Treadmill walking economy is not affected by body fat and body mass index in adults.

To determine whether body fat and body mass index (BMI) affect the energy cost of walking (Cw; J/kg/m), ventilation, and gas exchange data from 205 adults (115 females; percent body fat range = 3.0%-52.8%; BMI range = 17.5-43.2 kg/m2) were obtained at rest and during treadmill walking at 1.34 m/s to calculate gross and net Cw. Linear regression was used to assess relationships between body composition indices, Cw, and standing metabolic rate (SMR). Unpaired t-tests were used to assess differences between sex, and one-way ANOVA was used to assess differences by BMI categories: normal weight, <25.0 kg/m2; overweight, 25.0-29.9 km/m2; and obese, ≥30 kg/m2. Net Cw was not related to body fat percent, fat mass, or BMI (all R2 ≤ 0.011). Furthermore, mean net Cw was similar by sex (male: 2.19 ± 0.30 J/kg/m; female: 2.24 ± 0.37 J/kg/m, p = 0.35) and across BMI categories (normal weight: 2.23 ± 0.36 J/kg/m; overweight: 2.18 ± 0.33 J/kg/m; obese: 2.26 ± 0.31, p = 0.54). Gross Cw and SMR were inversely associated with percent body fat, fat mass, and BMI (all R2 between 0.033 and 0.270; all p ≤ 0.008). In conclusion, Net Cw is not influenced by body fat percentage, total body fat, and BMI and does not differ by sex.

Dietary Intake and Quality among Stroke Survivors: NHANES 1999-2018.

BACKGROUND: Nutrition is an important modifiable risk factor for prevention and treatment of stroke. However, examination of nutrient intake and diet quality in stroke survivors is limited. OBJECTIVES: The aim of the study was to estimate usual nutrient intake and diet quality in US adults with and without a history of self-reported stroke. METHODS: Using US National Health and Nutrition Examination Survey (NHANES) 1999-2018, we analyzed demographics, health history, and dietary intake data in 1626 individuals with a history of stroke matched for age, gender, and survey cycle to respective controls (n=1621) with no history of stroke. A minimum of one 24-h dietary recall was used to assess dietary intake. Diet quality was determined using Healthy Eating Index 2015 (HEI-2015) scores. Adult food security was assessed based on responses to the US Department of Agriculture Household Food Security Survey Module. Physical and mental limitations were assessed from responses to the NHANES Physical Functioning Questionnaire. Estimates were reported as mean (standard error). RESULTS: In comparison to controls, stroke survivors were more likely to be food insecure, experience poverty, and report physical and mental limitations (P < .001, all comparisons). Stroke survivors were more likely to report excessive (% > acceptable macronutrient distribution range) intake for total fat (50.9 [2.7]% vs. 40.4 [2.2]%, P < .001) and inadequate intake (% < estimated average requirement) for calcium (54.6 [1.8]% vs. 43.5 [2.4]%, P = .001) and magnesium (66 [1.8] vs. 53.6 [1.8]%, P < .001). In addition, stroke survivors reported lower HEI-2015 total scores than controls (49.8 vs. 51.9, P < .001). Finally, HEI-2015 total scores were lower in stroke survivors who were food insecure and those with a lower income-to-poverty ratio (< 185%) (P = .001). CONCLUSIONS: Dietary intake in stroke survivors was nutritionally poor, with suboptimal nutrient intake and lower overall diet quality compared with age- and gender-matched controls. Furthermore, poverty and food insecurity were more prevalent in stroke survivors and associated with worse diet quality.

College Campus Food Pantry Program Evaluation: What Barriers Do Students Face to Access On-Campus Food Pantries?

The purpose of this study was to explore barriers Texas Woman's University (TWU) students face in accessing on-campus food pantries. This cross-sectional, survey-based study was conducted in Fall 2021. Students' use of the food pantries and barriers to utilization, including qualitative questions, were evaluated using descriptive statistics and thematic analyses. Students (n = 529) completed the survey. Despite a high prevalence of food insecurity (49.2%), most students reported never using the pantries (89.8%). Almost half of the students were unaware that these pantries existed on campus (47.8%). More than one in four students believed there were barriers to accessing the pantries, with time tissues, lack of transportation, limited food pantry hours of operation, and social stigma most commonly cited as major barriers to access. Food insecurity remains an urgent problem at TWU since the prevalence has remained high since 2019 despite the institutions' efforts to reduce it. One of those resources has not been utilized as expected, which might impede improvements in food security among students. TWU on-campus food pantries can use these findings to address major barriers by offering after-hours access through the libraries or campus police, partnering with public transportation, and normalizing accessing food assistance.

Influence of COVID-19 Pandemic Restrictions on College Students' Dietary Quality and Experience of the Food Environment.

The COVID-19 pandemic restrictions sent college students online and off campus, potentially reducing access to healthy food. The objective of this cross-sectional, internet-based study was to use qualitative and quantitative survey methods to evaluate whether COVID-19 pandemic restrictions in Texas, USA affected college students' ability to buy food, how/what they shopped for, how they prepared food, what they ate, how they felt about eating, and overall dietary quality (assessed using Healthy Eating Index [HEI] scores). Survey responses from 502 students (87.5% female; 59.6% nonwhite, mean age 27.5 ± 0.4 years, >50% graduate students) were analyzed. The qualitative analysis of open-ended questions revealed 110 codes, 17 subthemes, and six themes. Almost all students experienced changes in at least one area, the most common being changes in shopping habits. Participants with low or very low food security had lower HEI scores compared to food secure students (p = 0.047). Black students were more likely to report changes in their ability to buy food (p = 0.035). The COVID-19 restrictions varied in their impact on students' ability to access sufficient healthy food, with some students severely affected. Thus, universities should establish procedures for responding to emergencies, including identifying at-risk students and mobilizing emergency funds and/or food assistance.

Cycling efficiency and energy cost of walking in young and older adults.

To determine whether age affects cycling efficiency and the energy cost of walking (Cw), 190 healthy adults, ages 18-81 yr, cycled on an ergometer at 50 W and walked on a treadmill at 1.34 m/s. Ventilation and gas exchange at rest and during exercise were used to calculate net Cw and net efficiency of cycling. Compared with the 18-40 yr age group (2.17 ± 0.33 J·kg-1·m-1), net Cw was not different in the 60-64 yr (2.20 ± 0.40 J·kg-1·m-1) and 65-69 yr (2.20 ± 0.28 J·kg-1·m-1) age groups, but was significantly ( P < 0.03) higher in the ≥70 yr (2.37 ± 0.33 J·kg-1·m-1) age group. For subjects >60 yr, net Cw was significantly correlated with age ( R2 = 0.123; P = 0.002). Cycling net efficiency was not different between 18-40 yr (23.5 ± 2.9%), 60-64 yr (24.5 ± 3.6%), 65-69 yr (23.3 ± 3.6%) and ≥70 yr (24.7 ± 2.7%) age groups. Repeat tests on a subset of subjects (walking, n = 43; cycling, n = 37) demonstrated high test-retest reliability [intraclass correlation coefficients (ICC), 0.74-0.86] for all energy outcome measures except cycling net energy expenditure (ICC = 0.54) and net efficiency (ICC = 0.50). Coefficients of variation for all variables ranged from 3.1 to 7.7%. Considerable individual variation in Cw and efficiency was evident, with a ~2-fold difference between the least and most economical/efficient subjects. We conclude that, between 18 and 81 yr, net Cw was only higher for ages ≥70 yr, and that cycling net efficiency was not different across age groups. NEW & NOTEWORTHY This study illustrates that the higher energy cost of walking in older adults is only evident for ages ≥70 yr. For older adults ages 60-69 yr, the energy cost of walking is similar to that of young adults. Cycling efficiency, by contrast, is not different across age groups. Considerable individual variation (∼2-fold) in cycling efficiency and energy cost of walking is observed in young and older adults.

Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity.

BACKGROUND: Emerging interventions that rely on and harness variability in behavior to adapt to individual performance over time may outperform interventions that prescribe static goals (e.g., 10,000 steps/day). The purpose of this factorial trial was to compare adaptive vs. static goal setting and immediate vs. delayed, non-contingent financial rewards for increasing free-living physical activity (PA). METHODS: A 4-month 2 × 2 factorial randomized controlled trial tested main effects for goal setting (adaptive vs. static goals) and rewards (immediate vs. delayed) and interactions between factors to increase steps/day as measured by a Fitbit Zip. Moderate-to-vigorous PA (MVPA) minutes/day was examined as a secondary outcome. RESULTS: Participants (N = 96) were mainly female (77%), aged 41 ± 9.5 years, and all were insufficiently active and overweight/obese (mean BMI = 34.1 ± 6.2). Participants across all groups increased by 2389 steps/day on average from baseline to intervention phase (p < .001). Participants receiving static goals showed a stronger increase in steps per day from baseline phase to intervention phase (2630 steps/day) than those receiving adaptive goals (2149 steps/day; difference = 482 steps/day, p = .095). Participants receiving immediate rewards showed stronger improvement (2762 step/day increase) from baseline to intervention phase than those receiving delayed rewards (2016 steps/day increase; difference = 746 steps/day, p = .009). However, the adaptive goals group showed a slower decrease in steps/day from the beginning of the intervention phase to the end of the intervention phase (i.e. less than half the rate) compared to the static goals group (-7.7 steps vs. -18.3 steps each day; difference = 10.7 steps/day, p < .001) resulting in better improvements for the adaptive goals group by study end. Rate of change over the intervention phase did not differ between reward groups. Significant goal phase x goal setting x reward interactions were observed. CONCLUSIONS: Adaptive goals outperformed static goals (i.e., 10,000 steps) over a 4-month period. Small immediate rewards outperformed larger, delayed rewards. Adaptive goals with either immediate or delayed rewards should be preferred for promoting PA. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02053259 registered prospectively on January 31, 2014.

Validity of SenseWear® Armband v5.2 and v2.2 for estimating energy expenditure.

We compared SenseWear Armband versions (v) 2.2 and 5.2 for estimating energy expenditure in healthy adults. Thirty-four adults (26 women), 30.1 ± 8.7 years old, performed two trials that included light-, moderate- and vigorous-intensity activities: (1) structured routine: seven activities performed for 8-min each, with 4-min of rest between activities; (2) semi-structured routine: 12 activities performed for 5-min each, with no rest between activities. Energy expenditure was measured by indirect calorimetry and predicted using SenseWear v2.2 and v5.2. Compared to indirect calorimetry (297.8 ± 54.2 kcal), the total energy expenditure was overestimated (P < 0.05) by both SenseWear v2.2 (355.6 ± 64.3 kcal) and v5.2 (342.6 ± 63.8 kcal) during the structured routine. During the semi-structured routine, the total energy expenditure for SenseWear v5.2 (275.2 ± 63.0 kcal) was not different than indirect calorimetry (262.8 ± 52.9 kcal), and both were lower (P < 0.05) than v2.2 (312.2 ± 74.5 kcal). The average mean absolute per cent error was lower for the SenseWear v5.2 than for v2.2 (P < 0.001). SenseWear v5.2 improved energy expenditure estimation for some activities (sweeping, loading/unloading boxes, walking), but produced larger errors for others (cycling, rowing). Although both algorithms overestimated energy expenditure as well as time spent in moderate-intensity physical activity (P < 0.05), v5.2 offered better estimates than v2.2.

Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure.

BACKGROUND: The Nike + Fuelband is a commercially available, wrist-worn accelerometer used to track physical activity energy expenditure (PAEE) during exercise. However, validation studies assessing the accuracy of this device for estimating PAEE are lacking. Therefore, this study examined the validity and reliability of the Nike + Fuelband for estimating PAEE during physical activity in young adults. Secondarily, we compared PAEE estimation of the Nike + Fuelband with the previously validated SenseWear Armband (SWA). METHODS: Twenty-four participants (n = 24) completed two, 60-min semi-structured routines consisting of sedentary/light-intensity, moderate-intensity, and vigorous-intensity physical activity. Participants wore a Nike + Fuelband and SWA, while oxygen uptake was measured continuously with an Oxycon Mobile (OM) metabolic measurement system (criterion). RESULTS: The Nike + Fuelband (ICC = 0.77) and SWA (ICC = 0.61) both demonstrated moderate to good validity. PAEE estimates provided by the Nike + Fuelband (246 ± 67 kcal) and SWA (238 ± 57 kcal) were not statistically different than OM (243 ± 67 kcal). Both devices also displayed similar mean absolute percent errors for PAEE estimates (Nike + Fuelband = 16 ± 13 %; SWA = 18 ± 18 %). Test-retest reliability for PAEE indicated good stability for Nike + Fuelband (ICC = 0.96) and SWA (ICC = 0.90). CONCLUSION: The Nike + Fuelband provided valid and reliable estimates of PAEE, that are similar to the previously validated SWA, during a routine that included approximately equal amounts of sedentary/light-, moderate- and vigorous-intensity physical activity.