Does frequency or duration of standing breaks drive changes in glycemic response? A randomized crossover trial.

Intervention strategies to break up sitting have mostly focused on the modality (i.e., comparing different intensities and/or type of activities) and less on how frequency and duration of breaks affect health outcomes. This study compared the efficacy of different strategies to break up sitting time [i.e., high frequency, low duration standing breaks (HFLD) and low frequency, high duration standing breaks (LFHD)] in reducing postprandial glucose. Eleven sedentary and prediabetic adults (mean ± SD age = 46.8 ± 10.6 years; 73% female) participated in a cross-over trial. There were six blocks that represented all potential combinations (ordering) of the study conditions and participants were randomly assigned to a block. Each participant underwent three 7.5-h laboratory visits (1 week apart) where they engaged in either continuous sitting, HFLD, or LFHD condition while performing their usual office-related tasks. Standardized breakfast and lunch meals were provided. Postprandial mean glucose, area under the curve (AUC), and incremental area under the curve (iAUC) were evaluated using mixed models. Compared with LFHD condition, the HFLD standing breaks condition significantly lowered mean glucose by -9.94 (-14.13, -5.74) mg/dL·h after lunch, and by -6.23 (-9.93, -2.52) mg/dL·h, for the total lab visit time. Overall, the results favor frequently interrupting sitting with standing breaks to improve glycemic control in individuals with prediabetes. Further studies are needed with larger sample sizes to confirm the results.

Cardiometabolic Risk Factors Among Insufficiently Active African American Women With Obesity: Baseline Findings From Smart Walk.

BACKGROUND: Low moderate-to-vigorous physical activity (MVPA) levels and obesity are associated with increased cardiometabolic disease risk. OBJECTIVE: The aim of this study was to describe MVPA and cardiometabolic risk characteristics of insufficiently active African American women with obesity (N = 60) enrolled in a culturally tailored MVPA intervention. METHODS: We assessed accelerometer-measured and self-reported MVPA, blood pressure, serum lipid profiles, cardiorespiratory fitness (VO 2 peak), and aortic pulse wave velocity. RESULTS: Participants (mean age, 38.4; mean body mass index, 40.6 kg/m 2 ) averaged 15 min/d of accelerometer-measured MVPA and 30 min/wk of self-reported MVPA. Systolic and diastolic blood pressure levels were elevated (135.4 and 84.0 mm Hg, respectively). With the exception of low-density lipoprotein cholesterol (121.4 mg/dL) and high-density lipoprotein cholesterol (47.6 mg/dL), lipid profiles were within reference ranges. Compared with normative reference values, average VO 2 peak was low (18.7 mL/kg/min), and pulse wave velocity was high (7.4 m/s). CONCLUSIONS: Our sample of insufficiently active African American women with obesity was at an elevated risk for cardiometabolic disease.

Efficacy of the 'Stand and Move at Work' multicomponent workplace intervention to reduce sedentary time and improve cardiometabolic risk: a group randomized clinical trial.

BACKGROUND: Sedentary time is associated with chronic disease and premature mortality. We tested a multilevel workplace intervention with and without sit-stand workstations to reduce sedentary time and lower cardiometabolic risk. METHODS: Stand and Move at Work was a group (cluster) randomized trial conducted between January 2016 and December 2017 among full-time employees; ≥18 years; and in academic, industry/healthcare, and government worksites in Phoenix, Arizona and Minneapolis/St. Paul, Minnesota, USA. Eligible worksites were randomized to (a) MOVE+, a multilevel intervention targeting reduction in sedentary time and increases in light physical activity (LPA); or (b) STAND+, the MOVE+ intervention along with sit-stand workstations to allow employees to sit or stand while working. The primary endpoints were objectively-measured workplace sitting and LPA at 12 months. The secondary endpoint was a clustered cardiometabolic risk score (blood pressure, glucose, insulin, triglycerides, and HDL-cholesterol) at 12 months. RESULTS: Worksites (N = 24; academic [n = 8], industry/healthcare [n = 8], and government [n = 8] sectors) and employees (N = 630; 27 ± 8 per worksite; 45 ± 11 years of age, 74% female) were enrolled. All worksites were retained and 487 participants completed the intervention and provided data for the primary endpoint. The adjusted between arm difference in sitting at 12 months was - 59.2 (CI: - 74.6,-43.8) min per 8 h workday, favoring STAND+, and in LPA at 12 months was + 2.2 (- 0.9,5.4) min per 8 h workday. Change in the clustered metabolic risk score was small and not statistically significant, but favored STAND+. In an exploratory subgroup of 95 participants with prediabetes or diabetes, the effect sizes were larger and clinically meaningful, all favoring STAND+, including blood glucose, triglycerides, systolic blood pressure, glycated hemoglobin, LDL-cholesterol, body weight, and body fat. CONCLUSIONS: Multilevel workplace interventions that include the use of sit-stand workstations are effective for large reductions in sitting time over 12 months. Among those with prediabetes or diabetes, clinical improvements in cardiometabolic risk factors and body weight may be realized. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02566317 . Registered 2 October 2015, first participant enrolled 11 January 2016.

High-intensity interval exercise attenuates but does not eliminate endothelial dysfunction after a fast food meal.

We investigated whether two different bouts of high-intensity interval exercise (HIIE) could attenuate postprandial endothelial dysfunction. Thirteen young (27 ± 1 yr), nonexercise-trained men underwent three randomized conditions: 1) four 4-min intervals at 85-95% of maximum heart rate separated by 3 min of active recovery (HIIE 4 × 4), 2) 16 1-min intervals at 85-95% of maximum heart rate separated by 1 min of active recovery (HIIE 16 × 1), and 3) sedentary control. HIIE was performed in the afternoon, ~18 h before the morning fast food meal (1,250 kcal, 63g of fat). Brachial artery flow-mediated dilation (FMD) was performed before HIIE ( baseline 1), during fasting before meal ingestion ( baseline 2), and 30 min, 2 h, and 4 h postprandial. Capillary glucose and triglycerides were assessed at fasting, 30 min, 1 h, 2 h, and 4 h (triglycerides only). Both HIIE protocols increased fasting FMD compared with control (HIIE 4 × 4: 6.1 ± 0.4%, HIIE 16 × 1: 6.3 ± 0.5%, and control: 5.1 ± 0.4%, P < 0.001). For both HIIE protocols, FMD was reduced only at 30 min postprandial but never fell below baseline 1 or FMD during control at any time point. In contrast, control FMD decreased at 2 h (3.8 ± 0.4%, P < 0.001) and remained significantly lower than HIIE 4 × 4 and 16 × 1 at 2 and 4 h. Postprandial glucose and triglycerides were unaffected by HIIE. In conclusion, HIIE performed ~18 h before a high-energy fast food meal can attenuate but not entirely eliminate postprandial decreases in FMD. This effect is not dependent on reductions in postprandial lipemia or glycemia. NEW & NOTEWORTHY Two similar high-intensity interval exercise (HIIE) protocols performed ∼18 h before ingestion of a high-energy fast food meal attenuated but did not entirely eliminate postprandial endothelial dysfunction in young men largely by improving fasting endothelial function. Both HIIE protocols produced essentially identical results, suggesting high reproducibility of HIIE effects.

Kids are not little adults: what MET threshold captures sedentary behavior in children?

PURPOSE: The study compares MET-defined cutpoints used to classify sedentary behaviors in children using a simulated free-living design. METHODS: A sample of 102 children (54 boys and 48 girls; 7-13 years) completed a set of 12 activities (randomly selected from a pool of 24 activities) in a random order. Activities were predetermined and ranged from sedentary to vigorous intensities. Participant's energy expenditure was measured using a portable indirect calorimetry system, Oxycon mobile. Measured minute-by-minute VO2 values (i.e., ml/kg/min) were converted to an adult- or child-MET value using the standard 3.5 ml/kg/min or the estimated child resting metabolic rate, respectively. Classification agreement was examined for both the "standard" (1.5 adult-METs) and an "adjusted" (2.0 adult-METs) MET-derived threshold for classifying sedentary behavior. Alternatively, we also tested the classification accuracy of a 1.5 child-MET threshold. Classification accuracy of sedentary activities was evaluated relative to the predetermined intensity categorization using receiver operator characteristic curves. RESULTS: There were clear improvements in the classification accuracy for sedentary activities when a threshold of 2.0 adult-METs was used instead of 1.5 METs (Se1.5 METs = 4.7%, Sp1.5 METs = 100.0%; Se2.0 METs = 36.9%, Sp2.0 METs = 100.0 %). The use of child-METs while maintaining the 1.5 threshold also resulted in improvements in classification (Se = 45.1%, Sp = 100.0%). CONCLUSION: Adult-MET thresholds are not appropriate for children when classifying sedentary activities. Classification accuracy for identifying sedentary activities was improved when either an adult-MET of 2.0 or a child-MET of 1.5 was used.

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.

Excess Postexercise Oxygen Consumption After High-Intensity and Sprint Interval Exercise, and Continuous Steady-State Exercise.

Tucker, WJ, Angadi, SS, and Gaesser, GA. Excess postexercise oxygen consumption after high-intensity and sprint interval exercise, and continuous steady-state exercise. J Strength Cond Res 30(11): 3090-3097, 2016-Higher excess postexercise oxygen consumption (EPOC) after high-intensity interval exercise (HIE) and sprint interval exercise (SIE) may contribute to greater fat loss sometimes reported after interval training compared with continuous steady-state exercise (SSE) training. We compared EPOC after HIE, SIE, and SSE. Ten recreationally active men (age 24 ± 4 years) participated in this randomized crossover study. On separate days, subjects completed a resting control trial and 3 exercise conditions on a cycle ergometer: HIE (four 4-minute intervals at 95% peak heart rate (HRpeak), separated by 3 minutes of active recovery), SIE (six 30-second Wingate sprints, separated by 4 minutes of active recovery), and SSE (30 minutes at 80% of HRpeak). Oxygen consumption (V[Combining Dot Above]O2) was measured continuously during and for 3 hours after exercise. For all conditions, V[Combining Dot Above]O2 was higher than resting control only during the first hour postexercise. Although 3-hour EPOC and total net exercise energy expenditure (EE) after exercise were higher (p = 0.01) for SIE (22.0 ± 9.3 L; 110 ± 47 kcal) compared with SSE (12.8 ± 8.5 L; 64 ± 43 kcal), total (exercise + postexercise) net O2 consumed and net EE were greater (p = 0.03) for SSE (69.5 ± 18.4 L; 348 ± 92 kcal) than those for SIE (54.2 ± 12.0 L; 271 ± 60 kcal). Corresponding values for HIE were not significantly different from SSE or SIE. Excess postexercise oxygen consumption after SIE and HIE is unlikely to account for the greater fat loss per unit EE associated with SIE and HIE training reported in the literature.

Postexercise Hypotension After Continuous, Aerobic Interval, and Sprint Interval Exercise.

We examined the effects of 3 exercise bouts, differing markedly in intensity, on postexercise hypotension (PEH). Eleven young adults (age: 24.6 ± 3.7 years) completed 4 randomly assigned experimental conditions: (a) control, (b) 30-minute steady-state exercise (SSE) at 75-80% maximum heart rate (HRmax), (4) aerobic interval exercise (AIE): four 4-minute bouts at 90-95% HRmax, separated by 3 minutes of active recovery, and (d) sprint interval exercise (SIE): six 30-second Wingate sprints, separated by 4 minutes of active recovery. Exercise was performed on a cycle ergometer. Blood pressure (BP) was measured before exercise and every 15-minute postexercise for 3 hours. Linear mixed models were used to compare BP between trials. During the 3-hour postexercise, systolic BP (SBP) was lower (p < 0.001) after AIE (118 ± 10 mm Hg), SSE (121 ± 10 mm Hg), and SIE (121 ± 11 mm Hg) compared with control (124 ± 8 mm Hg). Diastolic BP (DBP) was also lower (p < 0.001) after AIE (66 ± 7 mm Hg), SSE (69 ± 6 mm Hg), and SIE (68 ± 8 mm Hg) compared with control (71 ± 7 mm Hg). Only AIE resulted in sustained (>2 hours) PEH, with SBP (120 ± 9 mm Hg) and DBP (68 ± 7 mm Hg) during the third-hour postexercise being lower (p ≤ 0.05) than control (124 ± 8 and 70 ± 7 mm Hg). Although all exercise bouts produced similar reductions in BP at 1-hour postexercise, the duration of PEH was greatest after AIE.

Using a Verification Test for Determination of V[Combining Dot Above]O2max in Sedentary Adults With Obesity.

A constant-load exercise bout to exhaustion after a graded exercise test to verify maximal oxygen uptake (V[Combining Dot Above]O2max) during cycle ergometry has not been evaluated in sedentary adults with obesity. Nineteen sedentary men (n = 10) and women (n = 9) with obesity (age = 35.8 ± 8.6 years; body mass index [BMI] = 35.9 ± 5.1 kg·m; body fat percentage = 44.9 ± 7.2) performed a ramp-style maximal exercise test (ramp), followed by 5-10 minutes of active recovery, and then performed a constant-load exercise bout to exhaustion (verification test) on a cycle ergometer for determination of V[Combining Dot Above]O2max and maximal heart rate (HRmax). V[Combining Dot Above]O2max did not differ between tests (ramp: 2.29 ± 0.71 L·min, verification: 2.34 ± 0.67 L·min; p = 0.38). Maximal heart rate was higher on the verification test (177 ± 13 b·min vs. 174 ± 16 b·min; p = 0.03). Thirteen subjects achieved a V[Combining Dot Above]O2max during the verification test that was ≥2% (range: 2.0-21.0%; 0.04-0.47 L·min) higher than during the ramp test, and 8 subjects achieved a HRmax during the verification test that was 4-14 b·min higher than during the ramp test. Duration of verification or ramp tests did not affect V[Combining Dot Above]O2max results, but the difference in HRmax between the tests was inversely correlated with ramp test duration (r = -0.57, p = 0.01). For both V[Combining Dot Above]O2max and HRmax, differences between ramp and verification tests were not correlated with BMI or body fat percentage. A verification test may be useful for identifying the highest V[Combining Dot Above]O2max and HRmax during cycle ergometry in sedentary adults with obesity.

Physiological Responses to High-Intensity Interval Exercise Differing in Interval Duration.

We determined the oxygen uptake (V[Combining Dot Above]O2), heart rate (HR), and blood lactate responses to 2 high-intensity interval exercise protocols differing in interval length. On separate days, 14 recreationally active males performed a 4 × 4 (four 4-minute intervals at 90-95% HRpeak, separated by 3-minute recovery at 50 W) and 16 × 1 (sixteen 1-minute intervals at 90-95% HRpeak, separated by 1-minute recovery at 50 W) protocol on a cycle ergometer. The 4 × 4 elicited a higher mean V[Combining Dot Above]O2 (2.44 ± 0.4 vs. 2.36 ± 0.4 L·min) and "peak" V[Combining Dot Above]O2 (90-99% vs. 76-85% V[Combining Dot Above]O2peak) and HR (95-98% HRpeak vs. 81-95% HRpeak) during the high-intensity intervals. Average power maintained was higher for the 16 × 1 (241 ± 45 vs. 204 ± 37 W), and recovery interval V[Combining Dot Above]O2 and HR were higher during the 16 × 1. No differences were observed for blood lactate concentrations at the midpoint (12.1 ± 2.2 vs. 10.8 ± 3.1 mmol·L) and end (10.6 ± 1.5 vs. 10.6 ± 2.4 mmol·L) of the protocols or ratings of perceived exertion (7.0 ± 1.6 vs. 7.0 ± 1.4) and Physical Activity Enjoyment Scale scores (91 ± 15 vs. 93 ± 12). Despite a 4-fold difference in interval duration that produced greater between-interval transitions in V[Combining Dot Above]O2 and HR and slightly higher mean V[Combining Dot Above]O2 during the 4 × 4, mean HR during each protocol was the same, and both protocols were rated similarly for perceived exertion and enjoyment. The major difference was that power output had to be reduced during the 4 × 4 protocol to maintain the desired HR.

Predictors of fat mass changes in response to aerobic exercise training in women.

Aerobic exercise training in women typically results in minimal fat loss, with considerable individual variability. We hypothesized that women with higher baseline body fat would lose more body fat in response to exercise training and that early fat loss would predict final fat loss. Eighty-one sedentary premenopausal women (age: 30.7 ± 7.8 years; height: 164.5 ± 7.4 cm; weight: 68.2 ± 16.4 kg; fat percent: 38.1 ± 8.8) underwent dual-energy x-ray absorptiometry before and after 12 weeks of supervised treadmill walking 3 days per week for 30 minutes at 70% of (Equation is included in full-text article.). Overall, women did not lose body weight or fat mass. However, considerable individual variability was observed for changes in body weight (-11.7 to +4.8 kg) and fat mass (-11.8 to +3.7 kg). Fifty-five women were classified as compensators and, as a group, gained fat mass (25.6 ± 11.1 kg to 26.1 ± 11.3 kg; p < 0.001). The strongest correlates of change in body fat at 12 weeks were change in body weight (r = 0.52) and fat mass (r = 0.48) at 4 weeks. Stepwise regression analysis that included change in body weight and body fat at 4 weeks and submaximal exercise energy expenditure yielded a prediction model that explained 37% of the variance in fat mass change (R = 0.37, p < 0.001). Change in body weight and fat mass at 4 weeks were moderate predictors of fat loss and may potentially be useful for identification of individuals who achieve less than expected weight loss or experience unintended fat gain in response to exercise training.

Fitness versus Fatness: Which Influences Health and Mortality Risk the Most?

Cardiorespiratory fitness (CRF) is a more powerful predictor of mortality than body mass index or adiposity, and improving CRF is more important than losing body fat for reducing risk of cardiovascular disease and all-cause mortality. Data on reduced morbidity and mortality associated with increased CRF are strong and consistent. By contrast, data on intentional weight loss and mortality are uncertain, and weight loss-induced risk factor modification may be largely transient. Because weight loss maintenance is poor and considering the health risks associated with chronic weight instability ( "yo-yo" dieting), we propose an alternative paradigm that focuses on improving CRF rather than reducing body weight. We contend that this is a safer alternative for management of obesity and the associated comorbidities. Exercise adherence may improve if clinicians emphasized to their patients the importance of CRF compared with weight loss in improving health and reducing the risk of chronic diseases.

Navigating the gluten-free boom.

Gluten-free diets have gained popularity with the public at a rate greater than would be expected based on the prevalence of gluten-related disorders such celiac disease, nonceliac gluten sensitivity, and wheat allergy. This article reviews gluten-related disorders, indications for gluten-free diets, and the possible health benefits of gluten. Despite the health claims for gluten-free eating, no published experimental evidence supports weight-loss with a gluten-free diet or suggests that the general population would benefit from avoiding gluten.

Strength training increases endurance time to exhaustion during high-intensity exercise despite no change in critical power.

The purpose of this study was to determine whether improvements in endurance exercise performance elicited by strength training were accurately reflected by changes in parameters of the power-duration hyperbola for high-intensity exercise. Before and after 8 weeks of strength training (N = 14) or no exercise, control (N = 5), 19 males (age: 20.6 ± 2.0 years; weight: 78.2 ± 15.9 kg) performed a maximal incremental exercise test on a cycle ergometer and also cycled to exhaustion during 4 constant-power exercise bouts. Critical power (CP) and anaerobic work capacity (W') were estimated using nonlinear and linear models. Subjects in the strength training group improved significantly more than controls (p < 0.05) for strength (~30%), power at V[Combining Dot Above]O2peak (7.9%), and time to exhaustion (TTE) for all 4 constant-power tests (~39%). Contrary to our hypothesis, CP did not change significantly after strength training (p > 0.05 for all models). Strength training improved W' (mean range of improvement = +5.8 to +10.0 kJ; p < 0.05) for both linear models. Increases in W' were consistently positively correlated with improvements in TTE, whereas changes in CP were not. Our findings indicate that strength training alters the power-duration hyperbola such that W' is enhanced without any improvement in CP. Consequently, CP may not be robust enough to track changes in endurance capacity elicited by strength training, and we do not recommend it to be used for this purpose. Conversely, W' may be the better indicator of improvement in endurance performance elicited by strength training.

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.

Evaluation of racial differences in resting and postprandial endothelial function in postmenopausal women matched for age, fitness and body composition.

OBJECTIVE: We investigated endothelial function at rest and after a high-fat meal challenge in African American (AA) and Caucasian postmenopausal women matched for age, body mass index, percent fat and fitness level. DESIGN: Pilot study. SETTING: University of Virginia General Clinical Research Center. PARTICIPANTS: Eight AA and 8 Caucasian postmenopausal women. INTERVENTION: PARTICIPANTS underwent a VO2 peak treadmill protocol, percent fat assessment, and brachial artery flow-mediated dilation measurements (baseline and 4 hours following a high-fat meal). MAIN OUTCOMES MEASURES: Baseline and postprandial flow mediated dilation (FMD) following a high-fat meal. RESULTS: FMD values were similar in AA (5.4%, 95% CI: 3.3, 7.4) and Caucasian women (4.0%, 95% CI: 2.0, 6.1). There was no significant change in FMD from baseline to four hours following the meal challenge within groups (AA: .9%, P = .397, Caucasian: 2.3%, P = .063) or between groups (P = .449), despite a significant increase in triglycerides (AA: 81.8 mg/dL, P < .001, Caucasian: 99.7 mg/dL, P = .004). CONCLUSIONS: The present pilot study found that when postmenopausal AA and Caucasian women are matched for age, fitness and body composition, reported racial differences in resting endothelial function were not observed. Additionally, there were no racial differences in postprandial endothelial function or metabolism following a high-fat meal.

Smart Walk: A Culturally Tailored Smartphone-Delivered Physical Activity Intervention for Cardiometabolic Risk Reduction among African American Women.

This article reports the results of Smart Walk: a randomized pilot trial of an 8-month culturally tailored, smartphone-delivered physical activity (PA) intervention for African American women with obesity. Sixty participants (age range = 24−49 years; BMI range = 30−58 kg/m2) were randomized to the Smart Walk intervention (n = 30) or a wellness comparison intervention (n = 30). Results supported the acceptability and feasibility of the intervention, as demonstrated by participant retention (85% at 4 months and 78% at 8 months), Smart Walk app use, and intervention satisfaction (i.e., 100% of PA participants completing the intervention [n = 24] reported they would recommend it to friend). Smart Walk participants also reported greater increases in moderate-to-vigorous PA (4-month between-arm difference in change [b] = 43.3 min/week; p = 0.018; Cohen's d = 0.69; 8-month b = 56.6 min/week; p = 0.046; d = 0.63) and demonstrated clinically relevant, although not statistically significant (p-values > 0.05), baseline to 4 months improvements in cardiorespiratory fitness (b = 1.67 mL/kg/min; d = 0.40), systolic blood pressure (b = −3.33 mmHg; d = 0.22), diastolic blood pressure (b = −4.28 mmHg; d = 0.37), and pulse wave velocity (b = −0.46 m/s; d = 0.33). Eight-month cardiometabolic outcomes followed similar trends, but had high rates of missing data (45−53%) due to COVID-19 restrictions. Collectively, findings demonstrated favorable outcomes for acceptability and feasibility, while also highlighting key areas for refinement in future research.