Technology may improve self‐monitoring adherence and dietary changes in weight loss treatment. Our study aimed to investigate whether using a personal digital assistant (PDA) with dietary and exercise software, with and without a feedback message, compared to using a paper diary/record (PR), results in greater weight loss and improved self‐monitoring adherence. Healthy adults (N = 210) with a mean BMI of 34.01 kg/m2 were randomized to one of three self‐monitoring approaches: PR (n = 72), PDA with self‐monitoring software (n = 68), or PDA with self‐monitoring software and daily feedback messages (PDA+FB, n = 70). All participants received standard behavioral treatment. Self‐monitoring adherence and change in body weight, waist circumference, and diet were assessed at 6 months; retention was 91%. All participants had a significant weight loss (P < 0.01) but weight loss did not differ among groups. A higher proportion of PDA+FB participants (63%) achieved ≥5% weight loss in comparison to the PR group (46%) (P < 0.05) and PDA group (49%) (P = 0.09). Median percent self‐monitoring adherence over the 6 months was higher in the PDA groups (PDA 80%; PDA+FB 90%) than in the PR group (55%) (P < 0.01). Waist circumference decreased more in the PDA groups than the PR group (P = 0.02). Similarly, the PDA groups reduced energy and saturated fat intake more than the PR group (P < 0.05). Self‐monitoring adherence was greater in the PDA groups with the greatest weight change observed in the PDA+FB group.

Background Self-monitoring for weight loss has traditionally been performed with paper diaries. Technologic advances could reduce the burden of self-monitoring and provide feedback to enhance adherence. Purpose To determine if self-monitoring diet using a PDA only or the PDA with daily tailored feedback (PDA+FB), was superior to using a paper diary on weight loss and maintenance. Design The Self-Monitoring and Recording Using Technology (SMART) Trial was a 24-month RCCT; participants were randomly assigned to one of three self-monitoring groups. Setting/participants From 2006 to 2008, 210 overweight/obese adults (84.8% female, 78.1% white) were recruited from the community. Data were analyzed in 2011. Intervention Participants received standard behavioral treatment for weight loss which included dietary and physical activity goals, encouraged the use of self-monitoring, and was delivered in group sessions. Main outcome measures Percentage weight change at 24 months, adherence to self-monitoring over time. Results Study retention was 85.6%. The mean percentage weight loss at 24 months was not different among groups (paper diary: −1.94% [95% CI= −3.88, 0.01], PDA: −1.38% [95% CI= – 3.38, 0.62], PDA+FB: –2.32% [95% CI= –4.29, −0.35]); only the PDA+FB group (p=0.02) demonstrated a significant loss. For adherence to self-monitoring, there was a time-by-treatment group interaction between the combined PDA groups and the paper diary group (p=0.03) but no difference between PDA and PDA+FB groups (p=0.49). Across all groups, weight loss was greater for those who were adherent ≥60% versus <30% of the time, p<0.001. Conclusions PDA+FB use resulted in a small weight loss at 24 months; PDA use resulted in greater adherence to dietary self-monitoring over time. However, for sustained weight loss, adherence to self-monitoring is more important than the method used to self-monitor. A daily feedback message delivered remotely enhanced adherence and improved weight loss, which suggests that technology can play a role in improving weight loss.

BackgroundEcological momentary assessment (EMA) assesses individuals’ current experiences, behaviors, and moods as they occur in real time and in their natural environment. EMA studies, particularly those of longer duration, are complex and require an infrastructure to support the data flow and monitoring of EMA completion.ObjectiveOur objective is to provide a practical guide to developing and implementing an EMA study, with a focus on the methods and logistics of conducting such a study.MethodsThe EMPOWER study was a 12-month study that used EMA to examine the triggers of lapses and relapse following intentional weight loss. We report on several studies that informed the implementation of the EMPOWER study: (1) a series of pilot studies, (2) the EMPOWER study’s infrastructure, (3) training of study participants in use of smartphones and the EMA protocol and, (4) strategies used to enhance adherence to completing EMA surveys.ResultsThe study enrolled 151 adults and had 87.4% (132/151) retention rate at 12 months. Our learning experiences in the development of the infrastructure to support EMA assessments for the 12-month study spanned several topic areas. Included were the optimal frequency of EMA prompts to maximize data collection without overburdening participants; the timing and scheduling of EMA prompts; technological lessons to support a longitudinal study, such as proper communication between the Android smartphone, the Web server, and the database server; and use of a phone that provided access to the system’s functionality for EMA data collection to avoid loss of data and minimize the impact of loss of network connectivity. These were especially important in a 1-year study with participants who might travel. It also protected the data collection from any server-side failure. Regular monitoring of participants’ response to EMA prompts was critical, so we built in incentives to enhance completion of EMA surveys. During the first 6 months of the 12-month study interval, adherence to completing EMA surveys was high, with 88.3% (66,978/75,888) completion of random assessments and around 90% (23,411/25,929 and 23,343/26,010) completion of time-contingent assessments, despite the duration of EMA data collection and challenges with implementation.ConclusionsThis work informed us of the necessary preliminary steps to plan and prepare a longitudinal study using smartphone technology and the critical elements to ensure participant engagement in the potentially burdensome protocol, which spanned 12 months. While this was a technology-supported and -programmed study, it required close oversight to ensure all elements were functioning correctly, particularly once human participants became involved.

Introduction Weight loss has been associated with higher physical activity (PA) levels and frequent dietary self-monitoring. Less is known about how PA self-monitoring affects adherence to PA goals, PA levels and weight change. Methods The SMART Trial is a clinical weight loss trial in which 210 overweight adults were randomized equally to one of three arms: 1) paper record (PR); 2) personal digital assistant with self-monitoring software (PDA); and 3) PDA with daily tailored feedback message (PDA+FB). PA self-monitoring and adherence to PA goals were based on entries in weekly submitted diaries. PA levels were measured via self-report by the past 6 month Modifiable Activity Questionnaire at baseline and 6 months. Results Data are presented on 189 participants with complete 6-month PA data [84% female, 77% White, mean age: 47.3 ± 8.8 years, mean BMI: 34.1 ± 4.5 kg/m2]. Median PA level was 7.96 MET-hr-wk−1 at baseline and 13.4 MET-hr-wk−1 at 6 months, with significant PA increases in all three arms. PDA+FB arm had a higher mean number of weekly self-monitoring entries than the PR arm (3.4 vs. 2.4; p=0.003) and were more likely to maintain high (i.e., 100%) adherence to PA goals over time than the PDA (p=0.02) or PR arms (p=0.0003). Both PA self-monitoring and adherence to PA goals were related to higher PA levels at 6 months. A higher mean rate of PA self-monitoring was associated with a greater percentage of weight decrease (rho=−0.49; p<0.0001) at 6 months. Conclusions PA self-monitoring and adherence to PA goals were more likely in participants in the PDA+FB arm and in turn predicted higher PA levels and weight loss.

Findings from studies examining self-efficacy and its relationship to weight loss have been inconsistent. We examined self-efficacy specific to changing eating behaviors in the PREFER trial, an 18-month behavioral weight-loss study, to determine if self-efficacy and dietary adherence were associated with weight change, and what impact self-efficacy had on weight change after controlling for adherence. Measurements included the weight efficacy lifestyle (WEL) questionnaire, body weight, self-reported fat gram intake, kilocalorie intake, and adherence to kilocalorie and fat gram goals at baseline, 6, 12, and 18 months. The sample (N = 170) was 88.2% female and 70.0% Caucasian; the mean age was 44.1 years (SD = 8.8). Mean weight loss at 18 months was 4.64% (SD = 6.24) of baseline body weight and the mean increase in self-efficacy was 11.70% (SD = 38.61). Self-efficacy improved significantly over time (p = 0.04) and was associated with weight loss (p = 0.02). Adherence to the fat gram goal was associated with weight loss (p = 0.0003), and self-efficacy remained associated with weight loss after controlling for fat gram adherence (p = 0.0001). Consistent with self-efficacy theory, improvement in self-efficacy over time supported greater weight loss. Adherence to the fat gram goal also influenced weight loss.