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.

Rationale, design, and baseline characteristics of WalkIT Arizona: A factorial randomized trial testing adaptive goals and financial reinforcement to increase walking across higher and lower walkable neighborhoods.

Little change over the decades has been seen in adults meeting moderate-to-vigorous physical activity (MVPA) guidelines. Numerous individual-level interventions to increase MVPA have been designed, mostly static interventions without consideration for neighborhood context. Recent technologies make adaptive interventions for MVPA feasible. Unlike static interventions, adaptive intervention components (e.g., goal setting) adjust frequently to an individual's performance. Such technologies also allow for more precise delivery of "smaller, sooner incentives" that may result in greater MVPA than "larger, later incentives". Combined, these factors could enhance MVPA adoption. Additionally, a central tenet of ecological models is that MVPA is sensitive to neighborhood environment design; lower-walkable neighborhoods constrain MVPA adoption and maintenance, limiting the effects of individual-level interventions. Higher-walkable neighborhoods are hypothesized to enhance MVPA interventions. Few prospective studies have addressed this premise. This report describes the rationale, design, intervention components, and baseline sample of a study testing individual-level adaptive goal-setting and incentive interventions for MVPA adoption and maintenance over 2 years among adults from neighborhoods known to vary in neighborhood walkability. We scaled these evidenced-based interventions and tested them against static-goal-setting and delayed-incentive comparisons in a 2 × 2 factorial randomized trial to increase MVPA among 512 healthy insufficiently-active adults. Participants (64.3% female, M age = 45.5 ±â€¯9.1 years, M BMI = 33.9 ±â€¯7.3 kg/m2, 18.8% Hispanic, 84.0% White) were recruited from May 2016 to May 2018 from block groups ranked on GIS-measured neighborhood walkability and socioeconomic status (SES) and classified into four neighborhood types: "high walkable/high SES," "high walkable/low SES," "low walkable/high SES," and "low walkable/low SES." Results from this ongoing study will provide evidence for some of the central research questions of ecological models.

Cardiorespiratory Fitness (Peak Oxygen Uptake): Safe and Effective Measure for Cardiovascular Screening Before Kidney Transplant.

BACKGROUND: Significant heterogeneity exists in practice patterns and algorithms used for cardiac screening before kidney transplant. Cardiorespiratory fitness, as measured by peak oxygen uptake (VO2peak), is an established validated predictor of future cardiovascular morbidity and mortality in both healthy and diseased populations. The literature supports its use among asymptomatic patients in abrogating the need for further cardiac testing. METHODS AND RESULTS: We outlined a pre-renal transplant screening algorithm to incorporate VO2peak testing among a population of asymptomatic high-risk patients (with diabetes mellitus and/or >50 years of age). Only those with VO2peak <17 mL/kg per minute (equivalent to <5 metabolic equivalents) underwent further noninvasive cardiac screening tests. We conducted a retrospective study of the a priori dichotomization of the VO2peak <17 versus ≥17 mL/kg per minute to determine negative and positive predictive value of future cardiac events and all-cause mortality. We report a high (>90%) negative predictive value, indicating that VO2peak ≥17 mL/kg per minute is effective to rule out future cardiac events and all-cause mortality. However, lower VO2peak had low positive predictive value and should not be used as a reliable metric to predict future cardiac events and/or mortality. In addition, a simple mathematical calculation documented a cost savings of ≈$272 600 in the cardiac screening among our study cohort of 637 patients undergoing evaluation for kidney and/or pancreas transplant. CONCLUSIONS: We conclude that incorporating an objective measure of cardiorespiratory fitness with VO2peak is safe and allows for a cost savings in the cardiovascular screening protocol among higher-risk phenotype (with diabetes mellitus and >50 years of age) being evaluated for kidney transplant.

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.

Strength fitness and body weight status on markers of cardiometabolic health.

INTRODUCTION: Recent evidence suggests that resistance training (RT) may reduce metabolic and cardiovascular disease risk. We investigated whether overweight/class I obese individuals by BMI classification with high strength fitness exhibit cardiovascular/metabolic phenotypes similar to those overweight/obese and untrained or those normal-weight with high strength fitness. METHODS: A total of 90 young males were categorized into three groups: overweight untrained (OU, n = 30, BMI > 27 kg·m⁻²), overweight trained (OT, n = 30, BMI > 27 kg·m⁻², RT ≥ 4 d·wk⁻¹), and normal-weight trained (NT, n = 30, BMI < 25 kg·m⁻², RT ≥ 4 d·wk⁻¹). Participants were assessed for strength, body composition, central/peripheral blood pressures, arterial stiffness, and markers of cardiovascular and metabolic health. RESULTS: Body weight was similar in OT and OU and greater than NT (P < 0.00001), and fat mass was different in all groups (P < 0.001). Compared to OU, NT and OT groups exhibited higher relative strength (NT = 46.7%, OT = 44.4%, P < 0.00001), subendocardial viability ratio (NT = 21.0%, P < 0.001; OT = 17.0%, P < 0.01), and lower brachial/central blood pressures (NT P < 0.001; OT P ≤ 0.05); augmentation index and pulse-wave velocity were lower only in OT (P < 0.05). Total cholesterol, low-density lipoprotein (NT P < 0.01, OT P < 0.05), triglycerides (NT = -50.4%, OT = -41.8%, P < 0.001), oxidized LDL (NT = -39.8%, OT = -31.8%, P < 0.001), and CRP (NT = -63.7%, OT = -67.4%, P < 0.01) levels were lower and high-density lipoprotein (NT = 26.9%, OT = 21.4%, P < 0.001) levels were higher in NT and OT compared to OU. NT and OT also exhibited lower amylin (NT = -55.8%, OT = -40.8%) and leptin (NT = -84.6%, OT = -59.4%) and higher adiponectin (NT = 87.5%, P < 0.001; OT = 78.1%, P < 0.01) and sex hormone-binding globulin (NT = 124.4%, OT = 92.3%, P < 0.001). Despite greater total and trunk fat in OT compared with NT, other than glucose and insulin, which were lower in NT than in both OT and OU (OT P < 0.01, OU P < 0.001), OT did not exhibit any impaired biomarker/phenotype compared to NT. CONCLUSIONS: These findings provide evidence that overweight/class I obese individuals with high strength fitness exhibit metabolic/cardiovascular risk profiles similar to normal-weight, fit individuals rather than overweight/class I obese unfit individuals. Strength training may be important to metabolic and cardiovascular health.