Perceptions of an acceptance-based weight management treatment among adults living in rural areas.

Obesity and obesity-related comorbidities disproportionately affect rural communities. Research has emerged in support of a novel acceptance-based behavioural weight management treatment (ABT) that integrates the principles and procedures of acceptance-commitment therapy (ACT) with traditional components of standard behavioural treatment (SBT). The current study assessed the perceptions of community stakeholders in rural areas to session materials of a commercially available ABT program. Surveys and focus groups were used to solicit feedback from three former interventionists with experience delivering SBTs in rural counties and from 17 former participants in these programs. Qualitative responses encompassed four overarching themes: (1) recommendations to support participant engagement, (2) comments about preferences for specific ABT and SBT strategies, (3) concerns about specific aspects of treatment delivery, and (4) requests for aesthetic changes to session materials to enhance clarity and engagement. Overall, participants viewed ABT materials and concepts favourably but believed it would be important to begin the intervention with rapport building and training in traditional SBT strategies prior to delving into ACT strategies. Future studies should investigate the efficacy of ABT for weight loss in adults with obesity living in rural communities and continue to solicit feedback from rural community stakeholders.

Consistency With and Disengagement From Self-monitoring of Weight, Dietary Intake, and Physical Activity in a Technology-Based Weight Loss Program: Exploratory Study.

BACKGROUND: Digital self-monitoring tools offer promise to improve adherence to self-monitoring of weight and weight-related behaviors; however, less is known regarding the patterns of participant consistency and disengagement with these tools. OBJECTIVE: This study characterizes the consistency of use and time to disengagement with digital self-monitoring tools during a 6-month weight loss intervention and investigates whether the provision of phone-based intervention improved self-monitoring adherence. METHODS: Participants were 54 adults with overweight or obesity (mean age 49.6 years, SD 12.4 years; mean BMI 32.6 kg/m2, SD 3.2 kg/m2) enrolled in a pilot trial assessing the impact of self-monitoring technology (Fitbit Zip, Aria scale, and smartphone app), with and without additional interventionist contact, on weight loss. All participants received weight loss education and were asked to self-monitor weight, dietary intake, and physical activity daily throughout the 6-month program. Consistency was defined as the number of weeks that participants adhered to self-monitoring recommendations (7 out of 7 days). Disengagement was defined as the first of 2 consecutive weeks that the 7-day self-monitoring adherence goal was not met. Wilcoxon signed-rank tests were used to examine differences in consistency and disengagement by behavioral targets. t tests (2-tailed) and Cox proportional hazards models were used to examine whether providing additional interventionist contact would lead to significant improvements in consistency and time to disengagement from self-monitoring tools, respectively. Linear regressions were used to examine associations between consistency, time to disengagement, and weight loss. RESULTS: Participants consistently self-monitored physical activity for more weeks (mean 17.4 weeks, SD 8.5 weeks) than weight (mean 11.1 weeks, SD 8.5 weeks) or dietary intake (mean 10.8 weeks, SD 8.7 weeks; P<.05). Similarly, participants had a significantly longer time to disengagement from self-monitoring of physical activity (median 19.5 weeks) than weight (4 weeks) or dietary intake (10 weeks; P<.001). Participants randomized to receive additional interventionist contact had significantly greater consistency and longer time to disengagement for self-monitoring of dietary intake compared with participants who did not (P=.006); however, there were no statistically significant differences between groups for self-monitoring of weight or physical activity (P=.24 and P=.25, respectively). Greater consistency and longer time to disengagement were associated with greater weight loss for self-monitoring of weight and dietary intake (P<.001 and P=.004, respectively) but not for physical activity (P=.57). CONCLUSIONS: Results demonstrated that self-monitoring adherence differed by behavioral target, with greater consistency and longer time to disengagement associated with lower-burden tools (ie, self-monitoring of physical activity). Consistent with supportive accountability theory, additional interventionist contact improved consistency and lengthened time to disengagement from self-monitoring of dietary intake. Given the observed associations between consistency, disengagement, and weight loss outcomes, it is important to identify additional methods of increasing consistency and engagement with digital self-monitoring tools.

Food reward sensitivity, impulsivity, and weight change during and after a 3-month weight loss program.

BACKGROUND: Greater sensitivity to food rewards and higher levels of impulsivity (and an interaction between these variables, termed "reinforcement pathology") have been associated with obesity in cross-sectional studies. Less is known regarding how these constructs may impact attempts at weight loss or longer-term weight loss maintenance. METHODS: We provided 75 adults (69%Female, 84%White, age = 50.8y, BMI = 31.2kg/m2) with a 3-month Internet-based weight loss program and assessed weight, food reward sensitivity (via the Power of Food Scale [PFS]), and impulsivity (via Go No-Go [GNG] and Delay Discounting [DD] computer tasks) at baseline and at Months 3, 6, 9, and 12. No additional intervention was provided Months 3-12. Multi-level mixed-effect models were used to examine changes in PFS, GNG, and DD over time and associations between these measures and weight loss/regain. RESULTS: Participants lost 6.0±1.1kg Months 0-3 and regained 2.4±1.1kg Months 3-12. Across time points, higher PFS scores were associated with higher weight, p = .007; however, there were no significant associations between GNG or DD and weight nor between the interactions of PFS and GNG or DD and weight, ps>.05. There were significant decreases from Months 0-3 in PFS, GNG, and DD, ps < .05; however, neither baseline values nor changes were significantly associated with weight change and there were no significant associations between the interactions of PFS and GNG or DD and weight change, ps>.05. CONCLUSION: Results demonstrated an association between food reward sensitivity and weight. Further, decreases in both food reward sensitivity and impulsivity were observed during an initial weight loss program, but neither baseline levels nor improvements were associated with weight change. Taken together, results suggest that the constructs of food reward sensitivity, impulsivity, and reinforcement pathology may have limited clinical utility within behavioral weight management interventions. Future intervention studies should examine whether food-related impulsivity tasks lead to a similar pattern of results.

Associations between effort, importance, and self-monitoring during and after a 12-week behavioural weight management program.

OBJECTIVE: Self-monitoring of weight and caloric intake has been associated with improved weight loss and weight loss maintenance in behavioural weight loss programs; however, participants' adherence to self-monitoring tends to decrease over time. To identify potential barriers to self-monitoring adherence, the current study examined week-to-week associations between ratings of perceived effort, relative importance of weight loss goals, and adherence to self-monitoring of weight and caloric intake during and after a behavioural weight loss programme. METHOD: Participants were 74 adults with overweight and obesity enrolled in a 12-week, Internet-based weight loss programme followed by a 40-week "maintenance" period during which no additional intervention was provided. Participants self-reported adherence to self-monitoring and completed ratings of effort and importance on a study website weekly throughout the study period (1 year). RESULTS: Longitudinal multilevel models demonstrated that higher ratings of effort were associated with fewer days of self-monitoring of weight, ß = -0.100, p < .0001, and caloric intake, ß = -0.300, p < .0001. Conversely, higher ratings of importance were associated with more frequent self-monitoring of weight, ß = 0.360, p < .0001, and caloric intake, ß = 0.742, p < .0001. Moreover, the magnitude of these associations were stronger during the maintenance period than during initial intervention, ps < .01. CONCLUSIONS: Perceptions of effort and importance are both independently associated with adherence to self-monitoring weight and caloric intake, and this effect appears to be stronger after the end of initial intervention. Future research should investigate whether tailoring intervention content based on these constructs can improve adherence to self-monitoring.

Frequency and Consistency of Self-Weighing to Promote Weight-Loss Maintenance.

OBJECTIVE: This study aimed to investigate the roles of frequency and consistency of self-weighing in promoting weight-loss maintenance. METHODS: Participants were 74 adults who completed a 3-month internet-based weight-loss program followed by a 9-month no-intervention maintenance period. Frequency of self-weighing was defined as the number of days that participants self-weighed during the maintenance period via a study-provided smart scale. Consistency was defined as the number of weeks that participants self-weighed at a certain frequency, with multiple minimum thresholds examined. Hierarchical regression analyses were used to assess associations among frequency, consistency, and weight change during the maintenance period. RESULTS: Greater consistency was significantly associated with less weight regain when defined as the number of weeks that participants self-weighed on ≥6 d/wk or 7 d/wk (P values < 0.05). Contrary to hypotheses, frequency was not associated with weight change (P = 0.141), and there was not a significant interaction between frequency and consistency. CONCLUSIONS: Results demonstrate that consistency of self-weighing may be more important than total frequency for preventing weight regain after the end of a weight-loss program. Further, results suggest that a high level of consistency (self-weighing for ≥6 d/wk or 7 d/wk) may be necessary to promote successful weight-loss maintenance.

Acceptance-based therapy: the potential to augment behavioral interventions in the treatment of type 2 diabetes.

Diabetes is a complex and multifactorial disease affecting more than 415 million people worldwide. Excess adiposity and modifiable lifestyle factors, such as unhealthy dietary patterns and physical inactivity, can play a significant role in the development of type 2 diabetes. Interventions that implement changes to lifestyle behaviors, in addition to pharmacological treatment, may attenuate the development and worsening of diabetes. This narrative review delineates how standard behavioral interventions (SBTs), based in "first wave" behavioral therapies and "second wave" cognitive behavioral therapies, serve as the foundation of diabetes treatment by supporting effective lifestyle changes, including improving adherence to healthful behaviors, medication, and self-monitoring regimens. Moreover, "third wave" "acceptance-based therapies" (ABTs), which integrate techniques from acceptance and commitment therapy, are proposed as a potential novel treatment option for diabetes management. Further research and long-term, randomized controlled trials will clarify the feasibility, acceptability, and effectiveness of ABT for improving glucose control via enhancing medication adherence and promoting effective lifestyle changes in people with diabetes.

Rural/urban disparities in access to the National Diabetes Prevention Program.

Residents of rural communities generally have limited access to preventive health services such as lifestyle programs for weight management. In 2009, the U.S. Congress authorized the Centers for Disease Control and Prevention (CDC) to partner with local community organizations to disseminate the Diabetes Prevention Program (DPP), an evidence-based lifestyle intervention for weight management. Given that the National DPP (NDPP) was designed to broaden nationwide access to weight-loss treatment for adults at high risk for developing diabetes, the present study examined the implementation of the NDPP in rural and urban counties across the USA. The names and locations of NDPP community partnership sites were collected from the CDC website and cross-referenced with the U.S. Census Bureau's classification of counties as rural versus urban. Results showed that overall 27.9% of the 3,142 counties in the USA contained one or more NDPP partnership sites. However, significantly fewer rural counties had access to a NDPP site compared with urban counties (14.6% vs. 48.4%, respectively, p < .001). This disparity was evident across all types of partnership sites (ps < .001). These findings indicate that implementation of the NDPP has expanded the overall availability of evidence-based weight-management programs across the USA. However, this increase has been disproportionately greater for urban counties versus rural counties, thereby widening the rural/urban disparity in access to preventive health services. Alternative dissemination strategies that address the special barriers to implementation faced by rural communities are needed to increase access to the NDPP.

Accuracy of Self-Report Versus Objective Smart-Scale Weights During a 12-Week Weight Management Intervention.

OBJECTIVE: Greater frequency of self-weighing has been associated with greater weight loss in weight management interventions, but little is known regarding the accuracy of self-reported weight data. METHODS: Agreement between objective smart-scale and self-reported weight data was assessed in 74 adults (age = 50.7 years; BMI = 31.2 kg/m2 ) enrolled in a 12-week, Internet-based weight management program. Participants were asked to self-weight daily using a study-provided smart scale and to self-report weights via the study website. RESULTS: There was strong agreement between smart-scale and self-reported weight values (intraclass correlation = 0.982) but only moderate agreement regarding frequency of self-weighing assessed via each method (κ = 0.491; P < 0.0001). Greater self-weighing frequency was associated with greater weight loss across measures (all P < 0.001). Compared with days when participants did both, weights were 0.66 kg higher on days when participants self-weighed via the smart scale but did not self-report weight (8% of days) and 0.58 kg higher on days when they self-reported weight but did not self-weigh via the smart scale (4% of days; all P < 0.0001). CONCLUSIONS: Results suggest that self-reported weight values are similar to smart-scale measurements; however, either method alone may underestimate self-weighing frequency. Furthermore, missing self-weighing data should not be treated as ignorable because weights may be higher than those observed on nonmissing days.

Anxiety sensitivity and working memory capacity: Risk factors and targets for health behavior promotion.

Understanding the nature and influence of specific risk profiles is increasingly important for health behavior promotion. The purpose of this article is to document the value of two factors-anxiety sensitivity (AS) and working memory capacity (WMC)-for enhancing risk for the initiation and/or maintenance of a range of negative health behaviors. AS is a distress-related risk factor that potentiates avoidance/coping motivations for negative health behaviors. Stress provides the conditions for negative somatic and affective states, and AS amplifies the aversiveness of these experiences and correspondingly hinders adaptive functioning. In contrast, low WMC is hypothesized to exert its effect by decreasing the capacity to filter out current temptations, attenuating a focus on longer-term goals and impairing the application of relevant coping skills at times of stress. In this review, we provide conceptual models for the separate roles of high AS and low WMC in negative health behaviors, review the influence of these factors on specific health behavior exemplars (eating behaviors/obesity, physical activity, smoking, alcohol use, and sleep promotion), provide preliminary evidence for their value as independent treatment targets for health-behavior promotion, and encourage specific research directions in relation to these variables.