Translational journal of the American College of Sports Medicine最新文献

Introduction/purpose: Sedentary behavior (SB) is common in desk-based work and prolonged periods of SB are associated with negative health outcomes. This study assessed associations between workplace characteristics and setting and movement patterns during working hours.

Methods: This secondary analysis used baseline data from the Reducing Sedentary Behavior to Decrease Blood Pressure (RESET BP) clinical trial which enrolled inactive, desk-based workers with elevated blood pressure (n=271; mean age: 45.3±11.6 years; body mass index (BMI): 30.66±7.1 kg/m²; 59.4% women). Physical and social workplace characteristics were assessed by a study-developed questionnaire and the Office Environment and Sitting Scale (OFFESS). Participants also wore an activPAL activity monitor for 7 days and reported working hours in a diary to measure SB and physical activity (PA) specifically while working. Linear regression was used to analyze cross-sectional associations between workplace characteristics and SB and PA. A stratified analysis was also conducted to assess associations among home-based and in-office desk workers separately. Analyses were adjusted for age, gender, BMI, and work wear time.

Results: Participants spent 77% of working hours in SB. Public vs. private offices, working in-office vs. at home, higher local connectivity, and greater overall connectedness were associated with lower SB and/or greater PA (all p<0.05). Higher frequency of face-to-face interactions, and greater visibility and proximity to co-workers was associated with less SB and more PA (all p<0.05). For example, home-based workers had more total SB (+17.2±8.4 mins/day), more SB bouts ≥30 mins (+39.1±12.8 mins/day), and less steps (695±201 steps/day) than in-office employees. Stratification by office setting revealed differences in associations between SB and PA and workplace characteristics.

Conclusions: More public, open spaces with more social interactions and physical walkways could improve SB and PA patterns during work. Home-based workers had more SB, less PA, and unique associations of these activities with workplace characteristics, suggesting a need for tailored interventions.

Purpose: To identify baseline demographic, clinical, and psychosocial predictors of exercise intervention adherence in the Studies of a Targeted Risk Reduction Intervention through Defined Exercise (STRRIDE) trials.

Methods: A total of 947 adults with dyslipidemia or prediabetes were enrolled into an inactive control group or one of ten exercise interventions with doses of 10-23 kcal/kg/week, intensities of 40-80% of peak oxygen consumption, and training for 6-8-months. Two groups included resistance training. Mean percent aerobic and resistance adherence were calculated as the amount completed divided by the prescribed weekly minutes or total sets of exercise times 100, respectively. Thirty-eight clinical, demographic, and psychosocial measures were considered for three separate models: 1) clinical + demographic factors, 2) psychosocial factors, and 3) all measures. A backward bootstrapped variable selection algorithm and multiple regressions were performed for each model.

Results: In the clinical and demographic measures model (n=947), variables explained 16.7% of the variance in adherence (p<0.001); lesser fasting glucose explained the greatest amount of variance (partial R² = 3.2%). In the psychosocial factors model (n=561), variables explained 19.3% of the variance in adherence (p<0.001); greater 36-Item Short Form Health Survey (SF-36) physical component score explained the greatest amount of variance (partial R² = 8.7%). In the model with all clinical, demographic, and psychosocial measures (n=561), variables explained 22.1% of the variance (p<0.001); greater SF-36 physical component score explained the greatest amount of variance (partial R² = 8.9%). SF-36 physical component score was the only variable to account for >5% of the variance in adherence in any of the models.

Conclusions: Baseline demographic, clinical, and psychosocial variables explain approximately 22% of the variance in exercise adherence. The limited variance explained suggests future research should investigate additional measures to better identify participants who are at risk for poor exercise intervention adherence.

Introduction/purpose: Weight maintenance following intentional weight loss is challenging and often unsuccessful. Physical activity and self-monitoring are strategies associated with successful weight loss maintenance. However, less is known about the type and number of lifestyle strategies used following intentional weight loss. The purpose of this study was to determine the types and amounts of strategies associated with successful long-term weight loss maintenance.

Methods: Data from the 24-month Maintaining Activity and Nutrition Through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) trial were analyzed. MAINTAIN-pc recruited adults (n=194; 53.4±12.2 years of age, body mass index (BMI): 30.4±5.9 kg/m², 74% female) with recent intentional weight loss of ≥5%, randomized to tracking tools plus coaching (i.e., coaching group) or tracking tools without coaching (i.e., tracking-only group). At baseline, 6, 12, and 24 months, participants reported lifestyle strategies used in the past 6 months, including self-monitoring, group support, behavioral skills, and professional support. General linear models evaluated changes in the number of strategies over time between groups and the consistency of strategies used over the 24-month intervention.

Results: At baseline, 100% used behavioral skills, 73% used group support, 69% used self-monitoring, and 68% used professional support in the past 6 months; at 24 months, these rates were 98%, 60%, 75%, and 61%, respectively. While the number of participants utilizing individual strategies did not change significantly over time, the overall number of strategies participants reported decreased. More strategies were used at baseline and 6 months compared to 12- and 24-month follow-ups. The coaching group used more strategies at months 6 and 12 than the tracking-only group. Consistent use of professional support strategies over the 24-month study period was associated with less weight regain.

Conclusion: Weight loss maintenance interventions that incorporate continued follow-up and support from healthcare professionals are likely to prevent weight regain after intentional weight loss.

Purpose: This study aimed to characterize the timing and self-reported determinants of exercise dropout among sedentary adults with overweight or obesity. We also sought to explore variations in adherence among individuals who completed a 6- to 8-month structured exercise intervention.

Methods: A total of 947 adults with dyslipidemia [STRRIDE I, STRRIDE AT/RT] or prediabetes [STRRIDE-PD] were enrolled to either control or to one of 10 exercise interventions, ranging from doses of 8-23 kcal/kg/week; intensities of 50%-75% V̇O2 peak; and durations of 6-8 months. Two groups included resistance training and one included dietary intervention (7% weight loss goal). Dropout was defined as an individual who withdrew from the study due a variety of determinants. Timing of intervention dropout was defined as the last session attended and categorized into phases. Exercise training adherence was calculated by dividing weekly minutes or total sets of exercise completed by weekly minutes or total sets of exercise prescribed. General linear models were used to characterize the associations between timing of dropout and determinant category.

Results: Compared to exercise intervention completers (n=652), participants who dropped out (n=295) were on average non-white (98% vs. 80%, p<0.01), had higher body mass index (31.0 kg/m² vs. 30.2 kg/m²; p<0.01), and were less fit at baseline (25.0 mg/kg/min vs. 26.7 ml/kg/min, p<0.01). Of those who dropped out, 67% did so prior to the start of or while ramping up to the prescribed exercise volume and intensity. The most commonly reported reason for dropout was lack of time (40%). Notably, among individuals who completed the ramp training period, subsequent exercise intervention adherence did not waiver over the ensuing 6-8 months of training.

Conclusion: These findings are some of the first to delineate associations between the timing of dropout and dropout determinants, providing guidance to future exercise interventions to better support individuals at-risk for dropout.

Introduction/purpose: Dietary restriction (DIET) and aerobic exercise (AEX) interventions may impact energy balance differently. Our aim was to describe the effects of weight loss interventions via DIET or AEX on measures of energy balance.

Methods: Adults with overweight or obesity were randomized to 12 weeks of DIET or AEX with similar calorie deficit goals. A study day was conducted before and after the intervention to assess subjective and hormonal (ghrelin, peptide-YY, glucagon-like peptide-1) appetite responses to a control meal, ad libitum energy intake (EI) at a single meal, and over three days of free-living conditions and eating behavior traits. Resting metabolic rate (RMR) was measured with indirect calorimetry and adjusted for body composition measured by dual X-ray absorptiometry. Non-exercise activity was measured using accelerometers.

Results: Forty-four individuals were included (age: 37 ± 9 years, body mass index: 30.6 ± 3.1 kg/m²). Both interventions resulted in weight and fat mass loss. The DIET group lost fat-free mass, although differences between groups were not significant (DIET: -1.2 ± 1.7 kg, p<0.001; AEX: 0.4 ± 1.5 kg, p=0.186; p=0.095 interaction). There were no differences in RMR after body composition adjustment. Both interventions were associated with an increase in dietary restraint (DIET: 4.9 ± 1.2, AEX: 2.8 ± 0.7; p<0.001 in both groups). Hunger decreased with DIET (-1.4 ± 0.5, p=0.003), and disinhibition decreased with AEX (-1.5 ± 0.5, p<0.001), although these changes were not different between groups (i.e., no group × time interaction). No other differences in appetite, EI, or non-exercise physical activity were observed within or between groups.

Conclusions: AEX did not result in compensatory alterations in appetite, ad libitum EI, or physical activity, despite assumed increased energy expenditure. Modest evidence also suggested that disinhibition and hunger may be differentially impacted by weight loss modality.