There is currently a lack of information adjacent on the influence of sex and age in heart rate variability (HRV), adjusted according to accelerometer-based physical activity (PADL). We hypothesized that the effect of sex and age on the HRV should be reduced or absent in individuals with a suitable PADL level. We aim to evaluate the influence of sex and age on HRV, adjusted for the confounding effects of the PADL level. A total of 485 age-stratified subjects (18–39, 40–59, and ≥60 years) underwent HRV analyses at rest and 7-day assessments of accelerometer-based PADL. Multivariate analyses of covariance were done using log-transformed HRV indices as outcomes, age and sex as fixed factors, and PADL, cardiovascular risk, fat body mass, and heart rate (HR) at rest as covariates. Despite the adjustment for directly measured PADL, women had better indices of vagal tone, whereas men had higher sympathetic influence. Also, compared to middle-aged and older adults, younger individuals (ages 18–39 years) presented better HRV. Multiple regression analyses confirmed that age and sex were the main predictors of HRV indices, even after adjusting for PADL directly assessed by triaxial accelerometer and HR. We also observed that the correlation between some HRV indexes and the different indexes of physical activity directly evaluated was significant, but not very consistent. Thus, HRV indices are influenced by age and sex, regardless of accelerometer-based physical activity. Interventions with physical activity and exercise aimed at improving the autonomic modulation of asymptomatic adults should take such differences into account.

Background The sequential multiple assignment randomized trial (SMART) design allows for changes in the intervention during the trial period. Despite its potential and feasibility for defining the best sequence of interventions, so far, it has not been utilized in a smartphone/gamified intervention for physical activity. Objective We aimed to investigate the feasibility of the SMART design for assessing the effects of a smartphone app intervention to improve physical activity in adults. We also aimed to describe the participants’ perception regarding the protocol and the use of the app for physical activity qualitatively. Methods We conducted a feasibility 24-week/two-stage SMART in which 18 insufficiently active participants (<10,000 steps/day) were first randomized to group 1 (smartphone app only), group 2 (smartphone app + tailored messages), and a control group (usual routine during the protocol). Participants were motivated to increase their step count by at least 2000 steps/day each week. Based on the 12-week intermediate outcome, responders continued the intervention and nonresponders were rerandomized to subsequent treatment, including a new group 3 (smartphone app + tailored messages + gamification) in which they were instructed to form groups to use several game elements available in the chosen app (Pacer). We considered responders as those with any positive slope in the linear relationship between weeks and steps per day at the end of the first stage of the intervention. We compared the accelerometer-based steps per day before and after the intervention, as well as the slopes of the app-based steps per day between the first and second stages of the intervention. Results Twelve participants, including five controls, finished the intervention. We identified two responders in group 1. We did not observe relevant changes in the steps per day either throughout the intervention or compared with the control group. However, the rerandomization of five nonresponders led to a change in the slope of the steps per day (median −198 steps/day [IQR −279 to −103] to 20 steps/day [IQR −204 to 145]; P=.08). Finally, in three participants from group 2, we observed an increase in the number of steps per day up to the sixth week, followed by an inflection to baseline values or even lower (ie, a quadratic relationship). The qualitative analysis showed that participants’ reports could be classified into the following: (1) difficulty in managing the app and technology or problems with the device, (2) suitable response to the app, and (3) difficulties to achieve the goals. Conclusions The SMART design was feasible and changed the behavior of steps per day after rerandomization. Rerandomization should be implemented earlier to take advantage of tailored messages. Additionally, difficulties with technology and realistic and individualized goals should be considered in interventions for physical activity using smartphones. Trial Registration Brazilian Registry of Clinical Trials RBR-8xtc9c; http://www.ensaiosclinicos.gov.br/rg/RBR-8xtc9c/.

Purpose Obese individuals have reduced performance in cardiopulmonary exercise testing (CPET), mainly considering peak values of variables such as oxygen uptake (V˙O2), carbon dioxide production (V˙CO2), tidal volume (Vt), minute ventilation (V˙E) and heart rate (HR). The CPET interpretation and prognostic value can be improved through submaximal ratios analysis of key variables like ΔHR/ΔV˙O2, ΔV˙E/ΔV˙CO2, ΔV˙C/Δlinearized (ln)V˙E and oxygen uptake efficiency slope (OUES). The obesity influence on these responses has not yet been investigated. Our purpose was to evaluate the influence of adulthood obesity on maximal and submaximal physiological responses during CPET, emphasizing the analysis of submaximal dynamic variables. Methods We analyzed 1,594 CPETs of adults (755 obese participants, Body Mass Index ≥ 30 kg/m2) and compared the obtained variables among non-obese (normal weight and overweight) and obese groups (obesity classes I, II and III) through multivariate covariance analyses. Result Obesity influenced the majority of evaluated maximal and submaximal responses with worsened CPET performance. Cardiovascular, metabolic and gas exchange variables were the most influenced by obesity. Other maximal and submaximal responses were altered only in morbidly obese. Only a few cardiovascular and ventilatory variables presented inconsistent results. Additionally, Vtmax, Vt/V˙E, Vt/Inspiratory Capacity, Vt/Forced Vital Capacity, Lowest V˙E/V˙CO2, ΔV˙E/ΔV˙CO2, and the y-intercepts of V˙E/V˙CO2 did not significantly differ regardless of obesity. Conclusion Obesity expressively influences the majority of CPET variables. However, the prognostic values of the main ventilatory efficiency responses remain unchanged. These dynamic responses are not dependent on maximum effort and may be useful in detecting incipient ventilatory disorder. Our results present great practical applicability in identifying exercise limitation, regardless of overweight and obesity.

BackgroundThere is scientific evidence suggesting that app-based interventions targeted to increase the level of physical activity might be effective, although multicomponent interventions appear to be more effective than app-based interventions alone. Despite the motivating results, it remains unclear whether or not app-based interventions can increase the level of physical activity and cardiovascular health. Our study aims to investigate the effect of a smartphone app combined with gamification on the level of physical activity of adults and older adults. The specific aims are (1) to verify the effects of the intervention on cardiometabolic and cardiovascular health, lung function, and cardiorespiratory fitness; and (2) to verify the relationship between age group and the response rate.Methods/designWe will conduct a sequential multiple assignment randomized trial (SMART). The adaptive intervention protocol will last 6 months. After baseline assessments, participants will be randomized into one of three groups (group 1: app + tailored messages; group 2: app + tailored messages + gamification I; control group: physical activity counseling). For 12 weeks, we will record the average number of steps per day of participants from groups 1 and 2. At 6 weeks from initiation of recording, participants will be classified into responders and non-responders according to their increase in the average number of daily steps; all those considered as non-responders will be re-randomized, with the chance to participate in a third group – group 3: app + tailored messages + gamification II. Finally, at 12 weeks, participants will continue using the app but will no longer receive direct intervention from investigators. All participants will be reassessed at 3 and 6 months from baseline. Our pilot SMART will require 42 participants (14 per arm). Following the SMART pilot, we will calculate the sample size for the trial based on the variation of the average number of steps/day, including an up to 40% loss to follow-up and a less optimistic nonresponse rate of 65%.DiscussionTo our knowledge, this will be the first trial with adaptive intervention to test the effectiveness of using a smartphone app to increase the level of physical activity of adults and older adults.Trial registrationBrazilian Clinical Trials Registry: RBR-8xtc9c. Registered on 3 August 2018, http://www.ensaiosclinicos.gov.br; UTN number: U1111–1218-1092.

Background: The handgrip strength is a practical, valid, reliable, low-cost tool that presents strong correlations with several health conditions. However, handgrip strength may be inaccurate to prospectively predict the variability of muscular function since the decrease in muscular strength over the years varies according to a muscular group or between upper and lower limbs. Our hypothesis is that the handgrip strength cannot explain the variance of muscle function prospectively. Purpose: The aim of this study was to evaluate the cross-sectional and prospective association between handgrip strength and isokinetic muscle function of the knee and elbow in 780 asymptomatic adults. Methods: In a sample of 780 adults, we obtained handgrip strength and elbow and knee muscle function (for both flexion and extension at 60°/s and 300°/s) using, respectively, a hydraulic dynamometer and an isokinetic dynamometer. In a cross-sectional analysis, we analyzed the data obtained from baseline assessment. Then, we calculated the absolute change as a result of the variation data between the baseline and the 1-year follow-up assessment of each participant. The correlations were analyzed using Pearson or Spearman coefficients. We used multivariate models to investigate the association between handgrip strength and isokinetic muscle function. Results and Discussion: The cross-sectional correlations were significantly moderate-to-strong ( r = 0.41–0.71, p < 0.01), but became weak-to-moderate ( r = 0.26–0.34, p < 0.01) prospectively. In the cross-sectional analysis, the handgrip strength was selected as a strong predictor for isokinetic variables (∆ R2 = 0.171–0.583, p < 0.05) as expected. Although handgrip strength was also selected as a significant predictor in prospective analysis, it explained only a little variance in isokinetic muscle function of the knee (∆ R2 = 0.7–0.117, p < 0.05). Regarding the predictive models for the elbow, handgrip strength was not selected prospectively. Conclusion: The 1-year absolute change of the handgrip strength cannot explain the variance of the isokinetic muscle function. Thus, specific measures are required for assessing muscle function in epidemiological studies.

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We aimed to develop an equation to predict peak VO in obese subjects undergoing CPET. In addition, we evaluated and compared three published equations. We randomized 346 obese subjects undergoing CPET into a group for developing the equation (n = 272) and a group for cross-validation (n = 74), compared through the Bland and Altman method. Height, sex and age were responsible for 85·5% of total variability of the peak VO2. Additional 1% and 0·7% of the variability were, respectively, explained by physical inactivity and diabetes. The equation devised was as follows: peakVO2mlmin-1=-677·8+(2135·9×heightm)+(706·8×sexmales=1;females=0)-(15·5×ageyears)-(161·1×physicalinactivityyes=1;no=0)-(176·3×diabetesyes=1;no=0). The mean difference between the estimated and measured peak VO was 7 ml min , with a 23·9% bias. Published equations overestimated the peak VO2 by 35·3%, 49·1% and 46·2% bias. The equation developed in this study performed better in predicting peak VO in obese adults improving ramp protocol design and CRF evaluations in obese subjects.

Although sedentary behavior (SB) is related to the development of metabolic diseases, there is still no consensus in literature about the association between accelerometer-based SB and obesity, especially adjusted for cardiovascular risk factors and level of daily physical activities. The aim was to evaluate the association between obesity and SB adjusted for potential confounders in adults. Data from 780 participants of the Epidemiology and Human Movement (EPIMOV) Study were analyzed. Body weight, body mass index (BMI), and fat body mass as percentage (%FBM) (bioelectrical impedance) were obtained and, then, used to stratify participants. SB was objectively measured using triaxial waist-worn accelerometers placed above the dominant hip during waking hours for at least four consecutive days (4-7 days). SB and its pattern were not significantly different between obesity groups. Although SB presented some significant correlations with obesity, the correlation and determination coefficient indicated weak association between SB and obesity (e.g., BMI and %FBM). Obesity presented little or no association with SB and its pattern after adjustment for potential confounders, especially when SB is measured through accelerometry.