Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention

2020 ◽  
Author(s):  
Stephanie P Goldstein ◽  
Leslie A Brick ◽  
J Graham Thomas ◽  
Evan M Forman
Keyword(s):  
Weight Loss ◽  
Random Effect ◽  
Secondary Analysis ◽  
Future Research ◽  
Just In Time ◽  
Behavioral Weight Loss ◽  
Moderation Effect ◽  
Adaptive Intervention ◽  
Behavioral Weight Loss Intervention ◽  
Post Hoc

Abstract We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet). OnTrack utilizes ecological momentary assessment (EMA; repeated electronic surveys) for self-reporting lapse triggers, predicts lapses using machine learning, and provides brief intervention to prevent lapse. We have established preliminary feasibility, acceptability, and efficacy of OnTrack, but no study has examined our hypothesized mechanism of action: reduced lapse frequency will be associated with greater weight loss while using OnTrack. This secondary analysis investigated the association between lapse frequency and the weekly percentage of weight loss. Post hoc analyses evaluated the moderating effect of OnTrack engagement on this association. Participants (N = 121) with overweight/obesity (MBMI = 34.51; 84.3% female; 69.4% White) used OnTrack with a digital weight loss program for 10 weeks. Engagement with OnTrack (i.e., EMA completed and interventions accessed) was recorded automatically, participants self-reported dietary lapses via EMA, and weighed weekly using Bluetooth scales. Linear mixed models with a random effect of subject and fixed effect of time revealed a nonsignificant association between weekly lapses and the percentage of weight loss. Post hoc analyses revealed a statistically significant moderation effect of OnTrack engagement such that fewer EMA and interventions completed conferred the expected associations between lapses and weight loss. Lapses were not associated with weight loss in this study and one explanation may be the influence of engagement levels on this relationship. Future research should investigate the role of engagement in evaluating JITAIs.

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.

Abstract P249: A Behavioral Weight Loss Intervention is Effective for Change in CVD Risk Factors in Young Adults: The IDEA Study

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
John M Jakicic ◽  
Kelliann K Davis ◽  
Bethany Barone Gibbs ◽  
Diane Helsel ◽  
Wendy C King ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Weight Loss ◽  
Young Adults ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Disease Risk Factors ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Behavioral Weight Loss Intervention

Introduction: Few studies have examined behavioral weight loss interventions with respect to change in cardiovascular disease risk factors in young adults (aged 18 to 35 years). Hypothesis: We tested the hypothesis that a 6 month behavioral weight loss intervention resulted in significant improvements in selective cardiovascular disease risk factors in young adults. Methods: Data are presented as median [25 th , 75 th percentiles]. 470 participants (age: 30.9 [27.8, 33.7] years); BMI: 31.2 [28.4, 34.3] kg/m 2 ) were enrolled in a 6 month behavioral weight loss intervention that included weekly group sessions and prescribed an energy restricted diet and moderate-to-vigorous physical activity. Assessments included weight using a standardized protocol, resting blood pressure, and fasting lipids, glucose, and insulin. Statistical significance of change was according to tests of symmetry or the Wilcoxon matched pairs signed ranks test. Results: The primary outcome (weight) was available for 424 of the 470 participants (90.2%). Weight significantly decreased (-7.8 kg [-12.2, -3.7]) (p<0.0001). Systolic (-4.0 mmHg [-8.5, 0.5] and diastolic blood pressure (-3.0 mmHg [-6.5, 1.0]) decreased (p<0.0001). Total cholesterol (-13 mg/dl [-28.0, 2.0]), LDL cholesterol (-9.5 mg/dl [-21.7, 2.0]), triglycerides (-8.5 mg/dl [-44.0, 9.0]), glucose (-4.0 mg/dl [-8.0, 1.0]), and insulin (-2.6 mIU/L [-5.9, 0.7]) decreased (p<0.0001, n=416). There was not a significant change in HDL cholesterol (p=0.72). Conclusions: In conclusion, after 6 months, weight loss was observed in young adults assigned to this behavioral intervention that focused on physical activity and diet modification. They tended to also have improved cardiovascular disease risk factors. This may demonstrate an approach to reducing cardiovascular disease risk in young adults. Supported by NIH (U01HL096770) and AHA (12BGIA9410032)

Abstract P117: Day to Day Variation in Self-Efficacy Associated with Weight Loss and Risk of Dietary Lapses

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Lora E Burke ◽  
Linda J Ewing ◽  
Saul Shiffman ◽  
Dan Siewiorek ◽  
Asim Smailagic ◽  
...  
Keyword(s):  
Weight Loss ◽  
Self Efficacy ◽  
Healthy Lifestyle ◽  
Social Setting ◽  
Behavioral Weight Loss ◽  
Intentional Weight Loss ◽  
Logistic Regression Models ◽  
Unit Increase ◽  
Behavioral Weight Loss Intervention ◽  
Ecological Momentary

Introduction: Ecological momentary assessment (EMA) assesses individuals' experiences, behaviors, and moods as they occur in real time and in their own environment, making it useful to understand the processes of behavior change. We report the use of EMA to study the triggers of lapses after intentional weight loss in a 12-mo. study that includes a standard behavioral weight loss intervention. Purpose: We examined daily self-reports of self-efficacy and how they were related to unplanned eating episodes (‘lapses’) and weight change over the first 6 mos. of the study. Hypothesis: Higher self-efficacy is related to fewer “lapses” and better weight loss over time. Methods: Participants were provided a smartphone app programmed to administer EMA assessments up to 5 randomly-selected times/day. Each assessment included the self-efficacy query, How confident are you that if you have an urge to go off your healthy lifestyle plan, you can resist the urge? measured on a scale of 1-10. Participants were weighed at weekly, and after 3 months bi-weekly, group sessions. To account for replicate observations among subjects, generalized estimating equations were used to fit logistic regression models predicting lapses as a function of self-efficacy, adjusting for location (e.g., home, work, restaurant) and social setting (e.g., with others, alone). Results: The sample (N = 151) was 90.7% female and 79.5% White, and on average, 51.18 (10.22) years of age with a mean BMI of 34.0 (4.6) kg/m2. Of the 59,913 random assessments conducted over 6 mos., eating episodes were recorded in 7,991 (13.34%) of those assessments, of which 881 (11.03%) were not planned. Most of the 7,991 planned and unplanned eating episodes were captured when individuals were with others who were eating (49%), or when completely alone (24%). After adjusting for location and social setting, self-efficacy remained a significant predictor of a lapse (p < 0.001). The odds of a lapse decreased by 70% (95% CI, 64%, 76%) for every unit increase in self efficacy. After controlling for social setting, participants were estimated to lose 0.35 more lbs/mo. (SE = 0.14; p = 0.02) for each unit increase in self efficacy. Self-efficacy maintained a stable level between 7.3 and 7.4 for the first 4 mos., before decreasing at a rate of 0.11 points/month (SE = 0.04; p = 0.002) in the last 2 mos. This temporal trend in self-efficacy was paralleled by a similar trend in participants’ weights; they lost an average of 3.26 lbs/mo. (SE = 0.18) in the first 4 mos. compared to only 0.59 lbs/mo. (SE = 0.29) in the last 2 mos. Conclusions: The data suggest that as self-efficacy decreased to near 7.0, individuals were at greater risk to experience a lapse in their diet, an integral part of the healthy lifestyle plan. Targeting enhanced and sustained levels of self-efficacy above 7 may enable a person to resist lapses and prevent weight regain.

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

scholarly journals Behavioral Weight Loss Intervention for Migraine: A Randomized Controlled Trial

Obesity ◽  
2017 ◽  
Vol 26 (1) ◽  
pp. 81-87 ◽  
Author(s):  
Dale S. Bond ◽  
J. Graham Thomas ◽  
Richard B. Lipton ◽  
Julie Roth ◽  
Jelena M. Pavlovic ◽  
...  
Keyword(s):  
Weight Loss ◽  
Randomized Controlled Trial ◽  
Controlled Trial ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
Randomized Controlled ◽  
Behavioral Weight Loss Intervention

BestFIT Sequential Multiple Assignment Randomized Trial Results: A SMART Approach to Developing Individualized Weight Loss Treatment Sequences

2021 ◽  
Author(s):  
Nancy E Sherwood ◽  
A Lauren Crain ◽  
Elisabeth M Seburg ◽  
Meghan L Butryn ◽  
Evan M Forman ◽  
...  
Keyword(s):  
Weight Loss ◽  
Randomized Trial ◽  
Controlled Trial ◽  
Response Assessment ◽  
Optimal Time ◽  
Behavioral Weight Loss ◽  
Weight Loss Treatment ◽  
Adaptive Intervention ◽  
Weight Losses ◽  
Multiple Assignment

Abstract Background State-of-the-art behavioral weight loss treatment (SBT) can lead to clinically meaningful weight loss, but only 30–60% achieve this goal. Developing adaptive interventions that change based on individual progress could increase the number of people who benefit. Purpose Conduct a Sequential Multiple Assignment Randomized Trial (SMART) to determine the optimal time to identify SBT suboptimal responders and whether it is better to switch to portion-controlled meals (PCM) or acceptance-based treatment (ABT). Method The BestFIT trial enrolled 468 adults with obesity who started SBT and were randomized to treatment response assessment at Session 3 (Early TRA) or 7 (Late TRA). Suboptimal responders were re-randomized to PCM or ABT. Responders continued SBT. Primary outcomes were weight change at 6 and 18 months. Results PCM participants lost more weight at 6 months (−18.4 lbs, 95% CI −20.5, −16.2) than ABT participants (−15.7 lbs, 95% CI: −18.0, −13.4), but this difference was not statistically significant (−2.7 lbs, 95% CI: −5.8, 0.5, p = .09). PCM and ABT participant 18 month weight loss did not differ. Early and Late TRA participants had similar weight losses (p = .96), however, Early TRA PCM participants lost more weight than Late TRA PCM participants (p = .03). Conclusions Results suggest adaptive intervention sequences that warrant further research (e.g., identify suboptimal responders at Session 3, use PCMs as second-stage treatment). Utilizing the SMART methodology to develop an adaptive weight loss intervention that would outperform gold standard SBT in a randomized controlled trial is an important next step, but may require additional optimization work. Clinical Trial information ClinicalTrials.gov identifier; NCT02368002

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 &lt; 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.

Abstract MP27: Cellphone Intervention for You (CITY): A Randomized, Controlled Trial of Behavioral Weight Loss Intervention by Cellphone or Personal Coaching for Young Adults

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Laura P Svetkey ◽  
Stephen S Intille ◽  
Bryan C Batch ◽  
Leonor Corsino ◽  
Crystal C Tyson ◽  
...  
Keyword(s):  
Weight Loss ◽  
Young Adults ◽  
Controlled Trial ◽  
Personal Contact ◽  
Control Group ◽  
Weight Loss Intervention ◽  
Behavioral Weight Loss ◽  
National Guidelines ◽  
Personal Coaching ◽  
Behavioral Weight Loss Intervention

Background: Obesity affects young adults, leading to future morbidity and mortality. Early behavioral intervention may promote long-term weight control. Mobile technology-based (mHealth) interventions may be particularly effective in young adults. We compared both an mHealth behavioral weight loss intervention and a personal coaching weight loss intervention to no intervention (and to each other) in overweight/obese young adults. Methods: We randomized 365 generally healthy adults age 18-35 years with BMI > 25 kg/m2 (overweight or obese) to 24-months of intervention delivered primarily via investigator-designed cell phone (CP) or intervention delivered primarily via in-person (6 weekly) and by phone (23 monthly) coaching (PC), compared to usual care control group (Control). Primary outcome was weight change from baseline to 24 months. This study was conducted as part of the Early Adult Reduction of weight through LifestYle (EARLY) cooperative trials. Results: Randomized participants (N=365) had mean BMI 35 kg/m2, mean age 29yrs, were 70% women, 36% African American, 6% Latino. Final weight was obtained in 86%; missing weight was multiply imputed. At 24 months, weight loss was not different in either PC or CP vs Control (see Figure). Weight loss in PC was significantly greater than Control at 6 months. From baseline to 24 months, clinically significant weight loss (> 3% per national guidelines) occurred in 40% of PC, 34% of CP, and 30% of Control. Conclusions: mHealth alone may not be sufficient for weight loss in young adults but mHealth-enhanced contact with an interventionist has a modest short-term effect. Future interventions should maximize the complementarity of mHealth and personal contact to achieve larger and more sustained effect.

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