Perceived and calculated diet quality improvements in a randomized mHealth weight loss trial.

The diet quality of US adults is poor and cross-sectional analyses suggest self-perception of healthful dietary intake may be overestimated. This analysis assessed the concordance between calculated and perceived diet quality and changes in diet quality among adults seeking weight loss and enrolled in a 12-month randomized behavioral trial. Healthy Eating Index-2015 diet quality (HEI) was calculated from self-administered 24-hour recalls. Perceived diet quality (PDQ) was measured on a 100-point scale. Higher scores indicate better diet quality. Concordance was assessed using the concordance correlation coefficient and Bland-Altman plots. The one hundred and five participants with complete dietary data were mostly female and white. There was good agreement between HEI and PDQ scores at 12 months for less than a third of participants. Most of the disagreement arose from PDQ scores being higher than HEI scores. Even fewer participants had good agreement between HEI changes and PDQ changes. Participants perceived greater improvement in diet quality than indicated by HEI score changes. Concordance was low at 12 months and for change in diet quality. Despite the diet quality of adults seeking weight loss being suboptimal and not improving, many perceived their diet quality and diet quality improvements as better than calculated. Future studies might explore the effect of misperceptions on weight loss outcomes.

Community-Based, Cluster-Randomized Pilot Trial of a Cardiovascular Mobile Health Intervention: Preliminary Findings of the FAITH! Trial.

BACKGROUND: African Americans continue to have suboptimal cardiovascular health (CVH) based on the American Heart Association Life's Simple 7 (LS7), 7 health-promoting behaviors and biological risk factors (eg, physical activity, blood pressure). Innovative, community-level interventions in partnership with trusted institutions such as African American churches are potential means to improve CVH in this population. METHODS: Using a community-based participatory research approach, the FAITH! Trial (Fostering African American Improvement in Total Health) rigorously assessed the feasibility and preliminary efficacy of a refined, community-informed, mobile health intervention (FAITH! App) for promoting CVH among African Americans in faith communities using a cluster randomized controlled trial. Participants from 16 churches in Rochester and Minneapolis-St Paul, MN, were randomized to receive the FAITH! App (immediate intervention) or were assigned to a delayed intervention comparator group. The 10-week intervention core features included culturally relevant and LS7-focused education modules, diet/physical activity self-monitoring, and a group sharing board. Data were collected via electronic surveys and health assessments. Primary outcomes were average change in mean LS7 score (continuous measure of CVH ranging from poor to ideal [0-14 points]) from baseline to 6 months post-intervention (using generalized estimating equations) and app engagement/usability (by the Health Information Technology Usability Evaluation Scale; range, 0-5). RESULTS: Of 85 enrolled participants (randomized to immediate [N=41] and delayed [control] intervention [N=44] groups), 76 and 68 completed surveys/health assessments at baseline and 6 months post-intervention, respectively (80% retention rate with assessments at both baseline and 6-month time points); immediate intervention [N=30] and control [N=38] groups). At baseline, the majority of participants (mean age [SD], 54.2 [12.3] years, 71% female) had <4-year college education level (39/66, 59%) and poor CVH (44% in poor category; mean LS7 score [SD], 6.8 [1.9]). The mean LS7 score of the intervention group increased by 1.9 (SD 1.9) points compared with 0.7 (SD 1.7) point in the control group (both P<0.0001) at 6 months. The estimated difference of this increase between the groups was 1.1 (95% CI, 0.6-1.7; P<0.0001). App engagement/usability was overall high (100% connection to app; >75% completed weekly diet/physical activity tracking; Health Information Technology Usability Evaluation Scale, mean [SD], 4.2 [0.7]). CONCLUSIONS: On the basis of preliminary findings, the refined FAITH! App appears to be an efficacious mobile health tool to promote ideal CVH among African Americans. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03777709.

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.

Risk stratification by sex and menopausal status in the multivariable apnea prediction index.

STUDY OBJECTIVES: To determine the sensitivity of the Multivariable Apnea Prediction (MAP) index for obstructive sleep apnea (OSA) in pre- and post-menopausal women with the goal of developing a tailored scoring classification approach. METHODS: Data from two studies (N = 386); the diabetes sleep treatment trial (N = 236) and EMPOWER (N = 150) were used to assess the sensitivity and specificity of the MAP index by comparing men (n = 129) to women (n = 257), and premenopausal (n = 100) to post-menopausal women (n = 136). We evaluated participants at two cut points, apnea-hypopnea index (AHI) values of ≥ 5 and ≥ 10, using 0.5 as a predicted probability cut point to establish baseline sensitivity and specificity. Contingency tables and receiver operating characteristic (ROC) analysis were conducted to evaluate the accuracy of the MAP index in predicting OSA in men versus women, and in pre-versus post-menopausal women. To select optimal predicted probabilities for classification by sex and menopausal status, Youden's J statistic was generated from ROC coordinates. RESULTS: The MAP index was more sensitive to women in the AHI ≥ 5 group (76%) compared to AHI ≥ 10 group (30%). Among post-menopausal women with AHI ≥ 5, sensitivity was similar to men (98%), but less than men when AHI ≥ 10 (32%). Suggested probability cut points for women with an AHI ≥ 10 are 0.24 overall; 0.15 for premenopausal, and 0.38 for postmenopausal women. CONCLUSIONS: Because women's risk for OSA (AHI ≥ 10) was underestimated by the MAP index, we suggest the use of tailored cut points based on sex and menopausal status or assessing for OSA risk with an AHI of ≥ 5.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices.

BACKGROUND: Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual. OBJECTIVES: We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time. METHODS: We analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns. RESULTS: Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year. CONCLUSION: We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.

Neighborhood Walkability Is Associated with Physical Activity and Prediabetes in a Behavioral Weight Loss Study: a Secondary Analysis.

BACKGROUND: Socio-environmental factors may affect uptake and utility of behavioral interventions targeting weight loss and cardiometabolic health. To evaluate the relation of neighborhood walkability to physical activity (PA) and glucose control in a sample of adults with overweight/obesity participating in a weight loss study. METHODS: Secondary analysis of a 12-month behavioral weight loss intervention (2011-2015) using one-group pretest-posttest design. Neighborhood walkability was assessed via residential Walk Score (0-100) at study entry. Fasting plasma glucose (FPG) via phlebotomy and PA via waist-worn ActiGraph GT3X were assessed at baseline and end of study. Study variables included neighborhood walkability (car-dependent: Walk Score < 50 vs. walkable: Walk Score ≥ 50), prediabetes (FPG 100-125 mg/dL), and recommended PA (moderate to vigorous PA [MVPA] > 22 min/day). Generalized linear model with logit link results were reported as adjusted odds ratios (AOR) with 95% confidence intervals (CI). RESULTS: The sample (N = 114) was mostly female (88.6%), white (83.3%), college educated (73.7%), and on average 51.4 ± 1.0 years of age. At baseline, persons residing in car-dependent neighborhoods tended to have higher income than those in walkable neighborhoods. Neighborhood walkability interacted with household income at study entry to predict participants' ability to meet the MVPA goal at 12 months (AOR = 13.52, 95% CI: 1.86-119.20). Those from walkable neighborhoods had 67% lower odds of having prediabetes compared to those from car-dependent neighborhoods (AOR = 0.33, 95% CI: 0.10-0.87) at 12 months. CONCLUSION: Our findings corroborate previous research characterizing the relationship between neighborhood walkability, PA, and prediabetes status. Key drivers of this impact warrant further investigation in a study with a larger, more diverse sample.

The Association between Neighborhood Walkability and Physical Activity in a Behavioral Weight Loss Trial Testing the Addition of Remotely Delivered Feedback Messages to Self-Monitoring.

Adding feedback messages (FB) to self-monitoring (SM) may reinforce behavior change. However, socio-environmental conditions (e.g., limited access to parks or low walkability) may limit one's ability to respond to FB focused on physical activity (PA). In this analysis, we hypothesized that high neighborhood walkability will positively modify the treatment effect of FB on PA, and residents of high walkability neighborhoods will achieve higher PA levels at 12 months than those in low walkability neighborhoods. The study is a secondary analysis of a 12-month behavioral weight-loss trial. Adults with overweight/obesity were randomized to SM + FB (n = 251) or SM alone (n = 251). SM + FB group received smartphone pop-up messages thrice/week tailored to their PA SM data. The assessment included neighborhood walkability via Walk Score (low [<50] vs. high [≥50]), moderate to vigorous PA (MVPA) and step count via Fitbit Charge 2™, and weight via smart scale. We report adjusted linear regression coefficients (b) with standard errors (SE). The analysis included participants who were primarily white, female, and with obesity. In adjusted models, neighborhood walkability did not moderate the effect of treatment assignment on log-transformed (ln) MVPA or steps count over 12 months. The SM + FB group had greater lnMVPA than the SM group, but lnMVPA and steps were similar between walkability groups. There were no significant interactions for group and time or group, time, and walkability. These findings suggest that adding FB to SM had a small but significant positive impact on PA over 12 months, but neighborhood walkability did not moderate the treatment effect of FB on PA.

Program Factors Affecting Weight Loss and Mobility in Older Adults: Evidence From the Mobility and Vitality Lifestyle Program (MOVE UP).

Background. The Mobility and Vitality Lifestyle Program (MOVE UP) is a behavioral weight-management intervention for improving mobility among community-dwelling older adults. We examined program factors that affect implementation outcomes and participant-level health outcomes. Methods. The MOVE UP program was implemented in the greater Pittsburgh area from January 2015 to June 2019 to improve lower extremity performance in community-dwelling older adults who were overweight or obese. Thirty-two sessions were delivered over 13 months. All sessions were designed to be 1-hour in length, on-site, group-based, and led by trained and supported community health workers (CHWs). Participants completed weekly Lifestyle Logs for self-monitoring of body weight, diet, and physical activity. We evaluated the MOVE UP program using the RE-AIM framework, and collected quantitative data at baseline, 5-, 9-, and 13-months. Multilevel linear regression models assessed the impacts of program factors (site, CHW, and participant characteristics) on implementation outcomes and participant-level health outcomes. Results. Twenty-two CHWs delivered MOVE UP program to 303 participants in 26 cohorts. Participants were similar to the target source population in weight but differed in some demographic characteristics. The program was effective for weight loss and lower extremity function in both intervention and maintenance periods (Ps < .01), with an independent effect for Lifestyle Logs submission but not session attendance. Discussion. CHWs were able to deliver a multi-component weight loss intervention effectively in community settings. CHW and site characteristics had independent impacts on participants' adherence. Lifestyle Log submission may be a more potent measure of adherence in weight loss interventions than attendance.

Examining reactivity to intensive longitudinal ecological momentary assessment: 12-month prospective study.

PURPOSE: To examine the association between intensive, longitudinal ecological momentary assessment (EMA) and self-reported eating behaviors. METHODS: Secondary analysis of the EMPOWER study-a 12-month observational study that examined the microprocesses of relapse following intentional weight loss using smartphone-administered EMA-was conducted. Participants were asked to complete four types of EMA surveys using a mobile app. For this analysis, only the number of completed random EMA surveys was used. Using linear mixed-effects modeling, we analyzed whether the number of completed random EMA surveys was associated with changes in self-reported dietary restraint, dietary disinhibition, and susceptibility to hunger measured using the Three-Factor Eating Questionnaire (TFEQ). RESULTS: During the 12-month study, 132 participants completed a mean of 1062 random EMA surveys (range: 673-1362). The median time it took for participants to complete random EMA surveys was 20 s and 90% of random EMA surveys were completed within 46 s. The number of completed random EMA surveys was not significantly associated with the TFEQ scores. CONCLUSIONS: Intensive longitudinal EMA did not influence self-reported eating behaviors. The findings suggest that EMA can be used to frequently assess real-world eating behaviors with minimal concern about assessment reactivity. Nonetheless, care must be taken when designing EMA surveys-particularly when using self-reported outcome measures. LEVEL OF EVIDENCE: Level III, prospective observational study.

Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association.

The global obesity epidemic is well established, with increases in obesity prevalence for most countries since the 1980s. Obesity contributes directly to incident cardiovascular risk factors, including dyslipidemia, type 2 diabetes, hypertension, and sleep disorders. Obesity also leads to the development of cardiovascular disease and cardiovascular disease mortality independently of other cardiovascular risk factors. More recent data highlight abdominal obesity, as determined by waist circumference, as a cardiovascular disease risk marker that is independent of body mass index. There have also been significant advances in imaging modalities for characterizing body composition, including visceral adiposity. Studies that quantify fat depots, including ectopic fat, support excess visceral adiposity as an independent indicator of poor cardiovascular outcomes. Lifestyle modification and subsequent weight loss improve both metabolic syndrome and associated systemic inflammation and endothelial dysfunction. However, clinical trials of medical weight loss have not demonstrated a reduction in coronary artery disease rates. In contrast, prospective studies comparing patients undergoing bariatric surgery with nonsurgical patients with obesity have shown reduced coronary artery disease risk with surgery. In this statement, we summarize the impact of obesity on the diagnosis, clinical management, and outcomes of atherosclerotic cardiovascular disease, heart failure, and arrhythmias, especially sudden cardiac death and atrial fibrillation. In particular, we examine the influence of obesity on noninvasive and invasive diagnostic procedures for coronary artery disease. Moreover, we review the impact of obesity on cardiac function and outcomes related to heart failure with reduced and preserved ejection fraction. Finally, we describe the effects of lifestyle and surgical weight loss interventions on outcomes related to coronary artery disease, heart failure, and atrial fibrillation.

Community-based, cluster-randomized pilot trial of a cardiovascular mHealth intervention: Rationale, design, and baseline findings of the FAITH! Trial.

BACKGROUND: Compared to whites, African-Americans have lower prevalence of ideal cardiovascular health (CVH) based on the American Heart Association Life's Simple 7 (LS7). These CVH inequities have worsened during the COVID-19 pandemic. Ideal LS7 health-promoting behaviors and biological risk factors (eg, diet, blood pressure) are associated with improved CVH outcomes. The FAITH! (Fostering African-American Improvement in Total Health) App, a community-informed, mobile health (mHealth) intervention, previously demonstrated significant improvements in LS7 components among African-Americans, suggesting that mHealth interventions may be effective in improving CVH. This paper presents the FAITH! Trial design, baseline findings, and pandemic-related lessons learned. METHODS: Utilizing a community-based participatory research approach, this study assessed the feasibility/preliminary efficacy of a refined FAITH! App for promoting LS7 among African-Americans in faith communities using a cluster, randomized controlled trial. Participants received the FAITH! App (immediate intervention) or were assigned to a delayed intervention comparator group. Baseline data were collected via electronic surveys and health assessments. Primary outcomes are change in LS7 score from baseline to 6-months post-intervention and app engagement/usability. RESULTS: Of 85 enrolled individuals, 76 completed baseline surveys/health assessments, for a participation rate of 89% (N = 34 randomized to the immediate intervention, N = 42 to delayed intervention). At baseline, participants were predominantly female (54/76, 71%), employed (56/76, 78%) and of high cardiometabolic risk (72/76, 95% with hypertension and/or overweight/obesity) with mean LS7 scores in the poor range (6.8, SD = 1.9). CONCLUSIONS: The FAITH! Trial recruitment was feasible, and its results may inform the use of mHealth tools to increase ideal CVH among African-Americans.

Effect of Treatment of OSA With CPAP on Glycemic Control in Adults With Type 2 Diabetes: The Diabetes Sleep Treatment Trial (DSTT).

OBJECTIVE: The effect of obstructive sleep apnea (OSA) treatment with continuous positive airway pressure (CPAP) on glycemic measures in patients with type 2 diabetes (T2D) remains unclear. We aimed to determine whether CPAP treatment of OSA improves glycemic measures in patients with T2D. METHODS: This randomized controlled trial (N = 98) examined changes in glycemic measures following 12 weeks of active (n = 49) or sham (n = 49) CPAP and consideried participants' adherence to CPAP therapy (percentage of days with ≥4 hours use and average hours/day of use). RESULTS: Baseline treatment groups were similar. Regarding the efficacy of active vs sham-CPAP over time, at 6 weeks, both groups had similar reductions in fructosamine (mean difference [MD], 95% confidence interval [CI]: CPAP -13.10 [-25.49 to -0.7] vs. sham -7.26 [-20.2 to 5.69]; P = .519) but different in HbA1c (CPAP -0.24 [-0.48 to -0.003] vs sham 0.15 [-0.10 to 0.4]; P = .027). At 12 weeks, reductions in HbA1c values were similar by group (CPAP -0.26 [-0.53 to 0.002] vs sham -0.24 [-0.53 to 0.04]; P = .924). HbA1c reductions were associated with a greater percentage of cumulative days of CPAP usage ≥4 hours per day (b [SE] = 0.006 [0.002]; P = .013) and cumulative hours of CPAP use (b [SE] = 0.08 [0.08]; P = .012). CPAP use of ≥7 hours was associated with a significant reduction in HbA1c (b [SE] 0.54 [0.16]; P = .0012). CONCLUSION: CPAP treatment of OSA did not result in sustained improved glycemic control compared to sham in the intent-to-treat analysis. CPAP adherence was associated with greater improvements in glycemic control.

Metabolic outcomes in adults with type 2 diabetes and sleep disorders.

PURPOSE: Insomnia is frequently co-morbid with obstructive sleep apnea (OSA); the effect of insomnia or co-morbid insomnia and OSA (OSA + I) on associated metabolic outcomes in adults with type 2 diabetes (T2D) remains unclear. This study in adults with T2D compared metabolic outcomes among persons with OSA, insomnia, or OSA + I. METHODS: This study analyzed baseline data from the Diabetes Sleep Treatment Trial of persons recruited for symptoms of OSA or poor sleep quality. Home sleep studies determined OSA presence and severity. Insomnia was evaluated using the Insomnia Severity Index. Height and weight to calculate body mass index (BMI) and blood for laboratory values were obtained. Multivariate general linear models were used to examine the impact of the type of sleep disorder and sociodemographic, lifestyle, and sleep risk factors on metabolic outcomes. RESULTS: Participants (N = 253) were middle-aged (56.3 ± 10.5 years), white (60.5%), obese (mean BMI of 35.3 ± 7.1 kg/m2), and male (51.4%) with poor glucose control (mean HbA1c of 8.0 ± 1.8%). Most participants had OSA + I (42.7%) or insomnia only (41.0%). HbA1c and BMI differed among the sleep disorder groups. In addition, in the adjusted models, having insomnia only, compared to OSA only, was associated on average with higher HbA1c levels (b = 1.08 ± 0.40, p < 0.007) and lower BMI (b = - 7.03 ± 1.43, p < 0.001). CONCLUSIONS: Findings suggest that insomnia frequently co-exists with OSA, is independently associated with metabolic outcomes in adults with T2D, and should be considered in investigations of the effects of OSA in persons with T2D. TRIAL REGISTRATION: Diabetes-Obstructive Sleep Apnea Treatment Trial (NCT01901055), https: Clinicaltrials.gov/ct2/show/NCT01901055; Registration date: July 17, 2013.

Nightly Variation in Sleep Influences Self-efficacy for Adhering to a Healthy Lifestyle: A Prospective Study.

BACKGROUND: Self-efficacy, or the perceived capability to engage in a behavior, has been shown to play an important role in adhering to weight loss treatment. Given that adherence is extremely important for successful weight loss outcomes and that sleep and self-efficacy are modifiable factors in this relationship, we examined the association between sleep and self-efficacy for adhering to the daily plan. Investigators examined whether various dimensions of sleep were associated with self-efficacy for adhering to the daily recommended lifestyle plan among participants (N = 150) in a 12-month weight loss study. METHOD: This study was a secondary analysis of data from a 12-month prospective observational study that included a standard behavioral weight loss intervention. Daily assessments at the beginning of day (BOD) of self-efficacy and the previous night's sleep were collected in real-time using ecological momentary assessment. RESULTS: The analysis included 44,613 BOD assessments. On average, participants reported sleeping for 6.93 ± 1.28 h, reported 1.56 ± 3.54 awakenings, and gave low ratings for trouble sleeping (3.11 ± 2.58; 0: no trouble; 10: a lot of trouble) and mid-high ratings for sleep quality (6.45 ± 2.09; 0: poor; 10: excellent). Participants woke up feeling tired 41.7% of the time. Using linear mixed effects modeling, a better rating in each sleep dimension was associated with higher self-efficacy the following day (all p values < .001). CONCLUSION: Our findings supported the hypothesis that better sleep would be associated with higher levels of reported self-efficacy for adhering to the healthy lifestyle plan.

The Effect of Tailored, Daily, Smartphone Feedback to Lifestyle Self-Monitoring on Weight Loss at 12 Months: the SMARTER Randomized Clinical Trial.

BACKGROUND: Self-monitoring (SM) is the centerpiece of behavioral weight loss treatment, but the efficacy of smartphone-delivered SM feedback (FB) has not been tested in large, long-term, randomized trials. OBJECTIVE: The aim of this study was to establish the efficacy of providing remote FB to diet, physical activity (PA), and weight SM on improving weight loss outcomes when comparing the SM plus FB (SM+FB) condition to the SM-only condition in a 12-month randomized controlled trial. The study was a single-site, population-based trial that took place in southwestern Pennsylvania, USA, conducted between 2018 and 2021. Participants were smartphone users age ≥18 years, able to engage in moderate PA, with a mean BMI between 27 and 43 kg/m2. METHODS: All participants received a 90-minute, one-to-one, in-person behavioral weight loss counseling session addressing behavioral strategies, establishing participants' dietary and PA goals, and instructing on use of the PA tracker (Fitbit Charge 2), smart scale, and diet SM app. Only SM+FB participants had access to an investigator-developed smartphone app that read SM data, in which an algorithm selected tailored messages sent to the smartphone up to 3 times daily. The SM-only participants did not receive any tailored FB based on SM data. The primary outcome was percent weight change from baseline to 12 months. Secondary outcomes included engagement with digital tools (eg, monthly percentage of FB messages opened and monthly percentage of days adherent to the calorie goal). RESULTS: Participants (N=502) were on average 45.0 (SD 14.4) years old with a mean BMI of 33.7 (SD 4.0) kg/m2. The sample was 79.5% female (n=399/502) and 82.5% White (n=414/502). At 12 months, retention was 78.5% (n=394/502) and similar by group (SM+FB: 202/251, 80.5%; SM: 192/251, 76.5%; P=.28). There was significant percent weight loss from baseline in both groups (SM+FB: -2.12%, 95% CI -3.04% to -1.21%, P<.001; SM: -2.39%, 95% CI -3.32% to -1.47%; P<.001), but no difference between the groups (-0.27%; 95% CI -1.57% to 1.03%; t =-0.41; P=.68). Similarly, 26.3% (66/251) of the SM+FB group and 29.1% (73/251) of the SM group achieved ≥5% weight loss (chi-square value=0.49; P=.49). A 1% increase in FB messages opened was associated with a 0.10 greater percent weight loss at 12 months (b=-0.10; 95% CI -0.13 to -0.07; t =-5.90; P<.001). A 1% increase in FB messages opened was associated with 0.12 greater percentage of days adherent to the calorie goal per month (b=0.12; 95% CI 0.07-0.17; F=22.19; P<.001). CONCLUSIONS: There were no significant between-group differences in weight loss; however, the findings suggested that the use of commercially available digital SM tools with or without FB resulted in a clinically significant weight loss in over 25% of participants. Future studies need to test additional strategies that will promote greater engagement with digital tools. TRIAL REGISTRATION: Clinicaltrials.gov NCT03367936; https://clinicaltrials.gov/ct2/show/NCT03367936.

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.

Factors Facilitating Academic-Community Research Partnerships With African American Churches: Recruitment Process for a Community-Based, Cluster Randomized Controlled Trial During the COVID-19 Pandemic.

African American (AA) churches are valuable partners in implementing health promotion programming (HPP) to combat health disparities. The study purpose was to evaluate AA church characteristics associated with enrollment into the FAITH! (Fostering African American Improvement in Total Health) Trial, a community-based, cluster randomized controlled trial (RCT) of a mobile health intervention for cardiovascular health promotion among AA churches. Churches located in Minneapolis-St. Paul and Rochester, Minnesota were invited to complete an electronic screening survey and follow-up telephone interview including the PREACH (Predicting Readiness to Engage African American Churches in Health) tool to assess church characteristics and infrastructure for HPP. The primary outcome was church enrollment in the FAITH! Trial. Key predictors included overall PREACH scores and its subscales (Personnel, Physical Structure, Faith-based Approach, Funding), congregation size, and mean congregation member age. Of the 26 churches screened, 16 (61.5%) enrolled in the trial. The enrolled churches had higher overall mean PREACH scores (36.1 vs. 30.2) and subscales for Personnel (8.8 vs. 5.6), Faith-based Approach (11.0 vs. 9.6), and Funding (7.3 vs. 4.8) compared with non-enrolled churches; all differences were not statistically significant due to small sample size. Twelve (75.0%) of the enrolled churches had >75 members versus six (60.0%) of the non-enrolled churches. Twelve (80.0%) of the enrolled churches had an average congregation member age ≤54 years versus six (67.0%) of the non-enrolled churches. AA churches enrolling into a community-based RCT reported greater infrastructure for HPP, larger congregations, and members of younger age. These characteristics may be helpful to consider among researchers partnering with AA churches for HPP studies.

The association between sleep health and weight change during a 12-month behavioral weight loss intervention.

BACKGROUND: Prior research on the relationship between sleep and attempted weight loss failed to recognize the multidimensional nature of sleep. We examined the relationship between a composite measure of sleep health and change in weight and body composition among adults in a weight loss intervention. METHODS: Adults (N = 125) with overweight or obesity (50.3 ± 10.6 years, 91% female, 81% white) participated in a 12-month behavioral weight loss intervention, with assessments of sleep, weight, fat mass, and fat-free mass at baseline, 6 months, and 12 months. Six sleep dimensions (regularity, satisfaction, alertness, timing, efficiency, and duration) were categorized as "good" or "poor" using questionnaires and actigraphy. A composite score was calculated by summing the number of "good" dimensions. Obstructive sleep apnea (OSA) was assessed in a subsample (n = 117), using the apnea-hypopnea index (AHI) to determine OSA severity. Linear mixed modeling was used to examine the relationships between sleep health and outcomes of percent weight, fat mass, or fat-free mass change during the subsequent 6-month interval, adjusting for age, sex, bed partner, and race; an additional model adjusted for AHI. RESULTS: Mean baseline and 6-month sleep health was 4.5 ± 1.1 and 4.5 ± 1.2, respectively. Mean weight, fat mass, and fat-free mass changes from 0 to 6 months were -9.3 ± 6.1%, -16.9 ± 13.5%, and -3.4 ± 3.4%, respectively, and 0.4 ± 4.8%, -0.3 ± 10.3%, and 0.7 ± 4.1% from 6 to 12 months. Better sleep health was associated with greater subsequent weight loss (P = 0.016) and fat loss (P = 0.006), but not fat-free mass loss (P = 0.232). Following AHI adjustment, the association between sleep health and weight loss was attenuated (P = 0.102) but remained significant with fat loss (P = 0.040). Regularity, satisfaction, timing, and efficiency were each associated with weight and/or fat loss (P ≤ 0.041). CONCLUSIONS: Better sleep health was associated with greater weight and fat loss, with associations attenuated after accounting for OSA severity. Future studies should explore whether improving sleep health, OSA, or the combination improves weight loss.

mHealth Technology and CVD Risk Reduction.

PURPOSE OF REVIEW: To review existing mHealth-based interventions and examine their efficacy in reducing cardiovascular disease (CVD) risk factors. RECENT FINDINGS: A total of 50 articles are included in this review. The majority of the mHealth interventions targeted a specific CVD risk factor, while 4 addressed 2 or more CVD risk factors. Of the 9 mHealth-supported weight loss intervention trials, 4 resulted in significant weight loss. Four out of 7 RCTs targeting improvement in physical activity reported significant improvement, while 4 of the 8 mHealth-supported smoking cessation intervention trials resulted in smoking abstinence. Of the 10 mHealth-based diabetes intervention trials, 5 reported significant reductions in HbA1c; however, only 3 out of the 9 antihypertension interventions resulted in significant reductions in blood pressure. There is a growing body of literature focused on mHealth interventions that address CVD risk factors. Despite the immense potential of mHealth interventions, evidence of their efficacy in mitigating cardiovascular risk is heterogeneous.

Problem-solving, Adherence to Lifestyle Goals, and Weight Loss Among Individuals Participating in a Weight Loss Study.

BACKGROUND: The role of problem-solving is not well understood in behavioral weight loss interventions. In a 12-month behavioral weight loss study, we examined whether problem-solving changed over time and the relationships between problem-solving and changes in adherence to calorie, fat, and physical activity (PA) goals and percent weight change. METHODS: One of the 24 intervention sessions (15th) was devoted to problem-solving. Participants received individualized calorie and fat goals and were given a 150 min/week moderate-to-vigorous PA goal. Adherence to calorie/fat goals and PA goals was calculated at 1, 6, and 12 months using self-reported food intake in a mobile-based weight loss app and accelerometer data, respectively. Weight was measured via a digital scale at baseline, and 6 and 12 months. A general linear model was used to compare problem-solving across time points; post hoc linear mixed modeling was used to examine the relationships between problem-solving and changes in adherence to lifestyle goals and percent weight change. RESULTS: The sample (N = 150) was mostly female (90.7%), white (80.70%), with a mean age of 51.1 ± 10.2 years, and a mean body mass index of 34.1 + 4.6 kg/m2. The mean total score of problem-solving at baseline was 81.2 ± 12.3. Problem-solving total and subscale scores did not significantly change over time. Baseline problem-solving was not significantly associated with changes in adherence to lifestyle goals and percent weight change (P > 0.05). CONCLUSION: A behavioral weight loss study did not impact problem-solving, and problem-solving may not influence lifestyle adherence and weight changes. Future work needs to examine problem-solving in larger and more diverse samples.