Exacerbations and Changes in Physical Activity and Sedentary Behaviour in Patients with Bronchiectasis after 1 Year

Background: Low physical activity and high sedentary behaviour in patients with bronchiectasis are associated with hospitalisation over one year. However, the factors associated with longitudinal changes in physical activity and sedentary behaviour have not been explored. We aimed to identify clinical and sociodemographic characteristics related to a change in physical activity and sedentary behaviour in patients with bronchiectasis after one year. Methods: This was a prospective observational study during which physical activity measurements were recorded using a SenseWear Armband for one week at baseline and at one year. At each assessment point, patients were classified as active or inactive (measured as steps per day) and as sedentary or not sedentary (measured as sedentary time). Results: 53 patients with bronchiectasis were analysed, and after one year, 18 (34%) had worse activity and sedentary levels. Specifically, 10 patients became inactive and sedentary. Multivariable analysis showed that the number of exacerbations during the follow-up period was the only outcome independently associated with change to higher inactivity and sedentary behaviour (odds ratio (OR), 2.19; 95% CI, 1.12 to 4.28). Conclusions: The number of exacerbations in patients with bronchiectasis was associated with changes in physical activity and sedentary behaviour. Exacerbation prevention may appear as a key factor in relation to physical activity and sedentary behaviour in patients with bronchiectasis.

Agreement between the SHAPES Questionnaire and a Multiple-Sensor Monitor in Assessing Physical Activity of Adolescents Using Categorial Approach: A Cross-Sectional Study

This study aimed to evaluate the agreement between a 7-day recall questionnaire and multiple-sensor monitor in identifying sufficiently active adolescents. A total of 282 students involved in the CRO-PALS study were randomly selected for a device-based measurement of physical activity (PA) using the SenseWear Armband device (SWA) no more than three weeks before or after having fulfilled the SHAPES questionnaire. Valid data was obtained from 150 participants (61 boys; 89 girls) and included in the analysis. In boys, SHAPES exhibited high specificity (92.3%), overall percent agreement (85.0%), and significant agreement (κ = 0.32, p = 0.014) with the SWA in recognising sufficiently active individuals. Conversely, no agreement was detected for quartiles of PA, although boys that were classified in the first and in the fourth quartile by SHAPES differed in device-based measured duration of MVPA (134 [95%CI: 109–160] vs. 87 [95%CI: 65–108], p = 0.032); and VPA (39 [95%CI: 23–56] vs. 14 [95%CI: 6–22], p = 0.011). In girls, no significant agreement between the two methods was found in any of the analyses. It appears that the SHAPES questionnaire is effective to identify individuals that comply with PA recommendations and to distinguish between the most active and the least active individuals for adolescent boys, but not for girls.

New strategies of physical activity assessment in cystic fibrosis: a pilot study

Background Regular physical activity (PA) is a valued part of cystic fibrosis (CF) care. Although the accelerometer, SenseWear Armband (SWA), accurately measures habitual PA in CF, it is mostly used for research purposes. For the first time, we analyzed different methods of measuring PA in daily life by the use of smartphones and other electronic devices such as smartwatch and Fitbit. Methods Twenty-four stable adults with CF (mean age 37.5 ± 11.5SD yrs.; FEV1 58 ± 19% predicted, BMI 22.9 ± 3.2) were studied. Daily PA was monitored for seven consecutive days. All patients wore the accelerometer SWA and at the same time they monitored PA with the electronic device they used routinely. They were allocated into one of four arms according to their device: Smartwatch, Fitbit, Android smartphones and iOS smartphones. PA related measurements included: duration of PA, energy expenditure, number of steps. Results There was a good agreement between SWA and Fitbit for number of steps (p = 0.605) and energy expenditure (p = 0.143). iOS smartphones were similar to SWA in monitoring the number of steps (p = 0.911). Significant differences were found between SWA and both Smartwatch and Android smartphones. Conclusions Fitbit and iOS smartphones seem to be a valuable approach to monitor daily PA. They provide a good performance to measure step number compared to SWA.

The Use of SenseWear Armband for Assessment of Daily Energy Expenditure and the Relation to Body Fat Distribution and Nutritional Intake in Lean Women with Polycystic Ovary Syndrome

Objective. To evaluate nutritional intake, energy expenditure, and segmental body composition in lean women with polycystic ovary syndrome (PCOS) and compare them with age- and body mass index- (BMI-) matched control women. Methods. 32 nonobese patients with PCOS and 31 age- and BMI-matched healthy women were included in the study. Energy expenditure and physical activity level were assessed by metabolic Holter equipment (SenseWear Armband, SWA) which was never previously used in lean PCOS population. Food intake is recorded with 24 hours of food record. Segmental body composition analysis was assessed by bioelectrical impedance analyses (BIA). Results. Mean BMI was 22.64 ± 3.64 and 21.55 ± 2.77 kg/m2 (p=0.185) in PCOS and control groups, respectively. Mean age was 22.03 ± 4.21 and 21.71 ± 2.67 year (p=0.720), respectively. No significant differences were found in total energy intake and percentage of carbohydrates, fats, and other micronutrients (p>0.05). Energy percentage of proteins (%12.73 ± 1.98, p=0.008) was statistically lower in subjects versus the control group. The measurements of physical activity duration (PAD) (1.40 ± 0.87/2.18 ± 0.99 hours, p=0.002), active energy expenditure (372.35 ± 198.32/494.10 ± 186.50 kcal, p=0.018), and step counting (9370.03 ± 3587.49/11730.90 ± 3564.31 steps, p=0.013) measurement of the PCOS group were lower than the control group, respectively. Conclusions. New diagnosed women with PCOS had similar distribution and quantity of body fat parameters and nutritional status when compared to healthy women. Control subjects were found more active in energy expenditure.

An Optimal Self-Report Physical Activity Measure for Older Adults: Does Physical Function Matter?

The authors compared two self-report measures of physical activity, the Physical Activity Scale for the Elderly (PASE) and the Community Healthy Activities Model Program for Seniors (CHAMPS), against the device-derived SenseWear Armband (SWA), to identify a recommended self-report tool to measure physical activity in older adults across physical function levels. A total of 65 community-dwelling older adults completed the PASE, CHAMPS, and seven full days of SWA wear. The authors measured physical function using the modified short physical performance battery (SPPB) and a usual-paced 6-m walk. Age- and sex-adjusted Spearman correlations showed that CHAMPS and SWA were correlated in higher functioning participants (SPPB: ρ = .33, p = .03; gait speed: ρ = .40, p = .006) and also correlated in lower functioning participants for SPPB (ρ = .70, p = .003) only. PASE and SWA were not significantly correlated across physical function. When an objective measure of physical activity is not practical, the CHAMPS questionnaire appears to capture physical activity for older adults across physical function levels.

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable.

Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

Self-reported youth physical activity (PA) is typically overestimated. We aimed to calibrate and validate a self-report tool among English youth. Four-hundred-and-two participants (aged 9–16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the self-report Youth Activity Profile (YAP) on the eighth day. Calibration algorithms for temporally matched segments were generated from the YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and student- and school-level agreement were assessed. The utility of the YAP algorithms to assess compliance to PA guidelines was also examined. The school-level bias for the YAP estimates of in-school, out-of-school, and weekend moderate-to-vigorous PA (MVPA) were 17.2 (34.4), 31.6 (14.0), and −4.9 (3.6) min·week−1, respectively. Out-of-school sedentary behaviour (SB) was over-predicted by 109.2 (11.8) min·week−1. Predicted YAP values were within 15%–20% equivalence of the SWA estimates. The classification accuracy of the YAP MVPA estimates for compliance to 60 min·day−1 and 30 min·school-day−1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. The YAP generated robust school-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with PA guidelines. The accuracy of the YAP may be further improved through research with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.