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Introduction Effective, standardized, and easily accessible weight management programs are urgently needed for military beneficiaries. Videoconference interventions have the potential for widespread scaling, and can provide both real time interaction and flexibility in delivery times regardless of location, but there is little information on their effectiveness and acceptability. Materials and Methods This study as part of a larger weight loss trial describes the videoconference adaption of Group Lifestyle Balance (GLB) program, a community group-based Diabetes Prevention Program intervention, and provides a comparison of weight loss and meeting attendance between in-person and videoconference delivery modes over 12 weeks in adult family members of military service members. Forty-three participants were enrolled from two military installations and received either the videoconference-adapted or an in-person GLB program in a non-randomized trial design. Differences in program attendance and percent weight lost at 12 weeks were compared by independent samples t-tests and nonparametric methods. Group differences in the percentage of weight lost over the 12-week period were analyzed using a linear mixed model. Results All GLB intervention components were successfully delivered by videoconference with minor adaptations for the different delivery mechanism. Participant retention was 70% and 96% in the in-person and videoconference groups, respectively (p = 0.04). Completing participants in both groups lost a significant percent body weight over the 12 week intervention (p < 0.001) and there was no difference in percent body weight after 12 weeks of intervention (6.2 ± 3.2% and 5.3 ± 3.4% for in-person and videoconference at 12 weeks, respectively; p = 0.60). Conclusion This study describes the first videoconference adaption of the GLB program for use in military families. Attrition was lower in the videoconference group, and there were a similar levels of weight loss in both groups regardless of delivery modality. Videoconference weight loss interventions are effective and feasible for scaling to support healthy weight management in military as well as civilian populations.

OBJECTIVES: Emerging evidence suggests that anti-inflammatory dietary patterns may improve depression, stress, and quality of life. Similar benefits are observed during weight loss interventions; however, the effect of combining these dietary patterns with weight loss is not clear. We evaluated the association between dietary inflammation and measures of psychological health in female military dependents with overweight and obesity during a behavioral weight loss intervention. METHODS: Participants (n = 120) received a 6-month intervention recommending dietary patterns with an anti-inflammatory profile. An energy-adjusted dietary inflammatory index (DII) score was calculated from 28 food components collected using 3 interviewer-administered multiple-pass 24-hour recalls at each study time point. A negative score indicates a lower-inflammatory dietary pattern, and a positive score indicates a pro-inflammatory pattern. Self-reported symptoms of depression, stress, general health, vitality, and psychological quality of life were measured by validated questionnaires. Multivariable linear mixed models were used to assess the association between 6-month changes in DII and each outcome and were adjusted for age, ethnicity, military rank of spouse, body weight, and physical activity. RESULTS: On average, females were 42.2 ± 11.8 years old with a body mass index of 34.9 ± 6.4 kg/m(2). The average DII score was 0.07 (range: −4.3, 3.0) at baseline. Six-month improvements in DII scores (i.e., for a 1-unit reduction) were associated with fewer self-reported symptoms of depression (β: −0.87; 95% confidence interval: −1.43, −0.31; P = 0.003) and stress (−0.85; −1.44, −0.26; P = 0.005), as well as higher general health (0.57; 0.23, 0.91; P = 0.001), vitality (0.45; 0.12, 0.77; P = 0.008), and psychological quality of life (0.33; 0.11, 0.55; P = 0.004). No significant associations between weight loss or physical activity and any outcome were observed in these models that included the DII. CONCLUSIONS: Anti-inflammatory dietary patterns are associated with beneficial effects on a range of psychological health and wellbeing measures independent of weight loss. FUNDING SOURCES: Jean Mayer USDA Human Nutrition Research Center on Aging Doctoral Scholarship; Department of Defense (DoD) grant W81XWH-14–2-0005 (SBR). Author views do not reflect official DoD or Army policy.

OBJECTIVES: There is persistent controversy over the extent to which different components of energy expenditure disproportionately decrease after weight loss and contribute to weight regain through decreased energy requirements. We conducted a secondary analysis of the CALERIE I study to test the hypothesis that decreased resting metabolic rate (RMR) and energy expenditure for physical activity (EEPA) after a 6-month calorie restriction intervention would predict weight regain at 12 months, with a greater decrease in RMR than EEPA. METHODS: Participants (n = 46) received all food and energy-containing beverages for 6 months. Outcome measures included total energy expenditure by doubly labeled water, RMR by indirect calorimetry, and body composition by BOD POD. Predictions for RMR and EEPA were derived from baseline linear regression models including age, sex, fat mass, and fat free mass. Baseline regression coefficients were used to calculate the predicted RMR and EEPA at 6 months. Residuals were calculated as the difference between measured and predicted values and were adjusted for body weight. The presence of metabolic adaptation was evaluated by a paired t-test comparing measured and predicted RMR at 6 months. Differences between 6-month RMR and EEPA residuals were evaluated by the same method. Linear regression was used to assess the association between 6-month residuals and weight loss maintenance (% weight change, 6 to 12 months). RESULTS: Mean weight loss was 6.9% at 6 months with 2.1% regain from 6 to 12 months. No adaptation in RMR was observed at 6 months (mean residual: 19 kcal; 95% confidence interval: −9, 48; P = 0.18). However, significant adaptation was observed in EEPA (mean residual: −199 kcal; −126, −272; P

The amount of time spent in poor health at the end of life is increasing. This narrative review summarizes consistent evidence indicating that healthy dietary patterns and maintenance of a healthy weight in the years leading to old age are associated with broad prevention of all the archetypal diseases and impairments associated with aging including: noncommunicable diseases, sarcopenia, cognitive decline and dementia, osteoporosis, age-related macular degeneration, diabetic retinopathy, hearing loss, obstructive sleep apnea, urinary incontinence, and constipation. In addition, randomized clinical trials show that disease-specific nutrition interventions can attenuate progression-and in some cases effectively treat-many established aging-associated conditions. However, middle-aged and older adults are vulnerable to unhealthy dietary patterns, and typically consume diets with inadequate servings of healthy food groups and essential nutrients, along with an abundance of energy-dense but nutrient-weak foods that contribute to obesity. However, based on menu examples, diets that are nutrient-dense, plant-based, and with a moderately low glycemic load are better equipped to meet the nutritional needs of many older adults than current recommendations in US Dietary Guidelines. These summary findings indicate that healthy nutrition is more important for healthy aging than generally recognized. Improved public health messaging about nutrition and aging, combined with routine screening and medical referrals for age-related conditions that can be treated with a nutrition prescription, should form core components of a national nutrition roadmap to reduce the epidemic of unhealthy aging.

OBJECTIVES: Self-monitoring daily dietary intake is recommended for weight loss and weight loss maintenance. However, current online platforms and applications are often burdensome, which may limit use. We conducted a pilot study to evaluate the accuracy of a new application designed to self-monitor dietary intake using natural spoken language (COCO; The Conversational Calorie Counter). METHODS: A total of 35 participants were enrolled in this pilot study. Participants were asked to record daily dietary intake using the COCO application for a period of at least five days. Two 24-hour dietary recalls were conducted during this time, between day three and day five, and served as the reference method for evaluating total energy intake (TEI; measured in kcal). Mean two-day energy intake was calculated for each assessment method for the days when the 24-hr recall and COCO data were collected. Self-reported TEI from COCO were compared to estimates obtained from the 24-hour dietary recalls by a paired samples t-test and a Pearson's correlation coefficient. RESULTS: On average, participants consumed three meals a day and recorded six days of food intake days with COCO (range: 4 to 10 days). The mean TEI was not significantly different between the two methods (1902 ± 621 kcal by 24-hour dietary recall and 1988 ± 1033 kcal by COCO, P = 0.59). There was a significant correlation between mean TEI measured with the two methods (r = 0.45; P = 0.006). In addition, a strong correlation was observed between the number of food items logged in COCO and those recalled in the 24-hour diet recalls (r = 0.82; P >0.0001). Completion of the exit survey by 28 participants indicated that 43% would definitely or probably use the application again. CONCLUSIONS: These results suggest that natural spoken language technology may have utility in applications to self-monitor food intake. Additional research is required to fully elucidate the validity of COCO in estimating dietary intake. FUNDING SOURCES: This research was supported by the NIH Grant # 1R21HL118347–01 (SBR and JG), Quanta Computing, Inc., and the National Defense Science and Engineering Graduate fellowship.

OBJECTIVES: Self-monitoring daily dietary intake is recommended for weight loss, weight maintenance, and healthy eating. However, current tracking methods are often burdensome and result in short-term use. We conducted a pilot study to evaluate the accuracy of a new application designed to self-monitor dietary intake using natural spoken language (COCO; The Conversational Calorie Counter). METHODS: A total of 14 participants were recruited for the pilot study. They were instructed to record daily dietary intake using the COCO application for at least five consecutive days. Two unscheduled 24-hour dietary recalls were conducted between day 3 and day 5 as the reference method for evaluating total energy intake (TEI). The two-day energy estimates were averaged for each assessment method. Pearson's correlation coefficient was used to assess the validity of the COCO application. Estimates of TEI from COCO were compared to the 24-hour dietary recall by a paired samples t-test. RESULTS: Participants were primarily female (86%), with an average body mass index of 22.2 ± 1.8 kg/m(2) (mean ± standard deviation). On average, participants consumed three daily meals and recorded dietary intake for six days using the COCO application. The average TEI was 1782 ± 773 kcal for all recorded days (range: 4 to 10). The mean TEI measured by 24-hour dietary recall was 1791 ± 862 kcal, and mean TEI measured by COCO for the corresponding days was 1818 ± 916 kcal. We observed a significant correlation between the assessment methods (r = 0.58; P = 0.03), and there was no significant difference in TEI estimates from COCO compared to the 24-hour recall (P = 0.90). CONCLUSIONS: These results suggest that natural spoken language technology can be used in applications that facilitate self-monitoring of food intake to support weight management and the prevention of noncommunicable diseases. The significant correlation between estimates of TEI from COCO and the 24-hour dietary recall indicates the potential validity of this novel approach for capturing dietary data and assessing energy intake. FUNDING SOURCES: Sponsored by the National Institutes of Health (R21HL118347), the U.S. Department of Agriculture with Tufts University (58–1950-4–003), Quanta Computing, Inc., and the Department of Defense (National Defense Science Engineering Graduate Fellowship Program).

Purpose There is growing evidence of the importance of nutrition in age-related macular degeneration (AMD), but no prospective studies have explored the impact of vitamin D. We evaluated the association between vitamin D intake and progression to advanced AMD. Methods Among 2146 participants (3965 eyes), 541 (777 eyes) progressed from early or intermediate AMD to advanced disease (mean follow-up: 9.4 years) based on ocular imaging. Nutrients were log transformed and calorie adjusted. Survival analysis was used to assess associations between incident advanced disease and vitamin D intake. Neovascular disease (NV) and geographic atrophy (GA) were evaluated separately. Combined effects of dietary vitamin D and calcium were assessed based on high or low consumption of each nutrient. Results There was a lower risk of progression to advanced AMD in the highest versus lowest quintile of dietary vitamin D intake after adjustment for demographic, behavioral, ocular, and nutritional factors (hazard ratio [HR]: 0.60; 95% confidence interval [CI]: 0.43-0.83; P trend = 0.0007). Similar results were observed for NV (HR: 0.59; 95% CI: 0.39-0.89; P trend = 0.005) but not GA (HR: 0.83; 95% CI: 0.53-1.30; P trend = 0.35). A protective effect was observed for advanced AMD among participants with high vitamin D and low calcium compared to the group with low levels for each nutrient (HR: 0.67; 95% CI: 0.50-0.88; P = 0.005). When supplement use was considered, the effect was in the protective direction but was not significant. Conclusions A diet rich in vitamin D may prevent or delay progression to advanced AMD, especially NV. Additional exploration is needed to elucidate the potential protective role of vitamin D and its contribution to reducing visual loss.

Calorie restriction (CR) is a promising strategy to attenuate age-related disease risk. Higher protein diets enhance satiety but may also impair metabolic health and accelerate aging. The effect of higher protein intake on adherence to CR and cardiometabolic markers of healthspan remains unknown. We used the Geometric Framework for Nutrition to examine the association between diet composition and 1) CR adherence; and 2) cardiometabolic risk factors during a 2-year intervention. The CR group consumed higher percentage energy from protein and lower fat at 12 months compared to baseline (logit % protein=0.1; 95% CI=0.05, 0.15; logit % fat= -0.12; CI=-0.29, -0.18). Higher protein intake over the 2-year intervention was associated with higher adherence to CR. No effect of diet composition on cardiometabolic risk factors was observed. These findings suggest that dietary protein plays a critical role in adherence to CR with no adverse effects on cardiometabolic markers of healthspan. Part of a symposium sponsored by the Nutrition Interest Group.

Gluten-free (GF) eating patterns are frequently perceived to be healthier than gluten-containing (GC) ones, but there has been very little research to evaluate this viewpoint. The effect of GF eating patterns on dietary composition was assessed using two independent approaches. One approach compared macronutrients and typical shortfall nutrients between MyPlate example menus developed with either GC or equivalent GF foods. In this analysis, the GF menus were significantly lower in protein, magnesium, potassium, vitamin E, folate, and sodium (p = 0.002&ndash, 0.03), with suggestive trends towards lower calcium and higher fat (p = 0.06&ndash, 0.08). The second approach was a meta-analysis of seven studies comparing information on the nutrient intakes of adults with celiac disease following a GF diet with control subjects eating a GC diet, and differences were evaluated using paired t-tests or Wilcoxon Signed rank tests. In this analysis, consuming a GF diet was associated with higher energy and fat intakes, and lower fiber and folate intakes compared to controls (p &lt, 0.001 to p = 0.03). After adjusting for heterogeneity and accounting for the large mean effect size (&minus, 0.88 &plusmn, 0.09), the lower fiber remained significant (p &lt, 0.001). These combined analyses indicate that GF diets are not nutritionally superior except for sodium, and in several respects are actually worse.

The era of personalized medicine and targeted therapies is emerging in many areas of medicine. In ophthalmology, the disease age-related macular degeneration (AMD) stands alone in having several environmental and genetic risk factors with confirmed, combined high impact on its development and progression. The combination of demographic, behavioral, and genetic factors can achieve a high predictive value for progression to advanced stages of disease approaching 90%, unlike any other disease.1–,5 We developed genetic risk algorithms in 2006,6 subsequently adding nongenetic markers7 and more common genetic variants,2–,4,8 then recently validated these risk models in an independent study cohort.5 More genetic loci in a 9 gene model, including the strongly related rare variant in the gene CFH (R1210C),9 and common loci RAD51B and COL8A1,10,11 enhanced predictability for transition from early and intermediate stages to advanced disease.12 Knowledge about the genetic underpinnings of AMD continues to expand. New, rare genetic variants have been discovered in case-control association studies in the genes C313–,15 and C9,13 as well as a burden of several rare variants in CFI.13 While these variants are uncommon, they have a stronger impact on the development of the disease individually compared with the low to moderate impact of the 20 or more common variants associated with AMD. Given that previous analyses were based on cross-sectional analyses of prevalent cases, and that progression of disease over time can have different underlying predictors, we conducted longitudinal analyses to determine the impact of these new rare variants on transition to the late stages of AMD resulting in visual loss over time. We also assessed the independent association of these new variants on progression, controlling for other genetic and nongenetic predictors. Building upon our previous algorithms, we developed and assessed a new risk prediction model for progression of AMD (rPPMD) and evaluated the degree to which AMD progression could be attributed to its various components: environmental, ocular, and genetic. In order to lay the groundwork to disseminate these predictive models for clinical use, we developed a software framework and analytics components in the form of an online browser-based calculator (seddonamdriskscore.org, available in the public domain) to demonstrate its use with various subject risk profiles. The rPPMD software components are also designed to be adapted as a stand-alone application for a personal computer or mobile devices such as smartphones and tablets.