Dietary Self-monitoring Research Articles

Yes I can! Exploring the impact of self-efficacy in a digital weight loss intervention.

Self-efficacy is a modifiable intervention target in behavioral weight loss interventions. However, its role in the context of digital interventions is less clear. To determine change in self-efficacy in a digital weight loss intervention, and whether self-efficacy is associated with engagement in self-monitoring diet or weight loss. This is a secondary analysis of the GoalTracker study among 100 adults with overweight or obesity enrolled in a 12-week standalone digital weight loss intervention emphasizing daily self-monitoring. At baseline, 1 month, and 3 months, we assessed self-efficacy for controlling eating (via the Weight Efficacy Lifestyle Questionnaire; WELQ) and self-efficacy for tracking diet. Dietary self-monitoring engagement data were collected from the MyFitnessPal app. Weight was collected in person on a calibrated scale. Analyses included participants with complete data (N range: 72-99). Positive change from baseline to 1 month in self-efficacy for controlling eating was associated with higher dietary self-monitoring engagement (r = 0.21, P = .008) but not with 3-month weight change (r = -0.20, P = .052). Meanwhile, positive change from baseline to 1 month in self-efficacy for tracking diet was associated in a beneficial direction with both outcomes (r = 0.57, P < .001; r = -0.35, P < .001, respectively). However, on average, self-efficacy for controlling eating did not change over time while self-efficacy for tracking diet decreased (P < .001). Improvements in self-efficacy-particularly for tracking diet-early on in a digital weight loss intervention served as a mechanism of greater engagement and weight loss, highlighting the need for strengthening intervention strategies that promote early self-efficacy within a digital context.

Effect of participant baseline characteristics on adherence to self-monitoring of diet, physical activity and weight in a 12-month weight loss mHealth trial

Abstract Background Self-monitoring (SM) of lifestyle behaviors is the centerpiece of standard behavioral treatment (SBT) for weight loss as SM increases one’s awareness of their behaviors. Extensive evidence demonstrates a significant relationship between adherence to SM and successful weight loss outcomes. Purpose Examine baseline characteristics of participants that are associated with higher adherence to SM of diet, physical activity (PA), and weight in a 12-month mHealth weight loss trial. Methods SMARTER was a single-site, 2-arm randomized controlled trial. We randomized 502 adults with a body mass index (BMI) 27-43 kg/m2 to diet, weight, and PA SM plus tailored feedback messages (SM+FB, n=251) or SM alone (n=251). We provided the SM+FB group feedback messages via the SMARTER app on diet daily, PA every other day and weight weekly. The study provided a wearable PA tracker and smart scale for weight and utilized a smartphone app for dietary SM. Adherence of dietary SM was defined as the % of days with ≥50% of calorie goal recorded and PA SM was % of days with ≥500 steps. Weight SM was the % of days with weight based on the scale record. Linear mixed modeling was applied to assess the monthly percent adherence to SM of diet, PA, and weight over a 12-month period. Our analysis incorporated time effects in months and polynomials of time, treatment group assignment (SM+FB vs. SM), baseline variables of interest (e.g., BMI, age, sex, race, education years, income), and the interactions between treatment and time effects with other variables. For parsimony, non-significant effects were removed. Results The sample (N=502) was 82% white (n=414), 79% female (n=399), and had a mean age of 45.0±14.4 years and BMI of 33.7±4.0 kg/m2. Over 12 months, percent adherence to SM of diet, PA, and weight declined non-linearly. Treatment groups did not demonstrate significant differences. The impact of treatment on the monthly decline in adherence to diet SM was curvilinear and varied significantly over 12 months. Older age was associated with greater adherence to the SM of diet (b=0.72, 95% CI: 0.55-0.88, P &amp;lt; .0001), PA (b=0.50, 95% CI: 0.35-0.66, P &amp;lt; .0001), and weight (b=0.71, 95% CI: 0.57-0.85, P &amp;lt; .0001). Conversely, higher baseline BMI was associated with lower adherence to SM of diet (b=-1.14, 95% CI: -1.73 to -0.55, P &amp;lt; .0001) and PA (b=-0.79, 95% CI: -1.35 to -0.22, P= .006). Other baseline variables were not significantly related to adherence. Conclusion The findings suggest that individuals with a higher BMI at baseline may need additional enhancement strategies to improve their engagement with the digital tools for SM lifestyle which could lead to improved weight outcomes.

Decoding heterogeneity in data-driven self-monitoring adherence trajectories in digital lifestyle interventions for weight loss: a qualitative study

BackgroundData-driven trajectory modeling approaches have been used to identify participant subgroups with differing responses to digital lifestyle interventions. Identifying contributing factors to different participant subgroups can inform tailored strategies to early “rescue” intervention non-responders. Self-monitoring (SM) is a central mechanism in lifestyle interventions for driving behavior change and can serve as an early indicator for later intervention response. This qualitative study aimed to compare SM experiences between intervention response subgroups and to identify contributing factors to intervention response subgroups in a 6-month digital lifestyle intervention for adults with overweight or obesity.ResultsParticipants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Responder Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Non-responder Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app.ConclusionsOur study indicates that qualitative analysis is valuable for translating data-driven findings to actionable intervention improvement strategies. Our findings may inform the development of practical SM improvement strategies in future digital lifestyle interventions for weight loss. Notably, building problem solving skills emerge as a key approach to prevent potential non-responders from intervention disengagement.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

BackgroundThe Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).MethodsEligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.DiscussionThe DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.Trial registrationClinicalTrials.gov NCT05046886. Registered on September 16, 2021.

Factors associated with adherence to a public mobile nutritional health intervention: Retrospective cohort study

BackgroundObesity is a global health issue affecting over 2 billion people. Mobile health apps, specifically nutrition apps, have been identified as promising solutions to combat obesity. However, research on adherence to nutrition apps is scarce, especially for publicly available apps without monetary incentives and personal onboarding. Understanding factors associated with adherence is essential to improve the efficacy of these apps. This study aims to identify such factors by analyzing a large dataset of a free and publicly available app (“MySwissFoodPyramid”) that promotes healthy eating through dietary self-monitoring and nutrition literacy delivered via a conversational agent. MethodsA retrospective analysis was conducted on 19,805 users who used the app for at least two days between November 2018 and May 2022. Adherence was defined as completing a food diary by tracking dietary intake over a suggested period of three days. Users who finished multiple diaries were considered long-term adherent. The associations between the day and time of installation, tutorial use, reminder use, and conversational agent choice were examined regarding adherence, long-term adherence, and the number of completed diaries. ResultsOverall, 66.8% of included users were adherent, and 8.5% were long-term adherent. Users who started the intervention during the day (5 a.m.–7 p.m.) were more likely to be adherent and completed more diaries. Starting to use the intervention between Sunday and Wednesday was associated with better adherence and a higher number of completed diaries. Users who chose the female conversational agent were more likely to be adherent, long-term adherent, and completed more diaries. Users who skipped the tutorial were less adherent and completed fewer diaries. Users who set a follow-up reminder were more likely to be long-term adherent and completed more diaries. ConclusionsThis study demonstrates the potential of digital health interventions to achieve comparably high adherence rates, even without monetary incentives or human-delivered support. It also reveals factors associated with adherence highlighting the importance of app tutorials, customizable reminders, tailored content, and the date and time of user onboarding for improving adherence to mHealth apps. Ultimately, these findings may help improve the effectiveness of digital health interventions in promoting healthy behaviors.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes.

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).

A qualitative study of patients’ perceptions of DASH diet apps for dietary self- management of hypertension in Saudi Arabia

Nutrition smartphone apps have become a popular and readily accessible approach to improving dietary behaviour. Hypertension is often associated with a poor diet, and its prevalence is increasing in Saudi Arabia(1). The Dietary Approaches to Stop Hypertension (DASH) eating plan has been shown to reduce blood pressure in people with hypertension(2). A previous study identified two high-quality, safe, and potentially effective smartphone apps for managing hypertension: NOOM and DASH To TEN(3). This study aimed to explore the potential of these two DASH diet self- management apps for controlling blood pressure in the Saudi Arabian context.Fifteen patients were recruited at King Abdullah bin Abdulaziz University Hospital in Riyadh, Saudi Arabia. Ten patients had hypertension, and five had prehypertension (mean age = 45). Patients were given the two apps and asked to try each for one week. Focus groups (n=4) were held at the end of the trial to understand the patient perceptions of the apps. Discussions were recorded, and the transcriptions were analysed using a thematic framework analysis(4).The following four themes emerged:1.Managing hypertension through diet was perceived as important. Most patients reported thatadhering to the DASH diet was challenging due to a lack of motivation and willpower,inadequate support from household members, and high prices for healthy food.2.The potential and current reach of dietary apps emerged as a main theme, and manycomments related to patients’ belief that dietary apps could increase their motivation and awareness about the DASH diet. Barriers to the use of apps were also identified including their difficulty of use, a preference for dietetic monitoring, and cost.3.Interactive app functionality was identified as a key theme, as evidenced by comments about a straightforward sign-up process, an extensive food database for dietary self-monitoring, feedback, goal setting, and reminders, considered essential for diet adherence.4.A preference for the NOOM app emerged. Patients perceived the NOOM app more suitable in the Saudi context. Noom was deemed to support DASH self-management because it supports weight reduction, which helps lower blood pressure. NOOM was perceived as highly interactive, offering motivational reminders, feedback, realistic dietary plans, and a comprehensive food database. Patients made several recommendations for app improvement; these included Arabic language support, automatic calculation of sodium and potassium consumption, and lower cost. Patients also expressed a desire for training in the use of the app.This qualitative analysis suggests that the NOOM app could be suitable for use with hypertensive patients in Saudi Arabia. Further research is needed to determine the feasibility and efficacy of long- term use of the NOOM app with a Saudi Arabia population.

Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art.

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.

Identification of minimum thresholds for dietary self-monitoring to promote weight-loss maintenance.

Reduced schedules of dietary self-monitoring are typically recommended after the end of behavioral weight-loss programs; however, there exists little empirical evidence to guide these recommendations. We explored potential thresholds for dietary self-monitoring during a 9-month maintenance period following a 3-month weight-loss program in 74 adults with overweight or obesity (mean [SD] age = 50.7 [10.4] years, BMI = 31.2 [4.5] kg/m2) who were encouraged to self-monitor weight, dietary intake, and physical activity daily and report their adherence to self-monitoring each week via a study website. Greater self-monitoring was correlated with less weight regain for thresholds of ≥3 days/week, with the largest benefit observed for thresholds of ≥5 to ≥6 days/week (all p < 0.05); significant weight gain was observed for thresholds of ≥1 to ≥2 days/week, whereas no change in weight was observed for thresholds of ≥3 to ≥4 days/week, and weight loss was observed with thresholds of ≥5 or more days/week. Results demonstrate that self-monitoring at least 3 days/week may be beneficial for supporting long-term maintenance, although greater benefit (in relation to weight loss) may be realized at thresholds of 5 to 6 days/week. Future research should investigate whether individuals who were randomized to self-monitor at these different thresholds demonstrate differential patterns of weight-loss maintenance.

A Qualitative Evaluation of a Plate-Method Dietary Self-Monitoring Tool in a Sample of Adults Over 50

BackgroundSelf-monitoring is an important behavioral change technique to help users initiate and maintain dietary changes. Diet self-monitoring tools often involve the itemization of foods and recording of serving sizes. However, this traditional method of tracking does not conform to food guides using plate-based approach to nutrition education, such as the 2019 Canada’s Food Guide (CFG). ObjectiveTo explore the acceptability, facilitators and barriers of using a plate-based dietary self-monitoring tool based on the 2019 CFG (Plate Tool) compared with a traditional Food Journal (Food Journal). MethodsThe 2 dietary self-monitoring tools were compared using a crossover study design over 2 wk. Adults over 50 (n = 47) from Montreal, Canada, were randomly assigned to use one tool over 3 d during 1 wk, then used the other tool the next week. Semistructured interviews (n = 45) were conducted after completing the second tool. A qualitative description of the interviews was conducted through an inductive determination of themes. ResultsFacilitators to using the Plate Tool were its simplicity, quick completion time compared with the Food Journal and easiness to use, increased awareness of dietary habits and accountability, with participants expressing that it could help users make informed dietary changes aligning with the CFG. However, barriers to using the Plate Tool were its lack of precision, the participants’ difficulty categorizing foods into the CFG categories and recording intake of foods not present on the CFG. ConclusionsThe Plate Tool is an acceptable dietary self-monitoring tool for healthy adults over 50. Self-monitoring tools based on the plate method should take the barriers described in this study into account. Future studies should compare dietary self-monitoring methods to assess adherence and effectiveness at eliciting dietary behavior change.

Patterns and Predictors of Engagement With Digital Self-Monitoring During the Maintenance Phase of a Behavioral Weight Loss Program: Quantitative Study.

Long-term self-monitoring (SM) of weight, diet, and exercise is commonly recommended by behavioral weight loss (BWL) treatments. However, sustained SM engagement is notoriously challenging; therefore, more must be learned about patterns of engagement with digital SM tools during weight loss maintenance (WLM). In addition, insight into characteristics that may influence SM engagement could inform tailored approaches for participants at risk for poor adherence. This study explored patterns of digital SM of weight, diet, and exercise during WLM (aim 1) and examined timing, patterns, and rates of disengagement and reengagement (aim 2). This study also assessed relationships between individual-level factors (weight-related information avoidance and weight bias internalization) and SM engagement (aim 3). Participants were 72 adults enrolled in a BWL program consisting of a 3-month period of weekly treatment designed to induce weight loss (phase I), followed by a 9-month period of less frequent contact to promote WLM (phase II). Participants were prescribed daily digital SM of weight, diet, and exercise. At baseline, self-report measures assessed weight-related information avoidance and weight bias internalization. SM adherence was objectively measured with the days per month that participants tracked weight, diet, and exercise. Repeated-measures ANOVA examined differences in adherence across SM targets. Multilevel modeling examined changes in adherence across phase II. Relationships between individual-level variables and SM adherence were assessed with Pearson correlations, 2-tailed independent samples t tests, and multilevel modeling. During WLM, consistently high rates of SM (≥50% of the days in each month) were observed for 61% (44/72) of the participants for exercise, 40% (29/72) of the participants for weight, and 21% (15/72) of the participants for diet. Adherence for SM of exercise was higher than that for weight or diet (P<.001). Adherence decreased over time for all SM targets throughout phase II (P<.001), but SM of exercise dropped off later in WLM (mean 10.07, SD 2.83 months) than SM of weight (mean 7.92, SD 3.23 months) or diet (mean 7.58, SD 2.92 months; P<.001). Among participants with a period of low SM adherence (ie, <50% of the days in a month), only 33% (17/51 for weight, 19/57 for diet) to 46% (13/28 for exercise) subsequently had ≥1 months with high adherence. High weight-related information avoidance predicted a faster rate of decrease in dietary SM (P<.001). Participants with high weight bias internalization had the highest rates of weight SM (P=.03). Participants in BWL programs have low adherence to the recommendation to sustain daily SM during WLM, particularly for SM of diet and weight. Weight-related information avoidance and weight bias internalization may be relevant indicators for SM engagement. Interventions may benefit from innovative strategies that target participants at key moments of risk for disengagement.

P10-004-23 Dietary Self-Monitoring Adherence Is Associated With Increased Time in Recommended Glucose Range in Prediabetes and Moderately Controlled Type 2 Diabetes

P10-004-23 Dietary Self-Monitoring Adherence Is Associated With Increased Time in Recommended Glucose Range in Prediabetes and Moderately Controlled Type 2 Diabetes

P28-006-23 Dietary Self-Monitoring Trajectories and Associations With Glycemic Control in Adults With Prediabetes and Moderately-Controlled Type 2 Diabetes

P28-006-23 Dietary Self-Monitoring Trajectories and Associations With Glycemic Control in Adults With Prediabetes and Moderately-Controlled Type 2 Diabetes

113-LB: Daytime Time in Range Is Associated with Weight Loss Success in Patients with Prediabetes or Moderately Controlled Type 2 Diabetes

Objective: We quantified the association of overall and daytime time in range (TIR, 70-140 mg/dL) with weight change in a behavioral weight loss intervention. Methods: Data for this study came from the Personal Diet Study, a 6-month weight loss trial in adults with prediabetes or moderately controlled T2D (HbA1c&amp;lt;8%) involving remote behavioral counseling and dietary self-monitoring via a smartphone app. Participants wore blinded Abbott Freestyle Libre Pro CGMs for ≤2 weeks at baseline and 6 months. Based on clinically meaningful changes in two TIR metrics: “All-TIR” (all CGM readings) and “Day-TIR” (6am-midnight CGM readings), participants were categorized as “Improved” (&amp;gt;5%), “No Change” (-5 to 5%), or “Declined” (&amp;lt;-5%). Associations between TIR change categories and %weight change were determined using one-way ANOVAs. Results: We analyzed baseline and 6-month CGM data from 66 participants (age 63±8 years, 62% female, 64% White, 20% African American, BMI 31.5±5.0 kg/m2, HbA1c 5.8±0.5%). When stratified by Day-TIR change, Improved had greater weight loss compared to No Change and Declined (p=0.006) (Figure 1). When stratifying using All-TIR, no significant weight change differences were observed across groups (p=0.15). Conclusion: Improved daytime TIR, but not overall TIR, was associated with greater weight loss in a behavioral weight loss intervention. Disclosure S. Barua: None. M. Mottern: None. D. St-jules: Consultant; Ardelyx, Inc. E. Segal: None. M. Bergman: None. J. O. Aleman: Advisory Panel; Intellihealth, Consultant; Novo Nordisk, Employee; Veterans Administration, Research Support; NIH - National Institutes of Health, Veterans Administration. A. Schmidt: None. M. Sevick: None. D. A. Upadhyay: None. C. Popp: None. L. Hu: None. M. Curran: None. L. Berube: None. H. Li: None. C. Wang: None. M. Pompeii: None. Funding American Heart Association (17SFRM33590133)

Effect of an mHealth weight loss intervention on Healthy Eating Index diet quality: the SMARTER randomised controlled trial.

In the few weight loss studies assessing diet quality, improvements have been minimal and recommended calculation methods have not been used. This secondary analysis of a parallel group randomised trial (regsitered: https://clinicaltrials.gov/ct2/show/NCT03367936) assessed whether self-monitoring with feedback (SM + FB) v. self-monitoring alone (SM) improved diet quality. Adults with overweight/obesity (randomised: SM n 251, SM + FB n 251; analysed SM n 170, SM + FB n 186) self-monitored diet, physical activity and weight. Real-time, personalised feedback, delivered via a study-specific app up to three times daily, was based on reported energy, fat and added sugar intake. Healthy Eating Index 2015 (HEI-2015) scores were calculated from 24-hour recalls. Higher scores represent better diet quality. Data were collected August 2018 to March 2021 and analysed spring 2022. The sample was mostly female (78·9 %) and white (85·4 %). At baseline, HEI-2015 total scores and bootstrapped 95 % CI were similar by treatment group (SM + FB: 63·11 (60·41, 65·24); SM: 61·02 (58·72, 62·81)) with similar minimal improvement observed at 6 months (SM + FB: 65·42 (63·30, 67·20); SM: 63·19 (61·22, 64·97)) and 12 months (SM + FB: 63·94 (61·40, 66·29); SM: 63·56 (60·81, 65·42)). Among those who lost ≥ 5 % of baseline weight, HEI-2015 scores improved (baseline: 62·00 (58·94, 64·12); 6 months: 68·02 (65·41, 71·23); 12 months: 65·93 (63·40, 68·61)). There was no effect of the intervention on diet quality change. Clinically meaningful weight loss was related to diet quality improvement. Feedback may need to incorporate more targeted nutritional content.

Psychological Support Strategies for Adults With Type 2 Diabetes in a Very Low-Carbohydrate Web-Based Program: Randomized Controlled Trial.

A very low-carbohydrate (VLC) nutritional strategy may improve glycemic control and weight loss in adults with type 2 diabetes (T2D). However, the supplementary behavioral strategies that might be able to improve outcomes using this nutritional strategy are uncertain. This study aims to compare the impact of adding 3 different supplementary behavioral strategies to a web-based VLC diet intervention. To our knowledge, this is the first trial to randomize participants to different frequencies of dietary self-monitoring. The study included 112 overweight adults with T2D (hemoglobin A1c ≥6.5%) taking no antiglycemic medications or only metformin. They received a remotely delivered 12-month VLC diet intervention. Participants were randomly assigned through a full factorial 2×2×2 design to supplementary strategies: either daily or monthly dietary self-monitoring, either mindful eating training or not, and either positive affect skills training or not. Our research goal was to determine whether 3 different supplemental strategies had at least a medium effect size (Cohen d=0.5). Overall, the VLC intervention led to statistically significant improvements in glycemic control (-0.70%, 95% CI -1.04% to -0.35%; P<.001), weight loss (-6.82%, 95% CI -8.57% to -5.08%; P<.001), and depressive symptom severity (Cohen d -0.67, 95% CI -0.92 to -0.41; P<.001). Furthermore, 30% (25/83) of the participants taking metformin at baseline reduced or discontinued their metformin. Only 1 Cohen d point estimate reached 0.5; daily (vs monthly) dietary self-monitoring had a worse impact on depressive symptoms severity (Cohen d=0.47, 95% CI -0.02 to 0.95; P=.06). None of the strategies had a statistically significant effect on outcomes. For changes in our primary outcome, hemoglobin A1c, the daily (vs monthly) dietary self-monitoring impact was 0.42% (95% CI -0.28% to 1.12%); for mindful eating, it was -0.47% (95% CI -1.15% to 0.22%); and for positive affect, it was 0.12% (95% CI -0.57% to 0.82%). Other results for daily (vs monthly) dietary self-monitoring were mixed, suggesting an increase in weight (0.98%) and depressive symptoms (Cohen d=0.47), less intervention satisfaction (Cohen d=-0.20), more sessions viewed (3.02), and greater dietary adherence (Cohen d=0.24). For mindful eating, the results suggested a benefit for dietary adherence (Cohen d=0.24) and intervention satisfaction (Cohen d=0.30). For positive affect, the results suggested a benefit for depressive symptoms (Cohen d=-0.32), the number of sessions viewed (3.68), dietary adherence (Cohen d=0.16), and intervention satisfaction (Cohen d=0.25). Overall, our results support the use of a VLC diet intervention in adults with T2D. The addition of monthly (not daily) dietary self-monitoring, mindful eating, and positive affect skills training did not show a definitive benefit, but it is worth further testing. ClinicalTrials.gov NCT03037528; https://clinicaltrials.gov/ct2/show/NCT03037528.

363 Association between self-monitoring and ketogenic diet adherence in a technology-assisted lifestyle intervention

OBJECTIVES/GOALS: Self-monitoring (SM) improves adherence to low-fat low-calorie (LFLC) diet for weight management. Ketogenic diet (KD) is a promising alternative to LFLC, however, it is unclear whether SM improves KD adherence. We examined the association between SM and KD adherence during the first 12 weeks of a 6-month technology-assisted lifestyle intervention. METHODS/STUDY POPULATION: We included 30 (50.8 ± 12.4 years, 70% female) overweight/obese (body mass index: 37.1 ± 7.2 kg/m2) participants in the analysis. They received personalized KD goals with very low-carbohydrate (22–62 g/d), moderate protein (52 -87 g/d), and high-fat (115 - 219g/d) and calorie intake goals (1338–2554 kcal/d). Additionally, participants performed daily diet, exercise, and weight SM. Adherence to KD was measured by (1) self-monitored dietary intake, and (2) percent of days in ketosis state (blood ketone≥0.5 mmol/L) captured by a fingerstick blood ketone meter. SM frequency was defined as percent of days participant logged food intake, wore fitness tracker, and weighed body weight. Pearson correlation coefficients were computed to examine the correlation between SM in diet, exercise, and weight with KD adherence. RESULTS/ANTICIPATED RESULTS: Percentage of days participants SM for diet, exercise, and weight was 58.4 ± 32.2%, 66.4 ± 30.9%, and 59.0 ± 32.6%, respectively. Correlational analysis more frequent diet SM was positively correlated with more days in ketosis (r = 0.58, p = 0.003), higher fat intake (r = 0.68, p = 0.0001), and higher calorie intake (r = 0.67, p = 0.002) within the fat and calorie goals set; more frequent weight SM was positively correlated with more days in ketosis (r = 0.48, p = 0.02), higher fat intake (r = 0.45, p = 0.023), and higher calorie intake (r = 0.44, p = 0.027). DISCUSSION/SIGNIFICANCE: We found that diet and weight SM were positively associated with fat and calorie intake, as well as days in ketosis. Given the reported promising effect of KD on weight loss and the challenges of adhering to KD, our findings suggested that promoting SM on diet and weight might be a promising avenue for improving KD adherence leading to successful weight loss.

The Efficacy of Mobile Applications for Weight Loss.

A variety of mobile-based applications aimed at weight loss have become popular in recent years. This review describes the features and effectiveness of mobile weight loss apps. Overall, mobile apps can help patients lose weight either as well as or better than traditional paper-and-pencil weight loss interventions and often better than minimal intervention control groups. Mobile apps promote multiple strategies, including self-monitoring of diet, exercise, and weight, as well as social support and educational content. Significant variation exists in app types, which makes it difficult to conclude which features drive program effectiveness. Intervention success varies based on patients' level of engagement with the app. There is a deficit of apps and app-based studies of older, less tech-savvy adults, ethnic/racial minorities, and low-income individuals, as well as longer-term studies. Mobile apps can successfully help patients lose weight and represent a cost-effective, accessible alternative to intensive in-person weight loss programs. More research is needed into their long-term potential, especially for hard-to-reach populations.

Abstract P399: The Effect of Self-Monitoring of Diet, Weight and Steps and Adherence to Diet, Fat and Physical Activity Goals on Weight Loss at 12 Months: Smarter RCT

Introduction: Self-monitoring (SM) is the core of behavioral weight loss. Efficacy evidence for mHealth to improve SM adherence and impact weight loss outcomes is limited. Purpose: Examine associations among diet, weight, and physical activity (PA) SM and adherence to diet, fat and PA goals on weight loss at 12 mos in adults with overweight or obesity. Methods: SMARTER was a single-site, 2-arm weight loss trial. We randomized adults with (BMI) 27-43 kg/m 2 to diet, weight, and PA SM plus tailored feedback messages (SM+FB, n=251) or SM alone (n=251). The SM+FB group received feedback messages via the SMARTER app on diet daily, PA every other day and weight weekly. The study provided Fitbit Charge 2™ for PA, smart scales for weight and Fitbit app for diet. Participants received individualized daily calorie/fat goals (based on sex &amp; weight) and incremental weekly PA goals to reach ≥300 min/wk of moderate- and vigorous-intensity PA. Staff measured weight at baseline, 6 and 12 mos. Diet SM was the % of days with ≥50% of calorie goal recorded. Weight SM was the % of days with weight recorded. PA SM was % of days with ≥500 steps. Calorie goal adherence was % of days with ≥85% and &lt;115% of calorie goals and fat goals as ≤25% of calorie goals. PA goal adherence was the average % of weekly MVPA meeting the PA goal for ≥4 days/wk. Weight loss at 12 mos was computed as the % of change in weight from baseline to 12 mos relative to baseline weight. We compared diet, weight and PA SM and adherence to calorie, fat and PA goals by treatment assignment using Wilcoxon rank-sum tests. The effect of diet, weight and PA SM and adherence to calorie, fat and PA goals on weight loss at 12 mos was examined using separate linear regression models considering treatment assignment and interaction effects. Results: The sample (N=502) was 82% white (n=414), 79% female (n=399), on average 45.0±14.4 years of age with a mean BMI of 33.7±4.0 kg/m 2 . Treatment arms were similar on % days of weight and PA SM and % days adherent to calorie, fat and PA goals (p≥.05); however, the SM+FB group had greater % days of SM diet than the SM group (58.9 [24.7, 84.9] vs. 41.1 [14.0, 81.9]; P=.01). Weight loss at 12 mos was significantly associated (reported as b [95% CI]) with higher % of days of SM diet (-0.10 [-0.13, -0.08]), weight (-0.12 [-0.14, -0.09]) and PA (-0.10 [-0.13, -0.07]) and % days adherent to calorie (-0.06 [-0.11, -0.01]) and fat goals (-0.11 [-0.20, -0.02]) and average % days adherent to PA goals (-0.04 [-0.06, -0.02]). The effect of treatment assignment and its interactions with SM of diet, weight and PA and adherence to calorie, fat and PA goals on weight loss at 12 mos were not significant. Conclusions: In this study, SM of lifestyle behaviors and adherence to diet and PA goals were associated with better weight loss at 12 months and did not depend on treatment group assignment, suggesting that the mHealth intervention did not enhance adherence sufficiently to affect weight loss outcomes.

Identifying Predictors of Adherence to the Physical Activity Goal: A Secondary Analysis of the SMARTER Weight Loss Trial.

Research is needed to inform tailoring supportive strategies for promoting physical activity (PA) in the context of behavioral treatment of obesity. We aimed to identify baseline participant characteristics and short-term intervention response predictors associated with adherence to the study-defined PA goal in a mobile health (mHealth) weight loss trial. A secondary analysis was conducted of a 12-month weight loss trial (SMARTER) that randomized 502 adults with overweight or obesity to either self-monitoring of diet, PA, and weight with tailored feedback messages ( n = 251) or self-monitoring alone ( n = 251). The primary outcome was average adherence to the PA goal of ≥150 min·wk -1 of moderate- and vigorous-intensity aerobic activities (MVPA) from Fitbit Charge 2™ trackers over 52 wk. Twenty-five explanatory variables were considered. Machine learning methods and linear regression were used to identify predictors of adherence to the PA goal. The sample ( N = 502) was mostly female (80%), White (82%) with the average age of 45 ± 14.4 yr and body mass index of 33.7 ± 4.0 kg·m -2 . Machine learning methods identified PA goal adherence for the first week as the most important predictor of long-term PA goal adherence. In the parsimonious linear regression model, higher PA goal adherence for the first week, greater PA FB messages opened, older age, being male, higher education, being single and not having obstructive sleep apnea were associated with higher long-term PA goal adherence. To our knowledge, this is the first study using machine learning approaches to identify predictors of long-term PA goal adherence in a mHealth weight loss trial. Future studies focusing on facilitators or barriers to PA among young and middle-age adults and women with low PA goal adherence are warranted.