BackgroundEmerging technologies (ie, mobile phones, Internet) may be effective tools for promoting physical activity (PA). However, few interventions have provided effective means to enhance social support through these platforms. Face-to-face programs that use group dynamics-based principles of behavior change have been shown to be highly effective in enhancing social support through promoting group cohesion and PA, but to date, no studies have examined their effects in Web-based programs.ObjectiveThe aim was to explore proof of concept and test the efficacy of a brief, online group dynamics-based intervention on PA in a controlled experiment. We expected that the impact of the intervention on PA would be moderated by perceptions of cohesion and the partner’s degree of presence in the online media.MethodsParticipants (n=135) were randomized into same-sex dyads and randomly assigned to one of four experimental conditions: standard social support (standard), group dynamics-based–high presence, group dynamics-based–low presence, or individual control. Participants performed two sets of planking exercises (pre-post). Between sets, participants in partnered conditions interacted with a virtual partner using either a standard social support app or a group dynamics-based app (group dynamics-based–low presence and group dynamics-based–high presence), the latter of which they participated in a series of online team-building exercises. Individual participants were given an equivalent rest period between sets. To increase presence during the second set, participants in the group dynamics-based–high presence group saw a live video stream of their partner exercising. Perceptions of cohesion were measured using a modified PA Group Environment Questionnaire. Physical activity was calculated as the time persisted during set 2 after controlling for persistence in set 1.ResultsPerceptions of cohesion were higher in the group dynamics-based–low presence (overall mean 5.81, SD 1.04) condition compared to the standard (overall mean 5.04, SD 0.81) conditions (P=.006), but did not differ between group dynamics-based–low presence and group dynamics-based–high presence (overall mean 5.42, SD 1.07) conditions (P=.25). Physical activity was higher in the high presence condition (mean 64.48, SD 20.19, P=.01) than all other conditions (mean 53.3, SD 17.35).ConclusionsA brief, online group dynamics-based intervention may be an effective method of improving group cohesion in virtual PA groups. However, it may be insufficient on its own to improve PA.

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Background: Obesity is a major risk factor behind some of the most common problems encountered in primary care. Although effective models for obesity treatment have been developed, the 'reach' of these interventions is poor and only a small fraction of primary care patients receive evidence-based treatment. The purpose of this study is to identify factors that impact the uptake (reach) of an evidence-based obesity treatment program within the context of a pragmatic cluster randomized controlled trial comparing three models of care delivery. Methods: Recruitment and reach were evaluated by the following measures: 1) mailing response rates, 2) referral sources among participants contacting the study team, 3) eligibility rates, 4) participation rates, and 5) representativeness based on demographics, co-morbid conditions, and healthcare utilization of 1432 enrolled participants compared to > 17,000 non-participants from the clinic-based patient populations. Referral sources and participation rates were compared across study arms and level of clinic engagement. Results: The response rate to clinic-based mailings was 13.2% and accounted for 66% of overall program recruitment. An additional 22% of recruitment came from direct clinic referrals and 11% from media, family, or friends. Of those screened, 87% were eligible; among those eligible, 86% enrolled in the trial. Participation rates did not vary across the three care delivery arms, but were higher at clinics with high compared to low provider involvement. In addition, clinics with high provider involvement had a higher rate of in clinic referrals (33% versus 16%) and a more representative sample with regards to BMI, rurality, and months since last clinic visit. However, across clinics, enrolled participants compared to non-participants were older, more likely to be female, more likely to have had a joint replacement but less likely to have CVD or smoke, and had fewer hospitalizations.Conclusions: A combination of direct patient mailings and in-clinic referrals may enhance the reach of primary care behavioral weight loss interventions, although more proactive outreach is likely necessary for men, younger patients, and those at greater medial risk. Strategies are needed to enhance provider engagement in referring patients to behavioral weight loss programs.(Continued on next page)

Men remain underrepresented in behavioral weight loss trials and are more difficult to recruit compared to women. We describe recruitment response of men and women into a mixed-gender behavioral weight loss trial conducted within 36 rural primary care clinics. Participants were recruited through primary care clinics via direct mailings ( n = 15,076) and in-clinic referrals by their primary care provider (PCP). Gender differences were examined in response rate to direct mailings, study referral source, and rates of proceeding to study screening, being eligible, and enrolling. Men had a lower response rate to direct mailings than women (7.8% vs. 17.7%, p < .001). Men (vs. women) responding to the mailing were more likely to respond by opt-in postcard (64.6% vs. 56.8%) and less likely to respond by phone (33.9% vs. 39.6%), p = .002. Among potential participants contacting the study ( n = 2413), men were less likely to report being referred by PCPs (15.2% vs. 21.6%; p < .001), but were just as likely to proceed to screening, be eligible, and enroll. Men and women were more likely to proceed to screening when referred by PCPs (93.3% vs. 95.4%) compared to direct mailings (74.2% vs. 73.9%). Enrolled men were older ( p < .001), more likely to be married ( p = .04), and had higher levels of education ( p = .01). Men were less likely than women to respond to direct mailings and to be referred by their PCP, but after contacting the study, had similar screening, eligibility, and enrollment rates. Encouraging and training providers to refer men during clinic visits may help recruit more men into primary care-based weight loss trials.

Purpose: Rural residents have higher obesity rates and need access to treatment.Travel burden may limit use and effectiveness of clinic-based behavioral weight loss treatment, which includes frequent visits. This paper examines the impact of travel distance and time on visit attendance and weight loss during a 2-year cluster randomized trial.Methods: Thirty-six primary care practices were randomized to 1 of 3 methods of providing behavioral weight loss counseling: individual clinic visits, group clinic visits, or group phone visits. Participants in the clinic visit conditions were included in this secondary analysis (n = 875). Travel distance and time was measured using ArcGIS and categorized as <15 miles (and minutes) or ≥15 miles (and minutes). Percent visit attendance and percent weight loss were measured 6 and 24 months.Findings: Participants traveling ≥15 miles (n = 211) had lower attendance than those traveling <15 miles (n = 664) for individual clinic visits, both in the first 6 months (81.4% vs 89.1%, respectively; P = .005) and between 7 and 24 months (52.8% vs 61.6%, respectively; P = .02). Travel time revealed similar findings. Neither travel distance nor time had an effect on attendance to group clinic visits. Travel distance and time were not significantly associated with weight loss for either individual or group visits. Conclusion:Travel burden to clinics was associated with visit attendance for individual visits but not for group visits and was not significantly associated with weight loss.Assessment of travel burden as a potential barrier to clinic-based weight loss interventions should be based on local empirical data.