [SF-12]). Descriptive analyses and a k-medoids cluster analysis were performed using the software R 3.3.0 (The R Foundation) to identify key characteristics of the sample. ResultsCluster analyses revealed four clusters: (1) younger inactive women with poor well-being (218/545), characterized by a higher score on the DASS-21, low mental component summary score on the SF-12, and relatively young age; (2) older, active women (153/545), characterized by a lower score on DASS-21, a higher overall score on the SF-12, and relatively older age; (3) young, active but stressed men (58/545) with a higher score on DASS-21 and higher activity levels; and (4) older, low active and obese men (30/545), characterized by a high body mass index and lower activity levels. ConclusionsUnderstanding the characteristics of population segments attracted to a health promotion program will guide the development of cost-effective research translation campaigns. Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12617000113358; https://www.anzctr.org .au/Trial/Registration/TrialReview.aspx?id=371463 International Registered Report Identifier (IRRID)RR2-10.1186/s12889-017-4882-7

BackgroundIn randomized controlled trials, participants cannot choose their preferred intervention delivery mode and thus might refuse to participate or not engage fully if assigned to a nonpreferred group. This might underestimate the true effectiveness of behavior-change interventions. ObjectiveTo examine whether receiving interventions either matched or mismatched with participants’ preferred delivery mode would influence effectiveness of a Web-based physical activity intervention. MethodsAdults (n = 863), recruited via email, were randomly assigned to one of three intervention delivery modes (text based, video based, or combined) and received fully automated, Internet-delivered personal advice about physical activity. Personalized intervention content, based on the theory of planned behavior and stages of change concept, was identical across groups. Online, self-assessed questionnaires measuring physical activity were completed at baseline, 1 week, and 1 month. Physical activity advice acceptability and website usability were assessed at 1 week. Before randomization, participants were asked which delivery mode they preferred, to categorize them as matched or mismatched. Time spent on the website was measured throughout the intervention. We applied intention-to-treat, repeated-measures analyses of covariance to assess group differences. ResultsAttrition was high (575/863, 66.6%), though equal between groups (t863 =1.31, P =.19). At 1-month follow-up, 93 participants were categorized as matched and 195 as mismatched. They preferred text mode (493/803, 61.4%) over combined (216/803, 26.9%) and video modes (94/803, 11.7%). After the intervention, 20% (26/132) of matched-group participants and 34% (96/282) in the mismatched group changed their delivery mode preference. Time effects were significant for all physical activity outcomes (total physical activity: F2,801 = 5.07, P = .009; number of activity sessions: F2,801 = 7.52, P < .001; walking: F2,801 = 8.32, P < .001; moderate physical activity: F2,801 = 9.53, P < .001; and vigorous physical activity: F2,801 = 6.04, P = .002), indicating that physical activity increased over time for both matched and mismatched groups. Matched-group participants improved physical activity outcomes slightly more than those in the mismatched group, but interaction effects were not significant. Physical activity advice acceptability (content scale: t368 = .10, P = .92; layout scale: t368 = 1.53, P = .12) and website usability (layout scale: t426 = .05, P = .96; ease of use scale: t426 = .21, P = .83) were generally high and did not differ between the matched and mismatched groups. The only significant difference (t621 = 2.16, P = .03) was in relation to total time spent on the website: the mismatched group spent significantly more time on the website (14.4 minutes) than the matched group (12.1 minutes). ConclusionParticipants’ preference regarding delivery mode may not significantly influence intervention outcomes. Consequently, allowing participants to choose their preferred delivery mode may not increase effectiveness of Web-based interventions.

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: [...]