Motor unit firing rates increase in prepubescent youth following linear periodization resistance exercise training.

PURPOSE: The purpose was to examine the effects of 8-weeks (3 days/week) of linear periodization resistance exercise training (RET) on neuromuscular function in prepubescent youth. METHODS: Twenty-five healthy prepubescent youth (11 males, 14 females, age = 9.1 ± 0.8 years) completed the RET (n = 17) or served as controls (CON, n = 8). Isometric maximal voluntary contractions (MVCs) and trapezoidal submaximal contractions at 35 and 60% MVC of the right leg extensors were performed with surface electromyography (EMG) recorded from the leg extensors [vastus lateralis (VL), rectus femoris, and vastus medialis] and flexors (biceps femoris and semitendinosus). EMG amplitude of the leg extensors and flexors were calculated during the MVCs. Motor unit (MU) action potential trains were decomposed from the surface EMG of the VL for the 35 and 60% MVCs. MU firing rates and action potential amplitudes were regressed against recruitment threshold with the y-intercepts and slopes calculated for each contraction. Total leg extensor muscle cross-sectional area (CSA) was collected using ultrasound images. ANOVA models were used to examine potential differences. RESULTS: Isometric strength increased post-RET (P = 0.006) with no changes in leg extensor and flexor EMG amplitude. Furthermore, there were no changes in total CSA or the MU action potential amplitude vs. recruitment threshold relationships. However, there were increases in the firing rates of the higher-threshold MUs post-RET as indicated with greater y-intercepts (P = 0.003) from the 60% MVC and less negative slope (P = 0.004) of the firing rates vs. recruitment threshold relationships at 35% MVC. CONCLUSIONS: MU adaptations contribute to strength increases following RET in prepubescent youth.

Modifying diet and exercise in multiple sclerosis (MoDEMS): A randomized controlled trial for behavioral weight loss in adults with multiple sclerosis and obesity.

BACKGROUND: Obesity is a risk factor for developing multiple sclerosis (MS) and MS-related disability. The efficacy of behavioral weight loss interventions among people with MS (pwMS) remains largely unknown. OBJECTIVE: Examine whether a group-based telehealth weight loss intervention produces clinically significant weight loss in pwMS and obesity. METHODS: Seventy-one pwMS were randomized to the weight loss intervention or treatment-as-usual (TAU). The 6-month program promoted established guidelines for calorie reduction and increased physical activity. Anthropometric measurements, mobility tasks, self-report questionnaires, and accelerometry were used to assess changes at follow-up. RESULTS: Mean percent weight loss in the treatment group was 8.6% compared to 0.7% in the TAU group (p < .001). Sixty-five percent of participants in the intervention achieved clinically meaningful weight loss (⩾ 5%). Participants in the treatment group engaged in 46.2 minutes/week more moderate-to-vigorous physical activity than TAU participants (p = .017) and showed improvements in quality of life (p = .012). Weight loss was associated with improved mobility (p = .003) and reduced fatiguability (p = .008). CONCLUSION: Findings demonstrate the efficacy of a behavioral intervention for pwMS and obesity, with clinically significant weight loss for two-thirds of participants in the treatment condition. Weight loss may also lead to improved mobility and quality of life.

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique.

BACKGROUND: Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device. METHODS: Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis. RESULTS: Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis. CONCLUSIONS: Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

Predicting energy intake with an accelerometer-based intake-balance method.

Nutritional interventions often rely on subjective assessments of energy intake (EI), but these are susceptible to measurement error. To introduce an accelerometer-based intake-balance method for assessing EI using data from a time-restricted eating (TRE) trial. Nineteen participants with overweight/obesity (25-63 years old; 16 females) completed a 12-week intervention (NCT03129581) in a control group (unrestricted feeding; n 8) or TRE group (n 11). At the start and end of the intervention, body composition was assessed by dual-energy X-ray absorptiometry (DXA) and daily energy expenditure (EE) was assessed for 2 weeks via wrist-worn accelerometer. EI was back-calculated as the sum of net energy storage (from DXA) and EE (from accelerometer). Accelerometer-derived EI estimates were compared against estimates from the body weight planner of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Mean EI for the control group declined by 138 and 435 kJ/day for the accelerometer and NIDDK methods, respectively (both P ≥ 0·38), v. 1255 and 1469 kJ/day, respectively, for the TRE group (both P < 0·01). At follow-up, the accelerometer and NIDDK methods showed excellent group-level agreement (mean bias of -297 kJ/day across arms; standard error of estimate 1054 kJ/day) but high variability at the individual level (limits of agreement from -2414 to +1824 kJ/day). The accelerometer-based intake-balance method showed plausible sensitivity to change, and EI estimates were biologically and behaviourally plausible. The method may be a viable alternative to self-report EI measures. Future studies should assess criterion validity using doubly labelled water.

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults.

BACKGROUND: The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. OBJECTIVES: The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. METHODS: Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. RESULTS: There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. CONCLUSIONS: Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

Location-specific psychosocial and environmental correlates of physical activity and sedentary time in young adolescents: preliminary evidence for location-specific approaches from a cross-sectional observational study.

BACKGROUND: A better understanding of the extent to which psychosocial and environmental correlates of physical activity are specific to locations would inform intervention optimization. PURPOSE: To investigate cross-sectional associations of location-general and location-specific variables with physical activity and sedentary time in three common locations adolescents spend time. METHODS: Adolescents (N = 472,Mage = 14.1,SD = 1.5) wore an accelerometer and global positioning systems (GPS) tracker and self-reported on psychosocial (e.g., self-efficacy) and environmental (e.g., equipment) factors relevant to physical activity and sedentary time. We categorized each survey item based on whether it was specific to a location to generate psychosocial and environmental indices that were location-general or specific to either school, non-school, or home location. Physical activity (MVPA) and sedentary time were based on time/location match to home, school, or all "other" locations. Mixed-effects models investigated the relation of each index with location-specific activity. RESULTS: The location-general and non-school physical activity psychosocial indices were related to greater MVPA at school and "other" locations. The school physical activity environment index was related to greater MVPA and less sedentary time at school. The home activity environment index was related to greater MVPA at home. The non-school sedentary psychosocial index was related to less sedentary time at home. Interactions among indices revealed adolescents with low support on one index benefited (i.e., exhibited more optimal behavior) from high support on another index (e.g., higher scores on the location-general PA psychosocial index moderated lower scores on the home PA environment index). Concurrent high support on two indices did not provide additional benefit. CONCLUSIONS: No psychosocial or environment indices, including location-general indices, were related to activity in all locations. Most of the location-specific indices were associated with activity in the matching location(s). These findings provide preliminary evidence that psychosocial and environmental correlates of activity are location specific. Future studies should further develop location-specific measures and evaluate these constructs and whether interventions may be optimized by targeting location-specific psychosocial and environmental variables across multiple locations.

Differences in adolescent activity and dietary behaviors across home, school, and other locations warrant location-specific intervention approaches.

BACKGROUND: Investigation of physical activity and dietary behaviors across locations can inform "setting-specific" health behavior interventions and improve understanding of contextual vulnerabilities to poor health. This study examined how physical activity, sedentary time, and dietary behaviors differed across home, school, and other locations in young adolescents. METHODS: Participants were adolescents aged 12-16 years from the Baltimore-Washington, DC and the Seattle areas from a larger cross-sectional study. Participants (n = 472) wore an accelerometer and Global Positioning Systems (GPS) tracker (Mean days = 5.12, SD = 1.62) to collect location-based physical activity and sedentary data. Participants (n = 789) completed 24-h dietary recalls to assess dietary behaviors and eating locations. Spatial analyses were performed to classify daily physical activity, sedentary time patterns, and dietary behaviors by location, categorized as home, school, and "other" locations. RESULTS: Adolescents were least physically active at home (2.5 min/hour of wear time) and school (2.9 min/hour of wear time) compared to "other" locations (5.9 min/hour of wear time). Participants spent a slightly greater proportion of wear time in sedentary time when at school (41 min/hour of wear time) than at home (39 min/hour of wear time), and time in bouts lasting ≥30 min (10 min/hour of wear time) and mean sedentary bout duration (5 min) were highest at school. About 61% of daily energy intake occurred at home, 25% at school, and 14% at "other" locations. Proportionately to energy intake, daily added sugar intake (5 g/100 kcal), fruits and vegetables (0.16 servings/100 kcal), high calorie beverages (0.09 beverages/100 kcal), whole grains (0.04 servings/100 kcal), grams of fiber (0.65 g/100 kcal), and calories of fat (33 kcal/100 kcal) and saturated fat (12 kcal/100 kcal) consumed were nutritionally least favorable at "other" locations. Daily sweet and savory snacks consumed was highest at school (0.14 snacks/100 kcal). CONCLUSIONS: Adolescents' health behaviors differed based on the location/environment they were in. Although dietary behaviors were generally more favorable in the home and school locations, physical activity was generally low and sedentary time was higher in these locations. Health behavior interventions that address the multiple locations in which adolescents spend time and use location-specific behavior change strategies should be explored to optimize health behaviors in each location.

Beyond clinical food prescriptions and mobile markets: parent views on the role of a healthcare institution in increasing healthy eating in food insecure families.

BACKGROUND: Children in food-insecure families face increased barriers to meeting recommendations for fruit and vegetable consumption. Hospitals and pediatric healthcare institutions have attempted to alleviate food-insecurity through various internal programs like food prescriptions, yet little evidence for these programs exist. Consistent with a patient-centered perspective, we sought to develop a comprehensive understanding of barriers to fruit and vegetable consumption and a parent-driven agenda for healthcare system action. METHODS: We conducted six qualitative focus group discussions (four in English, two in Spanish) with 29 parents and caregivers of patients who had screened positive for food-insecurity during visits to a large pediatric healthcare system in a midwestern U.S. city. Our iterative analysis process consisted of audio-recording, transcribing and coding discussions, aiming to produce a) a conceptual framework of barriers to fruit and vegetable consumption and b) a synthesis of participant programmatic suggestions for their healthcare system. RESULTS: Participants were 90% female, 38% Black/African American and 41% Hispanic/Latino. Barriers to fruit and vegetable consumption in their families fell into three intersecting themes: affordability, accessibility and desirability. Participant-generated intervention recommendations were multilevel, suggesting healthcare systems focus not only on clinic and community-based action, but also advocacy for broader policies that alleviate barriers to acquiring healthy foods. CONCLUSION: Parents envision an expanded role for healthcare systems in ensuring their children benefit from a healthy diet. Findings offer critical insight on why clinic-driven programs aimed to address healthy eating may have failed and healthcare organizations may more effectively intervene by adopting a multilevel strategy.

Adult energy requirements predicted from doubly labeled water.

BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

Energy Intake Derived from an Energy Balance Equation, Validated Activity Monitors, and Dual X-Ray Absorptiometry Can Provide Acceptable Caloric Intake Data among Young Adults.

Background: Assessments of energy intake (EI) are frequently affected by measurement error. Recently, a simple equation was developed and validated to estimate EI on the basis of the energy balance equation [EI = changed body energy stores + energy expenditure (EE)]. Objective: The purpose of this study was to compare multiple estimates of EI, including 2 calculated from the energy balance equation by using doubly labeled water (DLW) or activity monitors, in free-living adults. Methods: The body composition of participants (n = 195; mean age: 27.9 y; 46% women) was measured at the beginning and end of a 2-wk assessment period with the use of dual-energy X-ray absorptiometry. Resting metabolic rate (RMR) was calculated through indirect calorimetry. EE was assessed with the use of the DLW technique and an arm-based activity monitor [Sensewear Mini Armband (SWA); BodyMedia, Inc.]. Self-reported EI was calculated by using dietitian-administered 24-h dietary recalls. Two estimates of EI were calculated with the use of a validated equation: quantity of energy stores estimated from the changes in fat mass and fat-free mass occurring over the assessment period plus EE from either DLW or the SWA. To compare estimates of EI, reporting bias (estimated EI/EE from DLW × 100) and Goldberg ratios (estimated EI/RMR) were calculated. Results: Mean ± SD EEs from DLW and SWA were 2731 ± 494 and 2729 ± 559 kcal/d, respectively. Self-reported EI was 2113 ± 638 kcal/d, EI derived from DLW was 2723 ± 469 kcal/d, and EI derived from the SWA was 2720 ± 730 kcal/d. Reporting biases for self-reported EI, DLW-derived EI, and SWA-derived EI are as follows: -21.5% ± 22.2%, -0.7% ± 18.5%, and 0.2% ± 20.8%, respectively. Goldberg cutoffs for self-reported EI, DLW EI, and SWA EI are as follows: 1.39 ± 0.39, 1.77 ± 0.38, and 1.77 ± 0.38 kcal/d, respectively. Conclusions: These results indicate that estimates of EI based on the energy balance equation can provide reasonable estimates of group mean EI in young adults. The findings suggest that, when EE derived from DLW is not feasible, an activity monitor that provides a valid estimate of EE can be substituted for EE from DLW.

Evaluation of the Healthy Lifestyles Initiative for Improving Community Capacity for Childhood Obesity Prevention.

PURPOSE AND OBJECTIVES: Policy, systems, and environmental approaches are recommended for preventing childhood obesity. The objective of our study was to evaluate the Healthy Lifestyles Initiative, which aimed to strengthen community capacity for policy, systems, and environmental approaches to healthy eating and active living among children and families. INTERVENTION APPROACH: The Healthy Lifestyles Initiative was developed through a collaborative process and facilitated by community organizers at a local children's hospital. The initiative supported 218 partners from 170 community organizations through training, action planning, coalition support, one-on-one support, and the dissemination of materials and sharing of resources. EVALUATION METHODS: Eighty initiative partners completed a brief online survey on implementation strategies engaged in, materials used, and policy, systems, and environmental activities implemented. In accordance with frameworks for implementation science, we assessed associations among the constructs by using linear regression to identify whether and which of the implementation strategies were associated with materials used and implementation of policy, systems, and environmental activities targeted by the initiative. RESULTS: Each implementation strategy was engaged in by 30% to 35% of the 80 survey respondents. The most frequently used materials were educational handouts (76.3%) and posters (66.3%). The most frequently implemented activities were developing or continuing partnerships (57.5%) and reviewing organizational wellness policies (46.3%). Completing an action plan and the number of implementation strategies engaged in were positively associated with implementation of targeted activities (action plan, effect size = 0.82; number of strategies, effect size = 0.51) and materials use (action plan, effect size = 0.59; number of strategies, effect size = 0.52). Materials use was positively associated with implementation of targeted activities (effect size = 0.35). IMPLICATIONS FOR PUBLIC HEALTH: Community-capacity-building efforts can be effective in supporting community organizations to engage in policy, systems, and environmental activities for healthy eating and active living. Multiple implementation strategies are likely needed, particularly strategies that involve a high level of engagement, such as training community organizations and working with them on structured action plans.

Cross-sectional and longitudinal associations between different exercise types and food cravings in free-living healthy young adults.

INTRODUCTION: An increase in energy intake due to alterations in hedonic appetite sensations may, at least in part, contribute to lower-than-expected weight loss in exercise interventions. The aim of this study was to examine cross-sectional and longitudinal associations between habitual exercise participation and food cravings in free-living young adults. METHODS: A total of 417 adults (49% male, 28 ± 4 years) reported frequency and duration of walking, aerobic exercise, resistance exercise and other exercise at baseline and every 3 months over a 12-month period. Food cravings were assessed via the Control of Eating Questionnaire at baseline and 12-month follow-up. RESULTS: Cross-sectional analyses revealed more frequent cravings for chocolate and a greater difficulty to resist food cravings in women compared to men (p < 0.01). Only with resistance exercise significant sex by exercise interaction effects were observed with favorable responses in men but not in women. Significant main effects were shown for walking and aerobic exercise with exercisers reporting more frequent food cravings for chocolate and fruits and greater difficulty to resist eating compared to non-exercisers (p < 0.05). Longitudinal analyses revealed significant interaction effects for other exercise (p < 0.05) with favorable results in men but not women. Furthermore, significant main effects were observed for aerobic exercise, resistance exercise and total exercise with an increase in exercise being associated with a reduced difficulty to resist food cravings (p < 0.05). DISCUSSION: The association between exercise participation and hedonic appetite sensations varies by exercise type and sex. Even though exercise was associated with more frequent and greater difficulty to food cravings in the cross-sectional analyses, which may be attributed to greater energy demands, longitudinal results indicate beneficial effects of increased exercise on appetite control, particularly in men.

Is nutrient intake associated with physical activity levels in healthy young adults?

OBJECTIVE: Both physical activity (PA) and diet are important contributors to health and well-being; however, there is limited information on the association of these behaviours and whether observed associations differ by weight. The present study aimed to evaluate whether nutrient intake is associated with PA and if this association varies by weight in young adults. DESIGN: Cross-sectional study to analyse the association between PA and nutrient intake. SETTING: Participants were stratified as normal weight (18·5 kg/m2

Effects of moderate and vigorous physical activity on fitness and body composition.

Current physical activity (PA) guidelines indicate that moderate-intensity (MPA) and vigorous intensity (VPA) PA provide similar benefits when total volume is equal. The present study examined the associations of MPA and VPA with body composition and cardiorespiratory fitness in free-living young adults. A total of 197 young adults (52.8 % male) were followed over a period of 15 months. Body composition was assessed via dual X-ray absorptiometry and time spent in various PA intensities was determined with a multi-sensor device every 3 months. Cardiorespiratory fitness was assessed with a graded exercise test at baseline and 15-months follow-up. Change in VPA was positively associated with cardiorespiratory fitness while MPA had beneficial associations with percent body fat. In overweight/obese participants the association with VO2peak was similar for MVPA bouts and VPA. Even though MPA and VPA have positive associations with overall health, their associations on key health parameters differ.

Resting Energy Expenditure Equations Have Lower Accuracy for Adolescents with Overweight/Obesity Versus Healthy-Weight Adolescents.

Objective: The objectives of the study were (1) to assess whether resting energy expenditure (REE) equations have comparable validity for adolescents with overweight/obesity vs. adolescents with healthy weight and (2) to examine determinants of measured REE in adolescents with overweight/obesity vs. adolescents with healthy weight. Methods: Ten equations were used to predict REE for 109 adolescents (70% males; 36.7% with overweight/obesity); 95% equivalence testing was used to assess how well each equation agreed with the criterion measure of indirect calorimetry. Linear regression models were fitted to examine how much REE variance was accounted for by age, sex, race, fat-free mass (FFM), and fat mass. Results: For adolescents with healthy weight, all ten equations were significantly equivalent to the criterion measure within ±8.4% (p < 0.05), whereas for participants with overweight/obesity, only three equations were equivalent within the same range (p < 0.05). Controlling for age, sex, race, fat mass, and FFM accounted for 74% of REE variance. FFM explained the greatest amount (26%) of variance in REE, while weight status itself explained an additional 22%. Conclusions: Prediction equations tend to be more accurate for adolescents with healthy weight than adolescents with overweight/obesity unless the original sample specifically included participants with overweight/obesity. Determinants of REE are similar regardless of weight status.

Relationship between youth cardiometabolic health and physical activity in medical records.

BACKGROUND: Thers is limited research examining modifiable cardiometabolic risk factors with a single-item health behavior question obtained during a clinic visit. Such information could support clinicians in identifying patients at risk for adverse cardiometabolic health. We investigated if children meeting physical activity or screen time recommendations, collected during clinic visits, have better cardiometabolic health than children not meeting recommendations. We hypothesized that children meeting either recommendation would have fewer cardiometabolic risk factors. METHODS AND FINDINGS: This cross-sectional study used data from electronic medical records (EMRs) between January 1, 2013 through December 30, 2017 from children (2-18 years) with a well child visits and data for ≥1 cardiometabolic risk factor (i.e., systolic and diastolic blood pressure, glycated hemoglobin, alanine transaminase, high-density and low-density lipoprotein, total cholesterol, and/or triglycerides). Physical activity and screen time were patient/caregiver-reported. Analyses included EMRs from 63,676 well child visits by 30,698 unique patients (49.3% female; 41.7% Black, 31.5% Hispanic). Models that included data from all visits indicated children meeting physical activity recommendations had reduced risk for abnormal blood pressure (odds ratio [OR] = 0.91, 95%CI 0.86, 0.97; p = 0.002), glycated hemoglobin (OR = 0.83, 95%CI 0.75, 0.91; p = 0.00006), alanine transaminase (OR = 0.85, 95%CI 0.79, 0.92; p = 0.00001), high-density lipoprotein (OR = 0.88, 95%CI 0.82, 0.95; p = 0.0009), and triglyceride values (OR = 0.89, 95%CI 0.83, 0.96; p = 0.002). Meeting screen time recommendations was not associated with abnormal cardiometabolic risk factors. CONCLUSION: Collecting information on reported adherence to meeting physical activity recommendations can provide clinicians with additional information to identify patients with a higher risk of adverse cardiometabolic health.

Association between frailty and sleep quality in people living with multiple sclerosis and obesity: An observational cross-sectional study.

BACKGROUND: A majority of the people with multiple sclerosis (pwMS) experience sleep disturbances. Frailty is also common in pwMS. The geriatric literature strongly suggests that frailty is associated with worse sleep outcomes in community-dwelling older adults, but this association has yet to be explored among pwMS. This study focused on examining the association between frailty and sleep quality in pwMS. METHODS: Seventy-six people with both MS and obesity (mean age: 47.6 ± 10.9 years, 81.6 % female, mean body mass index (BMI): 37.10 ± 5.5 kg/m2, mean Patient Determined Disease Steps (PDDS): 0.82 ± 1.20) were included in this cross-sectional secondary analysis. A comprehensive frailty index (FI) based on 41 health deficits from various health domains was calculated based on standardized procedures. Sleep quality was determined by the Pittsburgh Sleep Quality Index questionnaire (PSQI). RESULTS: Overall, 67.1 % of the participants were identified as non-frail (FI ≤ 0.25), and 32.9 % were identified as frail (FI > 0.25). A significant correlation was observed between FI scores and global PSQI scores (ρ = 0.43, p < 0.05). Cross-tabulation analyses revealed that frail participants had worse subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbances, daytime dysfunction, and higher use of sleep medications compared to non-frail participants (p < 0.05). CONCLUSIONS: The current study identified a significant association between frailty and sleep quality in people with both MS and obesity with minimal disability. These findings underscore the importance of untangling the relationship between frailty and sleep quality in pwMS. These results could lead to a more targeted approach for rehabilitation interventions aiming to improve frailty in MS.

Neurocomputational mechanisms of food and physical activity decision-making in male adolescents.

We examined the neurocomputational mechanisms in which male adolescents make food and physical activity decisions and how those processes are influenced by body weight and physical activity levels. After physical activity and dietary assessments, thirty-eight males ages 14-18 completed the behavioral rating and fMRI decision tasks for food and physical activity items. The food and physical activity self-control decisions were significantly correlated with each other. In both, taste- or enjoyment-oriented processes were negatively associated with successful self-control decisions, while health-oriented processes were positively associated. The correlation between taste/enjoyment and healthy attribute ratings predicted actual laboratory food intake and physical activities (2-week activity monitoring). fMRI data showed the decision values of both food and activity are encoded in the ventromedial prefrontal cortex, suggesting both decisions share common reward value-related circuits at the time of choice. Compared to the group with overweight/obese, the group with normal weight showed stronger brain activations in the cognitive control, multisensory integration, and motor control regions during physical activity decisions. For both food and physical activity, self-controlled decisions utilize similar computational and neurobiological mechanisms, which may provide insights into how to promote healthy food and physical activity decisions.

Neighborhood park access and park characteristics are associated with weight status in youth.

This study investigated park access and park quality in the context of childhood obesity. Participants were 20,638 children ages 6-17y from a large primary care health system. Analyses tested associations of park access and park characteristics with children's weight status, and sociodemographic interactions. Both park access and the quality of nearest park were associated with a lower odds of having obesity. Park quality interacted with age, sex, and income. Findings suggest park access is important for supporting a healthy weight in children. Park quality may be most important among 12-14-year-olds, girls, and higher income groups.

Associations between the Dietary Inflammatory Index and Sleep Metrics in the Energy Balance Study (EBS).

(1) Background: Sleep, a physiological necessity, has strong inflammatory underpinnings. Diet is a strong moderator of systemic inflammation. This study explored the associations between the Dietary Inflammatory Index (DII®) and sleep duration, timing, and quality from the Energy Balance Study (EBS). (2) Methods: The EBS (n = 427) prospectively explored energy intake, expenditure, and body composition. Sleep was measured using BodyMedia's SenseWear® armband. DII scores were calculated from three unannounced dietary recalls (baseline, 1-, 2-, and 3-years). The DII was analyzed continuously and categorically (very anti-, moderately anti-, neutral, and pro-inflammatory). Linear mixed-effects models estimated the DII score impact on sleep parameters. (3) Results: Compared with the very anti-inflammatory category, the pro-inflammatory category was more likely to be female (58% vs. 39%, p = 0.02) and African American (27% vs. 3%, p < 0.01). For every one-unit increase in the change in DII score (i.e., diets became more pro-inflammatory), wake-after-sleep-onset (WASO) increased (ßChange = 1.00, p = 0.01), sleep efficiency decreased (ßChange = −0.16, p < 0.05), and bedtime (ßChange = 1.86, p = 0.04) and waketime became later (ßChange = 1.90, p < 0.05). Associations between bedtime and the DII were stronger among African Americans (ßChange = 6.05, p < 0.01) than European Americans (ßChange = 0.52, p = 0.64). (4) Conclusions: Future studies should address worsening sleep quality from inflammatory diets, leading to negative health outcomes, and explore potential demographic differences.