No abstract

New approaches, such as partnerships with existing community organizations and expanded use of telehealth interventions, are needed to provide rural communities with greater access to effective weight-management programs such as the National Diabetes Prevention Program. Policy: Funding for the National Diabetes Prevention Program should earmark support for rural communities and other populations disproportionately affected by obesity-related health conditions. Research: Ongoing dissemination and implementation research is needed to evaluate the effectiveness and cost efficiency of telehealth interventions for weight management and diabetes prevention in rural communities.

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.

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 nointervention 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.

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.