BACKGROUND:The remote food photography method (RFPM) accurately estimates energy intake (EI) in adults, but its validity in adolescents has yet to be tested. Given the low accuracy of self-reported dietary intake in adolescents, evaluating the validity of the RFPM in both controlled and free-living settings is crucial. OBJECTIVES:To examine the validity of the RFPM in estimating adolescents' EI in controlled and free-living settings. METHODS:In Study 1, adolescents (n = 45) used the RFPM/SmartIntake application during 1) a laboratory test meal, 2) a cafeteria meal, and 3) 7 d of free-living intake, with EI estimations validated against weighed food intake, digital photography of foods method, and doubly labeled water, respectively. EI was measured under the same 3 settings with pen-and-paper food records. After refining the application and training, Study 2 (n = 22) repeated the protocol with a 3-d free-living period. For both studies, equivalence was evaluated using two one-sided t-tests (±10% bounds), and linear mixed models assessed RFPM error by participant characteristics. RESULTS:In Study 1, RFPM estimates of EI were equivalent to the criterion measure during cafeteria meals [mean standard deviation (SD) error: 4.1 (120.8) kcal; P = 0.005] but were not equivalent for the laboratory test meal [mean (SD) error: 61.1 (112.7) kcal; P = 0.675] and 7 d of free-living intake [mean (SD) error: -887.9 (554.8) kcal/d; P > 0.99]. In Study 2, RFPM EI estimates were equivalent to the criterion measure in the controlled setting [mean (SD) error: -2.2 (86.5) kcal; P = 0.007], but not in free-living settings [mean (SD) error: -782.8 (947.0) kcal/d; P = 0.99]. In both studies, a higher body mass index (in kg/m2) percentile was associated with greater underestimation of EI (both R2 > 0.19 and P < 0.05). CONCLUSIONS:The RFPM can accurately estimate the meal-level EI of adolescents in controlled settings; however, significant underestimation occurred in free-living settings in both studies. Previous studies demonstrated the inability to accurately assess adolescents' free-living intake, and the current results extend these findings to the RFPM. This trial was registered at clinicaltrials.gov as Study 1: NCT01719445 (https://clinicaltrials.gov/study/NCT01719445) and Study 2: NCT04148560 (https://clinicaltrials.gov/study/NCT04148560).

Exercise can reduce C-reactive protein (CRP), but heterogeneity exists and it is unclear whether exercise-induced weight compensation occurs or relates to CRP change. We examined whether compensation occurs in adults with elevated CRP during exercise and whether compensation and compensatory behaviours are associated with CRP change. In this 4-month randomised controlled trial, 124 sedentary adults with elevated CRP (mean ± SD: 4.3 ± 2.5 mg/L) were randomised to a control group or a supervised aerobic exercise group expending 16 kcal/kg/week. Predicted weight change was estimated, and weight compensation (weight change – predicted weight change) was calculated. CRP was measured, and energy intake and non-exercise step count were assessed using questionnaires and pedometers, respectively. Weight compensation was greater in the exercise group than the control group (1.3 [95

It is unclear how physical activity energy expenditure (PAEE) influences calorie restriction (CR)-induced benefits in individuals without obesity. We examined associations between PAEE and healthspan markers and physical activity (PA) time during prolonged CR. In Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE)™ 2, participants without obesity were randomized to 25

Background:Acute exercise alters appetite-regulating hormones like peptide tyrosine tyrosine (PYY), glucagon-like peptide-1 (GLP-1), and ghrelin, suppressing appetite and reducing food intake. The effect of exercise on hunger and satiety has been shown to vary by body composition, sex, and habitual physical activity, but the influence of aging is less understood. Objectives:We aimed to examine age-related differences in the effect of acute exercise on appetite-regulating hormones. Methods:Participants from 2 age cohorts (younger adults, 19-29 y, n = 39; older adults, 65-75 y, n = 29) completed 2 45-min study conditions on separate days in randomized order: 1) an exercise bout (60% V ˙ O2peak) on a bicycle ergometer (Exercise), and 2) a seated rest period (Rest). Plasma concentrations of PYY 3-36 (PYY3-36), GLP-1, and acylated ghrelin, as well as subjective perceptions of hunger, fullness, thirst, and nausea (via visual analog scales), were measured before a standardized snack (fasted) and before and after a subsequent exercise/rest condition. Results:Exercise induced a greater increase in PYY3-36 relative to Rest in younger adults compared to older adults (difference: 26.6 pg/mL; 95% confidence interval (CI): 3.4, 49.8 pg/mL; P = 0.025). GLP-1 concentrations were consistently greater in older adults independent of the study condition (Exercise/Rest; all P < 0.001), but the GLP-1 response to exercise did not differ by age group (P = 0.456). Similarly, exercise responses in acylated ghrelin (P = 0.114) and subjective appetite perceptions (all P ≥ 0.288) did not differ between younger adults and older adults. Conclusions:The present study showed age-related differences in the appetite-regulating hormone response to 45 min of nonfasted, moderate-intensity exercise in PYY3-36 but not GLP-1 or acylated ghrelin. The age-related variations did not translate into differences in subjective hunger or fullness.

Individuals with phenylketonuria (PKU) are at increased risk for obesity, possibly due to reduced satiety induced by a PKU diet that is low protein and high carbohydrate. It is unclear how exercise alters postprandial satiety after a PKU-like meal. The objective was to examine changes in postprandial satiety, satiety hormone concentrations, energy expenditure and substrate oxidation in response to acute treadmill exercise following a PKU-like meal. Sixteen males (mean age [±SD]: 26.5 ± 4.8 years; BMI: 23.7 ± 3.2 kg/m2) participated in a randomized cross-over trial with two conditions: exercise and control. Both trials involved consuming a PKU-like meal comprising naturally low-protein foods, a special low-protein food and a protein substitute. In the exercise trial, participants exercised at 60% of maximal oxygen uptake for 1 h before the meal; in the control trial, they rested. Satiety agents (peptide YY [PYY], glucagon-like peptide-1 [GLP-1] and growth differentiation factor-15 [GDF-15]), appetite, energy expenditure, fat oxidation and carbohydrate oxidation were measured. Mean (±SE) appetite and postprandial PYY and GLP-1 concentrations were unaffected by exercise (P ≥ 0.279). However, GDF-15 was higher in the exercise trial (control: 288 ± 25 pg/mL vs. exercise: 322 ± 24 pg/mL; P = 0.002). Exercise increased fat oxidation (P = 0.013) and decreased carbohydrate oxidation post-meal (P = 0.022), with concomitantly lower RER (P = 0.005). Energy expenditure rose during exercise (P < 0.001), but no difference occurred postprandially (P = 0.543). Acute exercise prior to a PKU-like meal does not affect postprandial GLP-1 and PYY concentrations compared to control but GDF-15 was increased and RER was reduced, potentially improving appetite regulation.

Background: School lunch is an important nutritious food source for children. The National School Lunch Program (NSLP) meal patterns guidelines have been established to promote healthier school lunches. This pilot study compared food selection during lunch in a school cafeteria with the NSLP meal pattern guidelines. Food intake and waste were also examined in relation to food selection.Methods: In a cross-sectional design, data were collected from children in the 1st, 6th, and 10th grades from a school in the United States. The digital photography of foods method was used to assess children's food selection, intake, and waste at lunch over 3 weeks. Results were presented as percentage, frequency, and mean +/- standard deviation.Results: About 48 children from 1st grade, 47 from 6th grade, and 50 from 10th grade participated each day. Food selection in these grades consistently fell below the NSLP guidelines, with 69%, 79.8%, and 86.9% of children selecting less than the guidelines for energy, respectively. On average, only 10.4% of children selected vegetables at or above the guidelines. About 41% of the selected energy, 43% of fruits, 43% of vegetables, and 56% of milk were discarded as plate waste across all grades.Conclusions: Selection of energy and vegetables was consistently below the NSLP guidelines, yet almost half of the selected fruits, vegetables, and milk were wasted by children. Initiatives to enhance meal quality and variety, along with nutrition education interventions and school policies, are needed to improve food selection and intake and reduce food waste.

CONTEXT:Exercise can decrease central adiposity, but the effect of exercise dose and the relationship between central adiposity and exercise-induced compensation is unclear. OBJECTIVE:Test the effect of exercise dose on central adiposity change and the association between central adiposity and exercise-induced weight compensation. METHODS:In this ancillary analysis of a 6-month randomized controlled trial, 170 participants with overweight or obesity (mean ± SD body mass index: 31.5 ± 4.7 kg/m2) were randomized to a control group or exercise groups that reflected exercise recommendations for health (8 kcal/kg/week [KKW]) or weight loss and weight maintenance (20 KKW). Waist circumference was measured, and dual-energy X-ray absorptiometry assessed central adiposity. Predicted weight change was estimated and weight compensation (weight change - predicted weight change) was calculated. RESULTS:Between-group change in waist circumference (control: .0 cm [95% CI, -1.0 to 1.0], 8 KKW: -.7 cm [95% CI, -1.7 to .4], 20 KKW: -1.3 cm [95% CI, -2.4 to -.2]) and visceral adipose tissue (VAT; control: -.02 kg [95% CI, -.07 to .04], 8 KKW: -.01 kg [95% CI, -.07 to .04], 20 KKW: -.04 kg [95% CI, -.10 to .02]) was similar (P ≥ .23). Most exercisers (82.6%) compensated (weight loss less than expected). Exercisers who compensated exhibited a 2.5-cm (95% CI, .8 to 4.2) and .23-kg (95% CI, .14 to .31) increase in waist circumference and VAT, respectively, vs those who did not (P < .01). Desire to eat predicted VAT change during exercise (β = .21; P = .03). CONCLUSION:In the presence of significant weight compensation, exercise at doses recommended for health and weight loss and weight maintenance leads to negligible changes in central adiposity.

Background: Predicting individual weight loss (WL) responses to lifestyle interventions is challenging but might help practitioners and clinicians select the most promising approach for each individual. Objective: The primary aim of this study was to develop machine learning (ML) models to predict individual WL responses using only variables known before starting the intervention. In addition, we used ML to identify pre-intervention variables influencing the individual WL response. Methods: We used 12-mo data from the comprehensive assessment of long-term effects of reducing intake of energy (CALERIETM) phase 2 study, which aimed to analyze the long-term effects of caloric restriction on human longevity. On the basis of the data from 130 subjects in the intervention group, we developed classification models to predict binary ("Success" and " No/low success") or multiclass ("High success," " Medium success," and " Low/no success") WL outcomes. Additionally, regression models were developed to predict individual weight change (percent). Models were evaluated on the basis of accuracy, sensitivity, specificity (classification models), and root mean squared error (RMSE; regression models). Results: Best classification models used 20-40 predictors and achieved 89%-97% accuracy, 91%-100% sensitivity, and 56%-86% specificity for binary classification. For multiclass classification, accuracy (69%) and sensitivity (50%) tended to be lower. The best regression performance was obtained with 36 variables with an RMSE of 2.84%. Among the 21 variables predicting individual weight change most consistently, we identified 2 novel predictors, namely orgasm satisfaction and sexual behavior/experience. Other common predictors have previously been associated with WL (16) or are already used in traditional prediction models (3). Conclusions: The prediction models could be implemented by practitioners and clinicians to support the decision of whether lifestyle interventions are sufficient or more aggressive interventions are needed for a given individual, thereby supporting better, faster, data-driven, and unbiased decisions. The CALERIETM phase 2 study was registered at clinicaltrials.gov as NCT00427193.

Considerable current interest is directed at pharmacological agents for producing significant weight loss. However, healthy lifestyle choices can also lead to clinically meaningful weight loss and improvements in cardiovascular disease (CVD) risk factors. In this review, we summarize the recent research from our PROmoting Successful Weight Loss in Primary CarE in Louisiana (PROPEL) randomized controlled trial and review previous data on the potential benefits of cardiac rehabilitation and exercise training (CRET) programs to produce weight loss and improvements in CVD risk factors. Although obesity medications are becoming extremely attractive for secondary and even primary CVD prevention, high-intensity non-pharmacological therapies with healthy lifestyle choices reviewed herein can also lead to substantial health improvements in patients with obesity, including improvements in body weight and other body composition parameters as well as overall CVD risk.

PURPOSE:American College of Sports Medicine (ACSM) metabolic equations are used to estimate energy expenditure (EE) of physical activity and prescribe aerobic exercise to meet EE requirements. Limited evidence supports their accuracy in sedentary adults with overweight or obesity during controlled exercise interventions. The purpose of this study was to compare EE estimated by the ACSM walking equation versus EE measured by indirect calorimetry during a 24-wk aerobic exercise intervention, and identify potential modulators for their accuracy. METHODS:Data from the exercising groups (8 or 20 kcal·kg body weight -1 ·wk -1 ) of the E-MECHANIC study were utilized in this ancillary analysis ( N = 103). Every 2 wk for the initial 8 wk and monthly thereafter, EE was measured via indirect calorimetry during absolute (2 mph, 0% grade) and relative (65%-85% peak oxygen uptake (V̇O 2peak )) workload exercise. Resting metabolic rate, V̇O 2peak , and body composition were assessed at baseline and follow-up. An EE offset factor (EOF) was calculated to express measured EE as a percentage of the estimated EE at each workload (EOF < 100% represents an overestimation of ACSM estimated EE). RESULTS:The accuracy of the equation decreased with increasing exercise workload (0.44%, 9.2%, and 20.3% overestimation at absolute, relative, and maximal workloads, respectively, at baseline) and overestimation of EE was greater after the exercise intervention. Furthermore, race, sex, age, fat mass, and V̇O 2peak were identified as modulators for equation accuracy. Greater overestimation of EE was observed in Black compared with White females, particularly at lower exercise workloads. CONCLUSIONS:These findings support future efforts to improve the accuracy of metabolic equations, especially in diverse populations. Researchers should account for exercise efficiency adaptations when using metabolic equations to prescribe exercise precisely.

ObjectiveThis study tested whether initial weight change (WC), self-weighing, and adherence to the expected WC trajectory predict longer-term WC in an underserved primary-care population with obesity. MethodsData from the intervention group (n = 452; 88% women; 74% Black; BMI 37.3 kg/m(2) [SD: 4.6]) of the Promoting Successful Weight Loss in Primary Care in Louisiana trial were analyzed. Initial (2-, 4-, and 8-week) percentage WC was calculated from baseline clinic weights and daily at-home weights. Weights were considered adherent if they were on the expected WC trajectory (10% at 6 months with lower [7.5%] and upper [12.5%] bounds). Linear mixed-effects models tested whether initial WC and the number of daily and adherent weights predicted WC at 6, 12, and 24 months. ResultsPercentage WC during the initial 2, 4, and 8 weeks predicted percentage WC at 6 (R-2 = 0.15, R-2 = 0.28, and R-2 = 0.50), 12 (R-2 = 0.11, R-2 = 0.19, and R-2 = 0.32), and 24 (R-2 = 0.09, R-2 = 0.11, and R-2 = 0.16) months (all p < 0.01). Initial daily and adherent weights were significantly associated with WC as individual predictors, but they only marginally improved predictions beyond initial weight loss alone in multivariable models. ConclusionsThese results highlight the importance of initial WC for predicting long-term WC and show that self-weighing and adherence to the expected WC trajectory can improve WC prediction.

A systematic review and meta-analysis was performed to determine the effect of exercise training on fasting gastrointestinal appetite hormones in adults living with overweight and obesity. For eligibility, only randomised controlled trials (duration > four weeks) examining the effect of exercise training interventions were considered. This review was registered in the International Prospective Register of Systematic Reviews (CRD42020218976). The searches were performed on five databases: MEDLINE, EMBASE, Cochrane Library, Web of Science, and Scopus. The initial search identified 13204 records. Nine studies, which include sixteen exercise interventions, met the criteria for inclusion. Meta-analysis was calculated as the standardised mean difference (Cohen's d). Exercise training had no effect on fasting concentrations of total ghrelin (d: 1.06, 95% CI-0.38 to 2.50, P = 0.15), acylated ghrelin (d: 0.08, 95% CI:-0.31 to 0.47, P = 0.68) and peptide YY (PYY) (d =-0.16, 95% CI:-0.62 to 0.31, P = 0.51) compared to the control group. Analysis of body mass index (BMI) (d:-0.31, 95% CI:-0.50 to-0.12, P < 0.01) and body mass (d:-0.22, 95% CI:-0.42 to-0.03, P = 0.03) found a significant reduction after exercise compared to controls. Overall, exercise interventions did not modify fasting concentrations of total ghrelin, acylated ghrelin, and PYY in individuals with overweight or obesity, although they reduced body mass and BMI. Thus, any upregulation of appetite and energy intake in individuals with overweight and obesity participating in exercise programmes is unlikely to be related to fasting concentrations of gastrointestinal appetite hormones.

Weight loss (WL) differences between isocaloric high-carbohydrate and high-fat diets are generally small; however, individual WL varies within diet groups. Genotype patterns may modify diet effects, with carbohydrate-responsive genotypes losing more weight on high-carbohydrate diets (and vice versa for fat-responsive genotypes). We investigated whether 12-week WL (kg, primary outcome) differs between genotype-concordant and genotype-discordant diets. In this 12-week single-center WL trial, 145 participants with overweight/obesity were identified a priori as fat-responders or carbohydrate-responders based on their combined genotypes at ten genetic variants and randomized to a high-fat (n = 73) or high-carbohydrate diet (n = 72), yielding 4 groups: (1) fat-responders receiving high-fat diet, (2) fat-responders receiving high-carbohydrate diet, (3) carbohydrate-responders receiving high-fat diet, (4) carbohydrate-responders receiving high-carbohydrate diet. Dietitians delivered the WL intervention via 12 weekly diet-specific small group sessions. Outcome assessors were blind to diet assignment and genotype patterns. We included 122 participants (54.4 [SD:13.2] years, BMI 34.9 [SD:5.1] kg/m 2 , 84% women) in the analyses. Twelve-week WL did not differ between the genotype-concordant (−5.3 kg [SD:1.0]) and genotype-discordant diets (−4.8 kg [SD:1.1]; adjusted difference: −0.6 kg [95% CI: −2.1,0.9], p = 0.50). With the current ability to genotype participants as fat- or carbohydrate-responders, evidence does not support greater WL on genotype-concordant diets. ClinicalTrials identifier: NCT04145466.

Background Intensive lifestyle interventions (ILIs) stimulate weight loss in underserved patients with obesity, but the mediators of weight change are unknown. Objectives We aimed to identify the mediators of weight change during an ILI compared with usual care (UC) in underserved patients with obesity. Methods The PROPEL (Promoting Successful Weight Loss in Primary Care in Louisiana) trial randomly assigned 18 clinics (n = 803) to either an ILI or UC for 24 mo. The ILI group received an intensive lifestyle program; the UC group had routine care. Body weight was measured; further, eating behaviors (restraint, disinhibition), dietary intake (percentage fat intake, fruit and vegetable intake), physical activity, and weight- and health-related quality of life constructs were measured through questionnaires. Mediation analyses assessed whether questionnaire variables explained between-group variations in weight change during 2 periods: baseline to month 12 (n = 779) and month 12 to month 24 (n = 767). Results The ILI induced greater weight loss at month 12 compared with UC (between-group difference: -7.19 kg; 95% CI: -8.43, -6.07 kg). Improvements in disinhibition (-0.33 kg; 95% CI: -0.55, -0.10 kg), percentage fat intake (-0.25 kg; 95% CI: -0.50, -0.01 kg), physical activity (-0.26 kg; 95% CI: -0.41, -0.09 kg), and subjective fatigue (-0.28 kg; 95% CI: -0.46, -0.10 kg) at month 6 during the ILI partially explained this between-group difference. Greater weight loss occurred in the ILI at month 24, yet the ILI group gained 2.24 kg (95% CI: 1.32, 3.26 kg) compared with UC from month 12 to month 24. Change in fruit and vegetable intake (0.13 kg; 95% CI: 0.05, 0.21 kg) partially explained this response, and no variables attenuated the weight regain of the ILI group. Conclusions In an underserved sample, weight change induced by an ILI compared with UC was mediated by several psychological and behavioral variables. These findings could help refine weight management regimens in underserved patients with obesity. This trial was registered at clinicaltrials.gov as NCT02561221.

BACKGROUND Intensive lifestyle interventions (ILIs) stimulate weight loss in underserved patients with obesity, but the mediators of weight change are unknown. OBJECTIVES Identify the mediators of weight change during an ILI versus usual care (UC) in underserved patients with obesity. DESIGN The Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL) trial randomized 18 clinics (n = 803) to either an ILI or UC for 24 months. The ILI group received an intensive lifestyle program; the UC group had routine care. Body weight was measured; further, eating behaviors (restraint, disinhibition), dietary intake (% fat intake, fruit and vegetable intake), physical activity, and weight- and health-related quality of life constructs were measured through questionnaires. Mediation analyses assessed whether questionnaire variables explained between-group variations in weight change during two periods: baseline to month 12 (n = 779) and month 12 to month 24 (n = 767). RESULTS The ILI induced greater weight loss at month 12 versus UC (between-group difference: -7.19 kg; 95% CI: -8.43, -6.07). Improvements in disinhibition (-0.33 kg; 95% CI: -0.55, -0.10), % fat intake (-0.25 kg; 95% CI: -0.50, -0.01), physical activity (-0.26 kg; 95% CI: -0.41 to -0.09), and subjective fatigue (-0.28 kg; 95% CI: -0.46, -0.10) at month 6 during the ILI partially explained this between-group difference. Greater weight loss occurred in the ILI at month 24, yet the ILI group gained 2.24 kg (95% CI: 1.32, 3.26) versus UC from month 12 to month 24. Change in fruit and vegetable intake (0.13 kg; 95% CI: 0.05, 0.21) partially explained this response, and no variables attenuated the weight regain of the ILI group. CONCLUSIONS In an underserved sample, weight change induced by an ILI compared to UC was mediated by several psychological and behavioral variables. These findings could help refine weight regimens in underserved patients with obesity. Clinical trial registry: ClinicalTrials.gov number, NCT02561221.

The ACSM metabolic equations have been used to estimate energy expenditure (EE) during exercise; yet limited evidence supports their accuracy in overweight/obese and sedentary individuals, especially during aerobic exercise training. PURPOSE: Evaluate the relationship between measured and estimated EE over a 24-wk exercise intervention and determine moderating demographic and anthropometric relationships. METHODS: Data from the EMECHANIC study were analyzed (N = 108; 77 female; 71 Caucasian). Participants (sedentary) were randomized to aerobic exercise (8 or 20 kcal/kg body mass/week). EE was measured via indirect calorimetry at absolute (2 mph, 0% grade) and relative (65-85% VO2max) intensities pre-intervention, every 2 weeks for the initial 12 weeks, and monthly thereafter. An EE offset factor (EOF) was computed at each intensity by expressing measured EE as a percentage of the ACSM estimated EE. An EOF lower than 100% indicates an overestimation of EE when using the equations. RESULTS: Absolute and relative EOF decreased during the intervention (time effect, P < 0.01). Exercise dose had no effect on EOF (P > 0.50). A sex effect was found for absolute EOF (P < 0.01), with women exhibiting lower values throughout the study compared to men (94.4 ± 1.9% vs 99.9 ± 3.0%; mean ± 95%CI), but not for relative EOF (P = 0.11). No sex by time interactions were observed for absolute or relative EOF (P > 0.35). African Americans (AA) demonstrated lower absolute (90.2 ± 2.9% vs 98.3 ± 1.9%, P < 0.01), but not relative EOF (87.3 ± 2.0% vs 88.3 ± 1.3%, P = 0.39), compared to Caucasians. No race by time interactions were observed for absolute or relative EOF (P > 0.35). At absolute intensity, sex and race differences in EOF are mirrored in VO2 (P < 0.03), but not respiratory exchange ratio (RER; P > 0.15). Although relative EOF was not different, RER was higher in AA and VO2 was lower in women (P < 0.03). Higher body weight resulted in a lower absolute and relative EOF at all time points (P < 0.01). Age had no effect on absolute or relative EOF (P = 0.91; P = 0.07, resp). CONCLUSION: Calculated EE may be overestimated in women and AA, especially at lower exercise intensities. Studies that use ACSM estimated EE in these populations (ex. Calorie balance studies) may overestimate prescribed EE resulting in lower than anticipated daily EE.

Background: The Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE (TM)) phase 2 trial tested the effects of two years of 25% calorie restriction (CR) on aging in humans. CALERIE 2 was one of the first studies to use a graph of predicted weight loss to: 1) provide a proxy of dietary adherence, and 2) promote dietary adherence. Assuming 25% CR, each participant's weight over time was predicted, with upper and lower bounds around predicted weights. Thus, the resulting weight graph included a zone or range of body weights that reflected adherence to 25% CR, and this was named the zone of adherence. Participants were considered adherent if their weight was in this zone. It is unlikely, however, that the entire zone reflects 25% CR. Objectives: To determine the level of CR associated with the zone of adherence and if the level of CR achieved by participants was within the zone. Methods: Percent CR associated with the upper and lower bounds of the zone were determined via the Body Weight Planner (https://www.niddk.nih.gov/bwp) for participants in the CALERIE 2 CR group (N = 143). Percent CR achieved by participants was estimated with the intake-balance method. Results: At month 24, the zone of adherence ranged from 10.4(0.0)% to 19.4(0.0)% CR [Mean(SEM)], and participants achieved 11.9(0.7)% CR and were in the zone. Conclusion: The results highlight the challenges of: 1) setting a single CR goal vs. a range of acceptable values, and 2) obtaining real-time and valid measures of CR adherence to facilitate adherence.

We developed the PortionSizeTM app (PS) to estimate food intake and dietary adherence based on images of meals captured before and after eating occurs. The PS app provides real-time feedback to users about their dietary intake (energy, nutrients, and food groups) and adherence to specific diets. This pilot study provided initial tests of the validity of the PS app at assessing energy and nutrient intake from simulated meals in a laboratory setting. We also explored participants' satisfaction with the PS app. Fifteen adult participants (aged 18–65 years) were trained to use the app. Participants then used the app to estimate food intake during simulated meals, where participants were provided with a plate of food to represent food provision as well as a plate of leftovers. The amount of food provided as food provision and waste was covertly weighed. Participants completed a six-point user satisfaction survey ranked from 1 ('extremely dissatisfied') to six 'very much satisfied'. Dependent t-tests were performed to compare intake of energy, macronutrients, and food groups (fruits, vegetables, grain, dairy, and protein) from the 15 meals, where intake was estimated with the PS app and compared to directly weighed food. Alpha was set at 0.05. Of the 15 participants, 73.3% (11) were female, and the mean (± SD) age and body mass index of the participants was 28.0 ± 12.2 years and 24.1 ± 6.6 kg/m2, respectively. Energy intake estimated by PS at the meal level (742.9 ± 328.2 kcal) was similar to directly weighed values (659.3 ± 190.7 kcal) and the difference (83.5 ± 287.5 kcal) was not significant (P > .05). No significant differences were found between the two methods (PS and weigh back) for macronutrients (protein, total fat, and carbohydrate), and four food group servings (all P values > .05) except total grains (P value < .05). About 71% of the participants rated the app as a five or six in terms of satisfaction and ease of using the PS app. Results from this pilot provide preliminary support for the validity of, and user satisfaction with, the PS app. The pilot study identified ways to improve PS. Larger validation studies and further app refinement are ongoing. National Institute of Diabetes and Digestive and Kidney Diseases; Nutrition Obesity Research Center: PBRC; Louisiana Clinical and Translational Science Center.