College students exhibit high levels of sedentary time and/or poor lifestyle factors (e.g., poor sleep, stress, physical inactivity). It is unknown; however, in what domains college students spend their sedentary time and whether there are associations between sedentary time and these lifestyle factors. This study examined sedentary behavior of college students by domains, current lifestyle factors and sociodemographics. Undergraduates (n = 272, M age = 20 years, 79% female) self-reported their sedentary behavior, sleep, stress, physical activity, anthropometrics and sociodemographics. Sedentary time was categorized as: total, recreational screen, education and social. Students reported spending > 12 h of their day sedentary on average, with over a third of this time spent in recreational screen time. All categories of sedentary time were significantly correlated with body mass index, and both total sedentary time and screen time were significantly correlated with sleep score, with poorer sleep quality associated with greater sedentary time. Physical activity was negatively correlated with social sedentary time only. Subgroups with elevated sedentary time included minority students, those with low parental education and students with overweight/obesity. Given the negative health impacts of sedentary behavior, college students would likely benefit from interventions tailored to this population which target reducing sedentary time, particularly recreational screen time.

Objective: This study evaluated whether the transition of a face‐to‐face behavioral intervention to videoconferencing‐based telehealth delivery during the COVID‐19 pandemic resulted in significantly smaller weight losses than those typically observed in gold‐standard, face‐to‐face programs.Methods: Participants were 160 adults with obesity (mean [SD] age = 49.2 [11.9] years, BMI = 36.1 [4.2] kg/m²) enrolled in two cohorts of a 16‐week comprehensive weight‐management program. Cohort 1 began in person and transitioned to telehealth (Zoom) delivery during week 11 of the intervention because of COVID‐19; Cohort 2 was conducted completely remotely. A noninferiority approach (using a clinically relevant noninferiority margin of 2.5%) was used to assess whether the weight losses observed were inferior to the 8% losses from baseline typically produced by gold‐standard, face‐to‐face lifestyle interventions.Results: From baseline to postintervention, participants lost an average of 7.4 [4.9] kg, representing a reduction of 7.2% [4.6%]. This magnitude of weight change was significantly greater than 5.5% (t[159] = 4.7, p < 0.001), and, thus, was within the proposed noninferiority margin.Conclusions: These findings demonstrate that the results of behavioral weight‐management interventions are robust, whether delivered in person or remotely, and that individuals can achieve clinically meaningful benefits from behavioral treatment even during a global pandemic. Pragmatic “lessons learned,” including modified trial recruitment techniques, are discussed.

Previous studies have examined the ability of the Fitbit to measure physical activity compared to research-grade accelerometers. However, few have examined whether Fitbits accurately measure sedentary behavior. This study examined whether the Fitbit Charge 3 adequately quantifies sedentary behavior compared to the gold standard in objectively measured sedentary behavior assessment, the activPAL. Eleven adults wore a Fitbit Charge 3 and activPAL device for 14 days and self-reported their sedentary behavior each week. ActivPAL epoch data were summed into minute-by-minute data and processed with two cutpoints (activPAL_Half and activPAL_Full) to compare to Fitbit data. Paired t-tests were used to examine differences between the two devices for sedentary behavior variables. Intraclass correlations were used to examine device agreement. There was no significant difference in sedentary time between activPAL_Half and Fitbit data, but activPAL_Full estimated significantly lower sedentary time than Fitbit. Intraclass correlations showed high agreement. We suggest that Fitbit could replace activPAL when measuring total sedentary time.

Self-monitoring of weight, dietary intake, and physical activity is a key strategy for weight management in adults with obesity. Despite research suggesting consistent associations between more frequent self-monitoring and greater success with weight regulation, adherence is often suboptimal and tends to decrease over time. New technologies such as smartphone applications, e-scales, and wearable devices can help eliminate some of the barriers individuals experience with traditional self-monitoring tools, and research has demonstrated that these tools may improve self-monitoring adherence. To improve the integration of these tools in clinical practice, the current narrative review introduces the various types of self-monitoring technologies, presents current evidence regarding their use for nutrition support and weight management, and provides guidance for optimal implementation. The review ends with a discussion of barriers to the implementation of these technologies and the role that they should optimally play in nutritional counseling and weight management. Although newer self-monitoring technologies may help improve adherence to self-monitoring, these tools should not be viewed as an intervention in and of themselves and are most efficacious when implemented with ongoing clinical support.

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.

Objectives: In this study, we sought to characterize the weight status, sedentary behavior, and physical activity of caregivers of individuals with Alzheimer's disease. Methods: In 2014, we surveyed caregivers of individuals with Alzheimer's disease from the South Carolina Alzheimer's Disease Registry (N = 47) about their personal health behaviors. Additionally, a subset of individuals (N = 14) wore an accelerometer for 7 days. Results: Caregivers (N = 47) were mostly overweight or obese (85%) and self-reported a daily average sedentary time of 246.5 ± 203.0 minutes and 455.8 ± 291.4 minutes, as measured by 2 questionnaires. Objective measures indicated that persons spent an average of 769.4 ± 167.6 minutes per day (77.8% of their waking day) engaged in sedentary behavior. Conclusion: Given the negative health outcomes associated with both obesity and sedentary behavior, this is a vulnerable population that likely would benefit from interventions focused on weight management and reducing sedentary behavior.