An Evaluation of Alternative Technology-Supported Counseling Approaches to Promote Multiple Lifestyle Behavior Changes in Patients With Type 2 Diabetes and Chronic Kidney Disease.

OBJECTIVES: Although technology-supported interventions are effective for reducing chronic disease risk, little is known about the relative and combined efficacy of mobile health strategies aimed at multiple lifestyle factors. The purpose of this clinical trial is to evaluate the efficacy of technology-supported behavioral intervention strategies for managing multiple lifestyle-related health outcomes in overweight adults with type 2 diabetes (T2D) and chronic kidney disease (CKD). DESIGN AND METHODS: Using a 2 × 2 factorial design, adults with excess body weight (body mass index ≥27 kg/m2, age ≥40 years), T2D, and CKD stages 2-4 were randomized to an advice control group, or remotely delivered programs consisting of synchronous group-based education (all groups), plus (1) Social Cognitive Theory-based behavioral counseling and/or (2) mobile self-monitoring of diet and physical activity. All programs targeted weight loss, greater physical activity, and lower intakes of sodium and phosphorus-containing food additives. RESULTS: Of 256 randomized participants, 186 (73%) completed 6-month assessments. Compared to the ADVICE group, mHealth interventions did not result in significant changes in weight loss, or urinary sodium and phosphorus excretion. In aggregate analyses, groups receiving mobile self-monitoring had greater weight loss at 3 months (P = .02), but between 3 and 6 months, weight losses plateaued, and by 6 months, the differences were no longer statistically significant. CONCLUSIONS: When engaging patients with T2D and CKD in multiple behavior changes, self-monitoring diet and physical activity demonstrated significantly larger short-term weight losses. Theory-based behavioral counseling alone was no better than baseline advice and demonstrated no interaction effect with self-monitoring.

Food insecurity, overweight and obesity among low-income African-American families in Baltimore City: associations with food-related perceptions.

OBJECTIVE: To examine associations between food insecurity, excess body weight, psychosocial factors and food behaviours among low-income African-American families. DESIGN: Cross-sectional survey of participants in the baseline evaluation of the B'More Healthy Communities for Kids (BHCK) obesity prevention trial. We collected data on socio-economic factors, food source destinations, acquiring food, preparation methods, psychosocial factors, beliefs and attitudes, participation in food assistance programmes, anthropometry and food security. We used principal component analysis to identify patterns of food source destinations and logistic regression to examine associations. SETTING: Fourteen low-income, predominantly African-American neighbourhoods in Baltimore City, MD, USA. SUBJECTS: Two hundred and ninety-eight adult caregiver-child (10-14 years old) dyads. RESULTS: Of households, 41·6 % had some level of food insecurity and 12·4 % experienced some level of hunger. Food-insecure participants with hunger were significantly more likely to be unemployed and to have lower incomes. We found high rates of excess body weight (overweight and obesity) among adults and children (82·8 % and 37·9 % among food insecure without hunger, 89·2 % and 45·9 % among food insecure with hunger, respectively), although there were no significant differences by food security status. Food source usage patterns, food acquisition, preparation, knowledge, self-efficacy and intentions did not differ by food security. Food security was associated with perceptions that healthy foods are affordable and convenient. Greater caregiver body satisfaction was associated with food insecurity and excess body weight. CONCLUSIONS: In this setting, obesity and food insecurity are major problems. For many food-insecure families, perceptions of healthy foods may serve as additional barriers to their purchase and consumption.

B'More Healthy Communities for Kids: design of a multi-level intervention for obesity prevention for low-income African American children.

BACKGROUND: Childhood obesity rates in the U.S. have reached epidemic proportions, and an urgent need remains to identify evidence-based strategies for prevention and treatment. Multi-level, multi-component interventions are needed due to the multi-factorial nature of obesity, and its proven links to both the social and built environment. However, there are huge gaps in the literature related to doing these kinds of interventions among low-income, urban, minority groups. METHODS: The B'More Healthy Communities for Kids (BHCK) intervention is a multi-level, multi-component intervention, targeting low-income African American youth ages 10-14 and their families in Baltimore, Maryland. This intervention prevents childhood obesity by working at multiple levels of the food and social environments to increase access to, demand for, and consumption of healthier foods. BHCK works to create systems-level change by partnering with city policy-makers, multiple levels of the food environment (wholesalers, corner stores, carryout restaurants), and the social environment (peers and families). In addition, extensive evaluation will be conducted at each level of the intervention to assess intervention effectiveness via both process and impact measures. DISCUSSION: This project is novel in multiple ways, including: the inclusion of stakeholders at multiple levels (policy, institutional, and at multiple levels of the food system); that it uses novel computational modeling methodologies to engage policy makers and guide informed decisions of intervention effectiveness; it emphasizes both the built environment (intervening with food sources) and the social environment (intervening with families and peers). The design of the intervention and the evaluation plan of the BHCK project are documented here. TRIAL REGISTRATION: NCT02181010 (July 2, 2014).

Self-reported adherence and reasons for nonadherence among patients with low proportion of days covered for antihypertension medications.

BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.

A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c.

BACKGROUND: Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR. OBJECTIVE: This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study. METHODS: The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions. RESULTS: We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values. CONCLUSIONS: Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.

Monday-focused tailored rapid interactive mobile messaging for weight management 2 (MTRIMM2): results from a randomized controlled trial.

BACKGROUND: Text-messaging interventions can reach many individuals across a range of socioeconomic groups, at a low cost. Few randomized controlled trials (RCTs) of text-messaging weight loss interventions have been conducted in United States. METHODS: From September of 2016 to September of 2018, we conducted a two-parallel group, superiority, RCT of a 16-week text-messaging, weight loss intervention in Baltimore, Maryland, in overweight and obese adults younger than 71, who were able to receive text-messages. Our objective was to assess the effect of receiving the message content only (in printed documents distributed at baseline and week 8), versus receiving messages via short messaging service (SMS) on weight loss (primary outcome), body mass index, perceived exercise benefits and barriers, self-efficacy, and physical activity (PA). The random allocation sequence was equally balanced intervention groups by gender and age groups. Participants were randomized after the baseline assessment. Then, participants and most study staff were unblinded. Follow-up assessments were conducted at 8-, 16-, and 42-week post randomization. We performed intention-to-treat analysis using mixed linear regression models. RESULTS: Of the 155 adults randomized (printed messages =77, SMS =78), 87.1% were women, 53.5% were African Americans, and 93.5% non-Hispanic. Participants who completed at least one follow-up assessment were included in regression analyses (n=145, printed messages =74, SMS =71). Compared to baseline, at the 42-week assessment, the average percent weight loss was 1.23 for the SMS group (P=0.006) and 0.86 for the printed messages group (P=0.047). Both groups experienced small reductions in weight (printed messages: -0.96 kg, P=0.022; SMS: -1.19 kg, P=0.006), BMI (printed messages: -0.32, P=0.035; SMS: -0.52, P=0.002), and percent energy from fat consumption (printed messages: -1.43, P=0.021; SMS: -2.14, P≤0.001). No statistically significant between groups differences were detected for any of the study outcomes. SMS response rates were not statistically significantly associated with study outcomes. No adverse events were reported. CONCLUSIONS: A semi-tailored SMS weight loss intervention among overweight and obese adults was not statistically superior in efficacy to paper-based messaging. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04506996.

Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial.

Importance: Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. Objective: To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. Design, Setting, and Participants: The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. Interventions: Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. Main Outcomes and Measures: The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. Results: Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). Conclusions and Relevance: A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure. Trial Registration: ClinicalTrials.gov Identifier: NCT03336411.

Challenges of conducting a remote behavioral weight loss study: Lessons learned and a practical guide.

OBJECTIVES: To describe challenges and lessons learned in conducting a remote behavioral weight loss trial. METHODS: The Personal Diet Study is an ongoing randomized clinical trial which aims to compare two mobile health (mHealth) weight loss approaches, standardized diet vs. personalized feedback, on glycemic response. Over a six-month period, participants attended dietitian-led group meetings via remote videoconferencing and were encouraged to self-monitor dietary intake using a smartphone app. Descriptive statistics were used to report adherence to counseling sessions and self-monitoring. Challenges were tracked during weekly project meetings. RESULTS: Challenges in connecting to and engaging in the videoconferencing sessions were noted. To address these issues, we provided a step-by-step user manual and video tutorials regarding use of WebEx, encouraged alternative means to join sessions, and sent reminder emails/texts about the WebEx sessions and asking participants to join sessions early. Self-monitoring app-related issue included inability to find specific foods in the app database. To overcome this, the study team incorporated commonly consumed foods as "favorites" in the app database, provided a manual and video tutorials regarding use of the app and checked the self-monitoring app dashboard weekly to identify nonadherent participants and intervened as appropriate. Among 135 participants included in the analysis, the median attendance rate for the 14 remote sessions was 85.7% (IQR: 64.3%-92.9%). CONCLUSIONS: Experience and lessons shared in this report may provide critical and timely guidance to other behavioral researchers and interventionists seeking to adapt behavioral counseling programs for remote delivery in the age of COVID-19.

Identification of Measurement Needs to Prevent Childhood Obesity in High-Risk Populations and Environments.

INTRODUCTION: Children at highest obesity risk include those from certain racial/ethnic groups, from low-income families, with disabilities, or living in high-risk communities. However, a 2013 review of the National Collaborative for Childhood Obesity Research Measures Registry identified few measures focused on children at highest obesity risk. The objective is to (1) identify individual and environmental measures of diet and physical activity added to the Measures Registry since 2013 used among high-risk populations or settings and (2) describe methods for their development, adaptation, or validation. METHODS: Investigators screened references in the Measures Registry from January 2013 to September 2017 (n=351) and abstracted information about individual and environmental measures developed for, adapted for, or applied to high-risk populations or settings, including measure type, study population, adaptation and validation methods, and psychometric properties. RESULTS: A total of 38 measures met inclusion criteria. Of these, 30 assessed individual dietary (n=25) or physical activity (n=13) behaviors, and 11 assessed the food (n=8) or physical activity (n=7) environment. Of those, 17 measures were developed for, 9 were applied to (i.e., developed in a general population and used without modification), and 12 were adapted (i.e., modified) for high-risk populations. Few measures were used in certain racial/ethnic groups (i.e., American Indian/Alaska Native, Hawaiian/Pacific Islander, and Asian), children with disabilities, and rural (versus urban) communities. CONCLUSIONS: Since 2013, a total of 38 measures were added to the Measures Registry that were used in high-risk populations. However, many of the previously identified gaps in population coverage remain. Rigorous, community-engaged methodologic research may help researchers better adapt and validate measures for high-risk populations.

Healthy versus Unhealthy Suppliers in Food Desert Neighborhoods: A Network Analysis of Corner Stores' Food Supplier Networks.

BACKGROUND: Products in corner stores may be affected by the network of suppliers from which storeowners procure food and beverages. To date, this supplier network has not been well characterized. METHODS: Using network analysis, we examined the connections between corner stores (n = 24) in food deserts of Baltimore City (MD, USA) and their food/beverage suppliers (n = 42), to determine how different store and supplier characteristics correlated. RESULTS: Food and beverage suppliers fell into two categories: Those providing primarily healthy foods/beverages (n = 15) in the healthy supplier network (HSN) and those providing primarily unhealthy food/beverages (n = 41) in the unhealthy supplier network (UHSN). Corner store connections to suppliers in the UHSN were nearly two times greater (t = 5.23, p < 0.001), and key suppliers in the UHSN core were more diverse, compared to the HSN. The UHSN was significantly more cohesive and densely connected, with corner stores sharing a greater number of the same unhealthy suppliers, compared to HSN, which was less cohesive and sparsely connected (t = 5.82; p < 0.001). Compared to African Americans, Asian and Hispanic corner storeowners had on average -1.53 (p < 0.001) fewer connections to suppliers in the HSN (p < 0.001). CONCLUSIONS: Our findings indicate clear differences between corner stores' HSN and UHSN. Addressing ethnic/cultural differences of storeowners may also be important to consider.

Relation between the Supplemental Nutritional Assistance Program cycle and dietary quality in low-income African Americans in Baltimore, Maryland.

BACKGROUND: There has been limited research regarding the Supplemental Nutritional Assistance Program (SNAP) and recipients' dietary quality during the days and weeks after benefit disbursement. OBJECTIVE: We examined the relation between participants' stages in the SNAP cycle and their macronutrient consumption, Healthy Eating Index (HEI) scores, and fruit and vegetable intake. DESIGN: In this cross-sectional study, we analyzed single 24-h dietary recalls collected from 244 African American SNAP participants recruited near 24 corner stores in Baltimore City. A multiple linear regression analysis and bootstrapping were used. RESULTS: Among participants who received a SNAP benefit ≤15 d before being surveyed, energy intake adjusted for minimum energy requirements (-4.49%; 95% CI: -8.77%, -0.15%) and HEI dairy scores (-0.12; 95% CI: -0.22, -0.01) were lower for each 1-d increase in the time since SNAP distribution (TSSD). Among participants who received SNAP benefits >15 d before being surveyed, energy intake (1.35%; 95% CI: 0.01%, 2.73%), energy intake adjusted for minimum energy requirements (3.86%; 95% CI: 0.06%, 7.96%), total fat intake (1.96%; 95% CI: 0.29%, 3.8%), saturated fat intake (2.02%; 95% CI: 0.23%, 4.01%), and protein intake (2.09%; 95% CI: 0.70%, 3.62%) were higher per each 1-d increase in the TSSD. CONCLUSIONS: These findings suggest that the relation between the TSSD and macronutrient intake might be U-shaped, with higher intake of calories, fat, and protein in individuals in the very early and late stages of their SNAP cycles. Foods high in these nutrients might be cheaper, more accessible, and have a longer shelf-life than healthier options, such as fruit, vegetables, and whole grains, for SNAP participants when their benefits run out. Additional efforts are needed to investigate the effect of the TSSD on dietary intake by using a longitudinal design and to improve the quality of dietary intake in African American SNAP participants.