The 6-minute walk test (6MWT) is a simple field test that is widely used in clinical settings to assess functional exercise capacity. However, studies with healthy subjects are scarce. We hypothesized that the 6MWT might be useful to assess exercise capacity in healthy subjects. The purpose of this study was to evaluate 6MWT intensity in middle-aged and older adults, as well as to develop a simple equation to predict oxygen uptake (V˙O2) from the 6-min walk distance (6MWD). Eighty-six participants, 40 men and 46 women, 40-74 years of age and with a mean body mass index of 28±6 kg/m2, performed the 6MWT according to American Thoracic Society guidelines. Physiological responses were evaluated during the 6MWT using a K4b2 Cosmed telemetry gas analyzer. On a different occasion, the subjects performed ramp protocol cardiopulmonary exercise testing (CPET) on a treadmill. Peak V˙O2 in the 6MWT corresponded to 78±13% of the peak V˙O2 during CPET, and the maximum heart rate corresponded to 80±23% of that obtained in CPET. Peak V˙O2 in CPET was adequately predicted by the 6MWD by a linear regression equation: V˙O2 mL·min-1·kg-1 = -2.863 + (0.0563×6MWDm) (R2=0.76). The 6MWT represents a moderate-to-high intensity activity in middle-aged and older adults and proved to be useful for predicting cardiorespiratory fitness in the present study. Our results suggest that the 6MWT may also be useful in asymptomatic individuals, and its use in walk-based conditioning programs should be encouraged.

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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/.

Objective: To determine whether a restrictive pattern on spirometry is associated with the level of physical activity in daily life (PADL), as well as with cardiovascular disease (CVD) risk factors, in asymptomatic adults. Methods: A total of 374 participants (mean age, 41 ± 14 years) underwent spirometry, which included the determination of FVC and FEV1. A restrictive pattern on spirometry was defined as an FEV1/FVC ratio > 0.7 and an FVC < 80% of the predicted value. After conducting demographic, anthropometric, and CVD risk assessments, we evaluated body composition, muscle function, and postural balance, as well as performing cardiopulmonary exercise testing and administering the six-minute walk test. The PADL was quantified with a triaxial accelerometer. Results: A restrictive pattern on spirometry was found in 10% of the subjects. After multivariate logistic regression, adjusted for confounders (PADL and cardiorespiratory fitness), the following variables retained significance (OR; 95% CI) as predictors of a restrictive pattern: systemic arterial hypertension (17.5; 1.65-184.8), smoking (11.6; 1.56-87.5), physical inactivity (8.1; 1.43-46.4), larger center-of-pressure area while standing on a force platform (1.34; 1.05-1.71); and dyslipidemia (1.89; 1.12-1.98). Conclusions: A restrictive pattern on spirometry appears to be common in asymptomatic adults. We found that CVD risk factors, especially systemic arterial hypertension, smoking, and physical inactivity, were directly associated with a restrictive pattern, even when the analysis was adjusted for PADL and cardiorespiratory fitness. Longitudinal studies are needed in order to improve understanding of the etiology of a restrictive pattern as well as to aid in the design of preventive strategies.

The autonomic nervous system maintains homeostasis, which is the state of balance in the body. That balance can be determined simply and noninvasively by evaluating heart rate variability (HRV). However, independently of autonomic control of the heart, HRV can be influenced by other factors, such as respiratory parameters. Little is known about the relationship between HRV and spirometric indices. In this study, our objective was to determine whether HRV correlates with spirometric indices in adults without cardiopulmonary disease, considering the main confounders (e.g., smoking and physical inactivity). In a sample of 119 asymptomatic adults (age 20-80 years), we evaluated forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). We evaluated resting HRV indices within a 5-min window in the middle of a 10-min recording period, thereafter analyzing time and frequency domains. To evaluate daily physical activity, we instructed participants to use a triaxial accelerometer for 7 days. Physical inactivity was defined as <150 min/week of moderate to intense physical activity. We found that FVC and FEV1, respectively, correlated significantly with the following aspects of the RR interval: standard deviation of the RR intervals (r =0.31 and 0.35), low-frequency component (r =0.38 and 0.40), and Poincaré plot SD2 (r =0.34 and 0.36). Multivariate regression analysis, adjusted for age, sex, smoking, physical inactivity, and cardiovascular risk, identified the SD2 and dyslipidemia as independent predictors of FVC and FEV1 (R 2=0.125 and 0.180, respectively, for both). We conclude that pulmonary function is influenced by autonomic control of cardiovascular function, independently of the main confounders.