scholarly journals Weight-Related Information Avoidance Prospectively Predicts Poorer Self-Monitoring and Engagement in a Behavioral Weight Loss Intervention

2020 ◽  
Author(s):  
Leah M Schumacher ◽  
Mary K Martinelli ◽  
Alexandra D Convertino ◽  
Evan M Forman ◽  
Meghan L Butryn
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Body Weight ◽  
Treatment Engagement ◽  
Treatment Discontinuation ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Information Avoidance ◽  
Related Information ◽  
Session Attendance

Abstract Background Self-monitoring is a key component of behavioral weight loss (BWL) interventions. Past research suggests that individuals may avoid self-monitoring in certain contexts (e.g., skipping self-weighing after higher-than-usual calorie intake). However, no studies have attempted to quantify individuals’ inclination to avoid information about their weight control (“weight-related information avoidance”; WIA) or prospectively examined its implications for treatment engagement and outcomes in BWL programs. Purpose Characterize WIA using a validated questionnaire among adults enrolled in BWL treatment and examine whether WIA prospectively predicts self-monitoring adherence, session attendance, treatment discontinuation, or weight loss. Methods Participants (N = 87; MBMI = 34.9 kg/m2, 83% female) completed a measure of WIA prior to starting a 12 week, group-based BWL intervention. Participants were given digital self-monitoring tools and instructed to self-monitor their food intake daily, physical activity daily, and body weight weekly (Weeks 1–10) and then daily (Weeks 11–12). Session attendance and treatment discontinuation were recorded. Weight was measured in-clinic pretreatment and posttreatment. Results While mean WIA was low (M = 2.23, standard deviation [SD] = 0.95; potential scale range: 1–7), greater WIA predicted poorer attendance (r = −.23; p = .03) and poorer self-monitoring of physical activity (r = −.28; p = .009) and body weight (r = −.32; p = .003). WIA did not predict food monitoring (p = .08), treatment discontinuation (p = .09), or 12 week weight loss (p = .91). Conclusions Greater WIA, as assessed via a brief questionnaire, may place individuals at risk for poorer self-monitoring and treatment engagement during BWL. Further research on the implications of WIA in the context of weight management is warranted, including evaluation of correlates, moderators, and mechanisms of action of WIA. Clinical Trial Registration NCT03337139.

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 Do Outcomes From a Behavioral Weight Loss Intervention Differ in Alabama vs Colorado

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 1205-1205
Author(s):  
Julianne Clina ◽  
R Drew Sayer ◽  
Caroline Cohen ◽  
James Hill
Keyword(s):  
Physical Activity ◽  
Social Support ◽  
Weight Loss ◽  
Body Weight ◽  
Regional Differences ◽  
Management Program ◽  
Future Research ◽  
Weight Loss Intervention ◽  
Obesity Prevalence ◽  
Behavioral Weight Loss

Abstract Objectives There are well-established regional differences in obesity prevalence in the U.S., but relatively little is known about whether these differences impact efforts for weight loss. The objective of the study was to determine whether changes in body weight, engagement in physical activity (PA), and psychosocial factors differed in Colorado (CO) vs Alabama (AL) in response to a 16-week standardized behavioral weight management program. We hypothesized that weight loss would be greater in Colorado due to a more favorable physical and social environment. Methods This is an ancillary study to a weight loss intervention being conducted simultaneously in AL and CO with identical intervention content and delivery. Study participants (n = 70, 39 CO, 31 AL) were randomized to either a high protein (HP) or normal protein (NP) diet for 16 weeks and attended weekly group classes led by a trained coach targeting diet, mindset, and physical activity. Body weight, objective (accelerometry) and self-reported (International Physical Activity Questionnaire) PA, and responses to psychosocial questionnaires were collected at baseline and week 16. Psychosocial constructs included executive function, hedonic eating, stress, and social support. Results Both states lost a significant amount of weight (CO 13.2 ± 4.9 kg P = 0.0067; AL 12.5 ± 5.6 kg P = 0.0262) with no differences between states (P = 0.9315). Both states improved in all PA outcomes over time, with AL increasing significantly more in objective PA measures when compared to CO. AL had more favorable scores for hedonic eating at baseline (23.2 ± 2.4 vs 32.5 ± 1.8, P = 0.0017), which persisted to week 16 (19.0 ± 2.7 vs 29.7 ± 2.2, P = 0.0021). Finally, AL improved in several social support factors while CO did not. Conclusions While weight loss did not differ between states, AL experienced greater improvements in some factors known to improve long-term weight loss maintenance. Results from this study provide a strong rationale for investigating potential regional differences in the maintenance of lost weight that may not be apparent during the active weight loss phase of interventions. Future research in this area will require effective methods for tracking participants beyond the conclusion of most clinical trials. Funding Sources The parent clinical trial is supported by The Beef Checkoff.

scholarly journals SAT-575 Association Between Baseline Fitness and Changes in Physical Activity and Weight Loss in an 18-Month Behavioral Weight Loss Program

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Adnin Zaman ◽  
Danielle M Ostendorf ◽  
Zhaoxing Pan ◽  
Seth A Creasy ◽  
Brian L Stauffer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Body Weight ◽  
Cardiovascular Fitness ◽  
Weight Loss Program ◽  
Moderate Intensity ◽  
Behavioral Weight Loss ◽  
Supervised Exercise ◽  
The Poor ◽  
Baseline Fitness

Abstract BACKGROUND: Baseline cardiovascular fitness may be a significant predictor of future success in a comprehensive behavioral weight loss program (BWLP). Yet, few studies have examined the association between baseline fitness and future weight loss. PURPOSE: To determine the association between baseline fitness and changes in body weight and device-measured levels of moderate-to-vigorous physical activity (MVPA) during a BWLP. METHODS: Adults (n=85) were enrolled in an 18-month BWLP combining a calorie-restricted diet, group-based behavioral support, and 6 months of supervised exercise (progressing to 300 min/wk of moderate-intensity) followed by 12 months of unsupervised exercise. Data from 60 completers (age 41.0±9.5 years, BMI 34.6±4.2 kg/m2, 80% female) were used in this analysis. MVPA was measured over 1 week with the Sensewear Armband at months 0, 6, 12, and 18. Fitness (VO2max) was measured on a treadmill using indirect calorimetry and categorized based on published age and sex norms (Physical Fitness Specialist Certification Manual, 1997). A linear mixed effects model with unstructured covariance was used to examine the association between baseline fitness category and changes in body weight, total MVPA, and MVPA in bouts ≥10 min at the four time points. RESULTS: Of the 60 completers, 33% (n=20) were classified as having very poor fitness, 45% (n=27) poor, 18% (n=11) fair, 3% (n=2) good, and 0% (n=0) excellent or superior. Due to the low proportion of participants categorized as having fair or better fitness, we created a binary fitness variable (very poor vs. poor or better). Baseline BMI was higher in those in the very poor category compared to those in the poor or better category (36.2±4.2 vs 33.7±4.0, p=0.03). There were no significant differences between the two fitness categories in weight change at 6 or 12 months. However, at 18 months, mean weight loss was 4.3±1.7 kg in those in the very poor category and 8.2±1.2 kg in those in the poor or better category, with a marginally significant between-group difference (p=0.07). There were no differences in changes in total or bout MVPA. However, those with very poor fitness had lower bout MVPA at baseline vs. those with poor or better fitness (16±20 vs 33±31 min/d, p=0.03). At 18 months, both groups increased bout MVPA, however bout MVPA remained lower in the very poor vs. poor or better group (24±29 vs 42±29 min/d, p=0.03). Total MVPA showed a similar pattern. CONCLUSION: Baseline fitness may moderate 18-month weight loss, as those with very poor fitness lost less weight compared to those with poor or better fitness levels. Those with poor or better fitness at baseline achieved significantly higher mean levels of MVPA at 18 months compared to those with very poor fitness. Participants with very poor fitness at baseline may require additional exercise support during a BWLP to achieve the high levels of MVPA recommended for weight loss maintenance.

scholarly journals Examination of Nutrition Literacy and Quality of Self-monitoring in Behavioral Weight Loss

2018 ◽  
Vol 52 (9) ◽  
pp. 809-816 ◽  
Author(s):  
Diane L Rosenbaum ◽  
Margaret H Clark ◽  
Alexandra D Convertino ◽  
Christine C Call ◽  
Evan M Forman ◽  
...  
Keyword(s):  
Weight Loss ◽  
Caloric Intake ◽  
Secondary Analysis ◽  
Behavioral Weight Loss ◽  
Food Record ◽  
Obese Adults ◽  
Baseline Measure ◽  
Self Monitoring ◽  
Session Attendance

Abstract Background Few have examined nutrition literacy (i.e., capacity to process and make informed nutritional decisions) in behavioral weight loss. Nutrition literacy (NL) may impact necessary skills for weight loss, contributing to outcome disparities. Purpose The study sets out to identify correlates of NL; evaluate whether NL predicted weight loss, food record completion and quality, and session attendance; and investigate whether the relations of race and education to weight loss were mediated by NL and self-monitoring. Methods This is a secondary analysis of 6-month behavioral weight loss program in which overweight/obese adults (N = 320) completed a baseline measure of NL (i.e., Newest Vital Sign). Participants self-monitored caloric intake via food records. Results NL was lower for black participants (p < .001) and participants with less education (p = .002). Better NL predicted better 6-month weight loss (b = −.63, p = .04) and food record quality (r = .37, p < .001), but not food record completion or attendance (ps > 0.05). Black participants had lower NL, which was associated with poorer food record quality, which adversely affected weight loss. There was no indirect effect of education on weight loss through NL and food record quality. Conclusions Overall, results suggest that lower NL is problematic for weight loss. For black participants, NL may indirectly impact weight loss through quality of self-monitoring. This might be one explanation for poorer behavioral weight loss outcomes among black participants. Additional research should investigate whether addressing these skills through enhanced treatment improves outcomes. Clinical trial information NCT02363010.

Abstract P118: Changes in Physical Activity by Self-Weighing Trajectory Groups

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Yaguang Zheng ◽  
Susan M Sereika ◽  
Linda J Ewing ◽  
Cynthia A Danford ◽  
Bonny Rockette-Wagner ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Lifestyle Changes ◽  
Future Research ◽  
Behavior Changes ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Consistent Group ◽  
Behavioral Weight Loss Intervention ◽  
Average Activity

Introduction: Numerous studies have established a significant association between regular self-weighing and weight loss; however, few studies have examined how self-weighing patterns are associated with lifestyle changes, e.g. physical activity (PA). The aim was to examine the association between frequency of self-weighing and changes in PA levels. Hypothesis: We hypothesized that higher frequencies of self-weighing are associated with greater increases in PA levels. Methods: This was an analysis of data from a 12-mo behavioral weight loss intervention study. Each subject was given a Wi-Fi scale and instructed to weigh daily. The scale transmitted weight values to a central server. PA was objectively assessed by an accelerometer (ActiGraph GT3x) at 0 and 6 mos. Participants were instructed to wear the accelerometer for ≥ 3 weekdays, one weekend day, ≥10 hours/day. General linear model was used for data analysis. Results: The sample (N=89) was largely female (89.9%), White (82%), with a mean age (±SD) of 51.9±9.3 years, and a mean BMI of 33.6±4.5 kg/m2. Our previous analysis using group-based trajectory modeling identified 3 self-weighing patterns: high/consistent (self-weighed 5-6 days/week regularly); moderate/declined (declined from 4-5 to 2 days/week); minimal/declined (declined from 5-6 to 0 days/week). As shown in the table, compared with minimal/declined self-weighing group, the high/consistent group had a significant increase in energy expenditure, steps, light and moderate PA levels as well as average activity/day, while the moderate/declined group had a significant increase in steps and average activity/day. Conclusions: The differences in PA level changes across the trajectory groups suggest that improved adherence to self-weighing carried over to improved PA behavior changes. It is unclear if self-monitoring weight and observing the results led participants to regulate their PA behavior accordingly. Future research needs to examine the mechanisms of how daily weighing impacts the level of daily PA.

scholarly journals Defining Valid Activity Monitor Data: A Multimethod Analysis of Weight-Loss Intervention Participants’ Barriers to Wear and First 100 Days of Physical Activity

Informatics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 39
Author(s):  
Stephanie L. Orstad ◽  
Lauren Gerchow ◽  
Nikhil R. Patel ◽  
Meghana Reddy ◽  
Christina Hernandez ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Loss ◽  
Low Income ◽  
Activity Monitor ◽  
Accelerometer Data ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Self Monitoring ◽  
Behavioral Weight Loss Intervention ◽  
Monitor Data

Despite the popularity of commercially available wearable activity monitors (WAMs), there is a paucity of consistent methodology for analyzing large amounts of accelerometer data from these devices. This multimethod study aimed to inform appropriate Fitbit wear thresholds for physical activity (PA) outcomes assessment in a sample of 616 low-income, majority Latina patients with obesity enrolled in a behavioral weight-loss intervention. Secondly, this study aimed to understand intervention participants’ barriers to Fitbit use. We applied a heart rate (HR) criterion (≥10 h/day) and a step count (SC) criterion (≥1000 steps/day) to 100 days of continuous activity monitor data. We examined the prevalence of valid wear and PA outcomes between analytic subgroups of participants who met the HR criterion, SC criterion, or both. We undertook qualitative analysis of research staff notes and participant interviews to explore barriers to valid Fitbit data collection. Overall, one in three participants did not meet the SC criterion for valid wear in Weeks 1 and 13; however, we found the SC criterion to be more inclusive of participants who did not use a smartphone than the HR criterion. Older age, higher body mass index (BMI), barriers to smartphone use, device storage issues, and negative emotional responses to WAM-based self-monitoring may predict higher proportions of invalid WAM data in weight-loss intervention research.

How to Maintain a Healthy Body Weight

2006 ◽  
Vol 76 (4) ◽  
pp. 208-215 ◽  
Author(s):  
Astrup
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Weight Loss ◽  
Body Weight ◽  
Glycemic Index ◽  
Dairy Products ◽  
Weight Control ◽  
Healthy Body Weight ◽  
Healthy Body

The epidemic of both obesity and type 2 diabetes is due to environmental factors, but the individuals developing the conditions possess a strong genetic predisposition. Observational surveys and intervention studies have shown that excess body fatness is the major environmental cause of type 2 diabetes, and that even a minor weight loss can prevent its development in high-risk subjects. Maintenance of a healthy body weight in susceptible individuals requires 45–60 minutes physical activity daily, a fat-reduced diet with plenty of fruit, vegetables, whole grain, and lean meat and dairy products, and moderate consumption of calorie containing beverages. The use of table values to predict the glycemic index of meals is of little – if any – value, and the role of a low-glycemic index diet for body weight control is controversial. The replacement of starchy carbohydrates with protein from lean meat and lean dairy products enhances satiety, and facilitate weight control. It is possible that dairy calcium also promotes weight loss, although the mechanism of action remains unclear. A weight loss of 5–10% can be induced in almost all obese patients providing treatment is offered by a professional team consisting of a physician and dieticians or nurses trained to focus on weight loss and maintenance. Whereas increasing daily physical activity and regular exercise does not significantly effect the rate of weight loss in the induction phase, it plays an important role in the weight maintenance phase due to an impact on daily energy expenditure and also to a direct enhancement of insulin sensitivity.

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

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