scholarly journals The Effect of Electronic Self-Monitoring on Weight Loss and Dietary Intake: A Randomized Behavioral Weight Loss Trial

Obesity ◽  
2011 ◽  
Vol 19 (2) ◽  
pp. 338-344 ◽  
Author(s):  
Lora E. Burke ◽  
Molly B. Conroy ◽  
Susan M. Sereika ◽  
Okan U. Elci ◽  
Mindi A. Styn ◽  
...  
Keyword(s):  
Weight Loss ◽  
Dietary Intake ◽  
Behavioral Weight Loss ◽  
Self Monitoring

scholarly journals Long-Term Evaluation of Counselor Efficiency at Providing Feedback in a Technology-Based Behavioral Weight Loss Intervention (Preprint)

2020 ◽  
Author(s):  
Margaret Fahey ◽  
Robert C. Klesges ◽  
Mehmet Kocak ◽  
Leslie Gladney ◽  
Gerald W. Talcott ◽  
...  
Keyword(s):  
Weight Loss ◽  
Electronic Mail ◽  
Detailed Examination ◽  
Monitoring Data ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Future Technology ◽  
Study Implementation ◽  
Weight Loss Interventions

BACKGROUND Feedback for participants’ self-monitoring is a crucial, and costly, component of technology-based weight loss interventions. Detailed examination of interventionist time when reviewing and providing feedback for online self-monitoring data is unknown. OBJECTIVE Study purpose was to longitudinally examine time counselors spent providing feedback on participant self-monitoring data (i.e., diet, physical activity, weight) in a 12-month technology-based weight loss intervention. We hypothesized that counselors would deliver feedback to participants more quickly over time. METHODS Time counselors (N=10) spent reviewing and providing feedback to participants via electronic mail (e-email) was longitudinally examined for all counselors across the three years of study implementation. Descriptives were observed for counselor feedback duration across counselors by 12 annual quarters (i.e., three-month periods). Differences in overall duration times by each consecutive annual quarter were analyzed using Wilcoxon-Mann-Whitney tests. RESULTS There was a decrease in counselor feedback duration from first to second quarter [Mean (M) = 53 to 46 minutes], and from second to third (M= 46 to 30). A trend suggested a decrease from third to fourth quarters (M = 30 to 26), but no changes were found in subsequent quarters. Consistent with hypothesis, counselors increased their efficiency in providing feedback. Across 12-months, mean time counselors needed to review participant self-monitoring and provide feedback decreased from 53 to 26 minutes. CONCLUSIONS Counselors needed increasingly less time to review online self-monitoring data and provide feedback after the initial nine months of study implementation. Results inform counselor costs for future technology-based behavioral weight loss interventions. For example, regardless of increasing counselor efficiency, 25-30 minutes per feedback message is a high cost for interventions. One possibility for reducing costs would be generating computer-automated feedback. CLINICALTRIAL NCT02063178

scholarly journals Using the Habit App for Weight Loss Problem Solving: Development and Feasibility Study (Preprint)

2018 ◽  
Author(s):  
Sherry Pagoto ◽  
Bengisu Tulu ◽  
Emmanuel Agu ◽  
Molly E Waring ◽  
Jessica L Oleski ◽  
...  
Keyword(s):  
Weight Loss ◽  
Problem Solving ◽  
Mobile Apps ◽  
Mobile App ◽  
Behavioral Weight Loss ◽  
Problem Solving Therapy ◽  
Pilot Studies ◽  
Self Monitoring ◽  
Problem Solving Skills ◽  
Automated Delivery

BACKGROUND Reviews of weight loss mobile apps have revealed they include very few evidence-based features, relying mostly on self-monitoring. Unfortunately, adherence to self-monitoring is often low, especially among patients with motivational challenges. One behavioral strategy that is leveraged in virtually every visit of behavioral weight loss interventions and is specifically used to deal with adherence and motivational issues is problem solving. Problem solving has been successfully implemented in depression mobile apps, but not yet in weight loss apps. OBJECTIVE This study describes the development and feasibility testing of the Habit app, which was designed to automate problem-solving therapy for weight loss. METHODS Two iterative single-arm pilot studies were conducted to evaluate the feasibility and acceptability of the Habit app. In each pilot study, adults who were overweight or obese were enrolled in an 8-week intervention that included the Habit app plus support via a private Facebook group. Feasibility outcomes included retention, app usage, usability, and acceptability. Changes in problem-solving skills and weight over 8 weeks are described, as well as app usage and weight change at 16 weeks. RESULTS Results from both pilots show acceptable use of the Habit app over 8 weeks with on average two to three uses per week, the recommended rate of use. Acceptability ratings were mixed such that 54% (13/24) and 73% (11/15) of participants found the diet solutions helpful and 71% (17/24) and 80% (12/15) found setting reminders for habits helpful in pilots 1 and 2, respectively. In both pilots, participants lost significant weight (P=.005 and P=.03, respectively). In neither pilot was an effect on problem-solving skills observed (P=.62 and P=.27, respectively). CONCLUSIONS Problem-solving therapy for weight loss is feasible to implement in a mobile app environment; however, automated delivery may not impact problem-solving skills as has been observed previously via human delivery. CLINICALTRIAL ClinicalTrials.gov NCT02192905; https://clinicaltrials.gov/ct2/show/NCT02192905 (Archived by WebCite at http://www.webcitation.org/6zPQmvOF2)

The Impact of Self-monitoring of Blood Glucose on a Behavioral Weight Loss Intervention for Patients With Type 2 Diabetes

2012 ◽  
Vol 39 (3) ◽  
pp. 397-405 ◽  
Author(s):  
Lisa M. McAndrew ◽  
Melissa A. Napolitano ◽  
Leonard M. Pogach ◽  
Karen S. Quigley ◽  
Kerri Leh Shantz ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Blood Glucose ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Behavioral Weight Loss Intervention ◽  
The Impact

Discrepancies Between Clinician and Participant Intervention Adherence Ratings Predict Percent Weight Change During a Six-Month Behavioral Weight Loss Intervention

2021 ◽  
Vol 11 (4) ◽  
pp. 1006-1014
Author(s):  
Michael P Berry ◽  
Elisabeth M Seburg ◽  
Meghan L Butryn ◽  
Robert W Jeffery ◽  
Melissa M Crane ◽  
...  
Keyword(s):  
Weight Loss ◽  
Weight Change ◽  
Weight Control ◽  
Calorie Intake ◽  
Behavioral Weight Loss ◽  
Weight Loss Treatment ◽  
Self Monitoring ◽  
Weight Control Behaviors ◽  
Behavioral Weight Loss Intervention ◽  
Behavioral Weight Loss Treatment

Abstract Background Individuals receiving behavioral weight loss treatment frequently fail to adhere to prescribed dietary and self-monitoring instructions, resulting in weight loss clinicians often needing to assess and intervene in these important weight control behaviors. A significant obstacle to improving adherence is that clinicians and clients sometimes disagree on the degree to which clients are actually adherent. However, prior research has not examined how clinicians and clients differ in their perceptions of client adherence to weight control behaviors, nor the implications for treatment outcomes. Purpose In the context of a 6-month weight-loss treatment, we examined differences between participants and clinicians when rating adherence to weight control behaviors (dietary self-monitoring; limiting calorie intake) and evaluated the hypothesis that rating one’s own adherence more highly than one’s clinician would predict less weight loss during treatment. Methods Using clinician and participant-reported measures of self-monitoring and calorie intake adherence, each assessed using a single item with a 7- or 8-point scale, we characterized discrepancies between participant and clinician adherence and examined associations with percent weight change over 6 months using linear mixed-effects models. Results Results indicated that ratings of adherence were higher when reported by participants and supported the hypothesis that participants who provided higher adherence ratings relative to their clinicians lost less weight during treatment (p < 0.001). Conclusions These findings suggest that participants in weight loss treatment frequently appraise their own adherence more highly than their clinicians and that participants who do so to a greater degree tend to lose less weight.

scholarly journals Binge eating predicts adherence to digital self-monitoring during behavioral weight loss

2020 ◽  
Vol 39 ◽  
pp. 101448
Author(s):  
Mary K. Martinelli ◽  
Laura D'Adamo ◽  
Meghan L. Butryn
Keyword(s):  
Weight Loss ◽  
Binge Eating ◽  
Behavioral Weight Loss ◽  
Self Monitoring

scholarly journals Describing Transitions in Adherence to Physical Activity Self-monitoring and Goal Attainment in an Online Behavioral Weight Loss Program: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Melissa Lee Stansbury ◽  
Jean R Harvey ◽  
Rebecca A Krukowski ◽  
Christine A Pellegrini ◽  
Xuewen Wang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Goal Attainment ◽  
Vigorous Physical Activity ◽  
Secondary Analysis ◽  
Individual Characteristics ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Program Goal ◽  
Monitoring Behavior

BACKGROUND Standard behavioral weight loss interventions often set uniform physical activity (PA) goals and promote PA self-monitoring; however, adherence remains a challenge and recommendations may not accommodate all individuals. Identifying patterns of PA goal attainment and self-monitoring behavior will offer a deeper understanding of how individuals adhere to different types of commonly prescribed PA recommendations (ie., minutes of moderate-to-vigorous physical activity [MVPA] and daily steps) and guide future recommendations for improved intervention effectiveness. OBJECTIVE This study examined weekly patterns of adherence to steps-based and minutes-based PA goals and self-monitoring behavior during a 6-month online behavioral weight loss intervention. METHODS Participants were prescribed weekly PA goals for steps (7,000 to 10,000 steps/day) and minutes of MVPA (50 to 200 minutes/week) as part of a lifestyle program. Goals gradually increased during the initial 2 months, followed by 4 months of fixed goals. PA was self-reported daily on the study website. For each week, participants were categorized as “adherent” if they self-monitored their PA and met the program PA goal, “suboptimally adherent” if they self-monitored but did not meet the program goal, or “nonadherent” if they did not self-monitor. The probability of transitioning into a less adherent status was examined using multinomial logistic regression. RESULTS Individuals (N=212) were predominantly middle-aged females with obesity, and 31.6% self-identified as a racial/ethnic minority. Initially, 34.4% were categorized as “adherent” to steps-based goals (51.9% “suboptimally adherent” and 13.7% “nonadherent”), and there was a high probability of either remaining “suboptimally adherent” from week-to-week or transitioning to a “nonadherent” status. On the other hand, 70.3% of individuals started out “adherent” to minutes-based goals (16.0% “suboptimally adherent” and 13.7% “nonadherent”), with “suboptimally adherent” seen as the most variable status. During the graded goal phase, individuals were more likely to transition to a less adherent status for minutes-based goals (OR 1.39, 95% CI 1.31-1.48) compared to steps-based goals (OR 1.24, 95% CI 1.17-1.30); however, no differences were seen during the fixed goal phase (minutes-based goals: OR 1.06, 95% CI 1.05, 1.08 versus steps-based goals: OR 1.07, 95% CI 1.05, 1.08). CONCLUSIONS States of vulnerability to poor PA adherence can emerge rapidly and early in obesity treatment. There is a window of opportunity within the initial two months to bring more people towards “adherent” behavior, especially those who fail to meet the prescribed goals but engage in self-monitoring. While this study describes the probability of adhering to steps-based and minutes-based targets, it will be prudent to determine how individual characteristics and contextual states relate to these behavioral patterns, which can inform how best to adapt interventions. CLINICALTRIAL This study was a secondary analysis of a pre-registered randomized trial (Trial Registration: ClinicalTrials.gov NCT02688621).

scholarly journals Adherence to Self-monitoring of Dietary Intake During a Weight Loss Intervention: Does a Personalized Approach Maintain Adherence? (FS11-04-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Collin Popp ◽  
Mark Butler ◽  
David St-Jules ◽  
Lu Hu ◽  
Paige Illiano ◽  
...  
Keyword(s):  
Weight Loss ◽  
Dietary Intake ◽  
Repeated Measures ◽  
Heart Association ◽  
Behavioral Approach ◽  
Self Monitoring ◽  
Behavioral Counseling ◽  
Weight Loss Programs ◽  
Session Attendance ◽  
Personalized Approach

Abstract Objectives We compared self-monitoring adherence in participants randomized to two weight loss programs: a STANDARDIZED, one-size-fits-all, low-fat diet, or a diet PERSONALIZED to minimize the postprandial glycemic response. Methods Participants were adults with pre-diabetes or type 2 diabetes, and a BMI >27 k/m2. Both groups were instructed to restrict total calories, monitor dietary intake with the Personal Nutrition Program (PNP) smartphone app, and attend videoconference behavioral counseling sessions on the same intervention schedule. STANDARDIZED (n = 12) received app feedback about intake of total calories and dietary fat. PERSONALIZED (n = 20) received app feedback about intake of total calories plus a meal-specific predicted glycemic score. Total meal entries were measured at 1, 2 and 3 months. Self-monitoring adherence was defined as logging >50% of expected meals each month into the PNP app, assuming 3 meals/day. Session attendance was also measured. Repeated measures binomial logistic regression analysis was used to assess change in adherence due to treatment group, time (i.e., months), and the interaction between treatment and time, adjusting for age, gender and BMI. Results Proportion adherent was 75.0%, 41.7% and 8.3% in the STANDARDIZED group and 85.0%, 80.0% and 75.0% in the PERSONALIZED group during months 1, 2 and 3, respectively. The repeated measures model demonstrated a significant effect of month (P < 0.001) and a treatment*month interaction (P = 0.011). After adjusting for covariates, these effects remained significant, showing a significant reduction in odds of adherence by month (OR [95%CI]: 0.13 [0.05, 0.37]; P < 0.001). Moreover, compared to the STANDARDIZED, PERSONALIZED participants had greater odds of adherence over time (OR [95%CI]: 5.12 [1.49, 17.6]; P = 0.009). Higher BMI was significantly associated with lower adherence (OR [95%CI]: 0.92 [0.87, 0.98]; P = 0.006). The proportion of attendance at videoconference sessions was similar between groups (STANDARDIZED: 77.1%; PERSONALIZED: 77.5%). Conclusions Two weight loss programs having similar calorie targets, behavioral approach, and contact schedule resulted in similar session attendance. However, adherence to self-monitoring was better when feedback was personalized. Funding Sources American Heart Association.

scholarly journals Promoting Weight Loss Before Pregnancy

2013 ◽  
Vol 11 (2) ◽  
pp. 86-92 ◽  
Author(s):  
Suzanne Phelan ◽  
Todd Hagobian ◽  
Anna Brannen ◽  
Ana Stewart ◽  
Brianna Schmid ◽  
...  
Keyword(s):  
Weight Loss ◽  
Lifestyle Modification ◽  
Obese Women ◽  
Cesarean Sections ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Established Risk Factor ◽  
Cutting Out ◽  
Clinically Significant

Pre-pregnancy obesity is a well-established risk factor for several adverse maternal and fetal outcomes, including gestational diabetes, hypertension, cesarean sections, and fetal macrosomia. Weight loss before pregnancy could help prevent such complications, but the feasibility of such an approach remains unknown. The current study examined the feasibility of a 3-month pre-pregnancy behavioral weight loss program in 12 overweight/obese women planning pregnancy. The 3 month program resulted in an average 5.4 ± 3.0 kg weight loss and significant improvements in self-monitoring, physical activity, eating and exercise self-efficacy, and healthy eating (p < 0.04). By the end of the 9 month follow-up, half of sample (n = 6) had conceived. Women reported significant increases in weekly or more frequent selfweighing (p < 0.0001), counting calories (p < 0.001), consuming fruit and vegetables (p = 0.007), and cutting out fat (p = 0.0001) and junk foods (p = 0.002). A lifestyle modification program to promote weight loss before pregnancy promoted clinically significant weight loss and appeared feasible.

scholarly journals Importance of Multiple Reinforcing Comments and Areas for Change in Optimizing Dietary and Exercise Self-Monitoring Feedback in Behavioral Weight Loss Programs: Factorial Design (Preprint)

2020 ◽  
Author(s):  
Rebecca Krukowski ◽  
Hyeonju Kim ◽  
Melissa Stansbury ◽  
Qian Li ◽  
Saunak Sen ◽  
...  
Keyword(s):  
Weight Loss ◽  
Factorial Design ◽  
Random Order ◽  
Behavioral Changes ◽  
Behavioral Weight Loss ◽  
Response Scale ◽  
Core Element ◽  
Self Monitoring ◽  
The Future ◽  
The Individual

BACKGROUND Individualized dietary and physical activity self-monitoring feedback is a core element of behavioral weight loss interventions and is associated with clinically significant weight loss. To our knowledge, no studies have evaluated individuals’ perspectives on the composition of feedback messages or the effect of feedback composition on the motivation to self-monitor. OBJECTIVE This study aims to assess the perceptions of feedback emails as a function of the number of comments that reinforce healthy behavior and the number of areas for change (ie, behavioral changes that the individual might make to have an impact on weight) identified. METHODS Emailed feedback followed a factorial design with 2 factors (ie, reinforcing comments and areas for change), each with 3 levels (ie, 1, 4, or 8 comments). A total of 250 adults with overweight or obesity who were interested in weight loss were recruited from the Qualtrics research panel. Participants read 9 emails presented in a random order. For each email, respondents answered 8 questions about the likelihood to self-monitor in the future, motivation for behavioral change, and perceptions of the counselor and the email. A mixed effects ordinal logistic model was used to compute conditional odds ratios and predictive margins (ie, average predicted probability) on a 5-point Likert response scale to investigate the optimal combination level of the 2 factors. RESULTS Emails with more reinforcing comments or areas for change were better received, with small incremental benefits for 8 reinforcing comments or areas for change versus 4 reinforcing comments or areas for change. Interactions indicated that the best combination for 3 of 8 outcomes assessed (ie, motivation to make behavioral changes, counselor’s concern for their welfare, and the perception that the counselor likes them) was the email with 8 reinforcing comments and 4 areas for change. Emails with 4 reinforcing comments and 4 areas for change resulted in the highest average probability of individuals who reported being very likely to self-monitor in the future. CONCLUSIONS The study findings suggest how feedback might be optimized for efficacy. Future studies should explore whether the composition of feedback email affects actual self-monitoring performance.

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