Search

Volitional physical activity level is predictive of a variety of health outcomes, but has not been examined in patients recently hospitalized for acute decompensated HF (ADHF).Ten to 14 days after index hospitalization for ADHF, 93 participants wore a wrist-mounted triaxial accelerometer (ActiGraph GT3X+) to objectively quantify sedentary behavior, light physical activity, and moderate-to-vigorous physical activity. Levels were compared to 2 groups of age-matched NHANES participants: healthy and chronic, stable HF. The relationship between physical activity levels and physical function [Short Physical Performance Battery (SPPB)], HF-specific quality-of-life (QOL) [Kansas City Cardiomyopathy Questionnaire (KCCQ)], and cognition [Montreal Cognitive Assessment (MOCA)] were examined.ADHF participants accumulated a median 1,008 (IQR 896, 1,109) minutes of sedentary time, 88 (57, 139) minutes of light physical activity, and 10 (6, 25) minutes of moderate-to-vigorous physical activity per day. Sedentary time, light physical activity, or moderate-to-vigorous activity did not differ by sex or EF subtype. ADHF participants spent only 9% of awake time nonsedentary, compared to 34% and 27% for healthy adults and adults with chronic, stable HF, respectively. Among ADHF participants, SPPB, KCCQ, and MOCA scores did not differ among quartiles of total physical activity.Older patients recently hospitalized for ADHF have very low levels of physical activity and high levels of sedentary time, both of which may be potential targets for interventions in this high-risk population. Physical activity level was not significantly associated with objectively measured physical function, QOL, or cognition, suggesting that this measure provides independent information regarding the patient experience of living with HF.NCT02196038, https://clinicaltrials.gov/ct2/show/NCT02196038.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were:2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Background/aimConsumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol.MethodsThirty men and women (18–80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80 min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn’t cycle.ResultsParticipants took 3321±571 steps, had 28±6 active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%–32% (pConclusionConsumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

Consumer-based physical activity (PA) monitors are popular for individual tracking of PA variables. However, current research has not examined how these monitors track energy expenditure (EE) and steps in distinct activities. This study examined the accuracy of the Fitbits One, Zip, and Flex and Jawbone UP24 for estimating EE and steps for specific activities and activity categories.Thirty subjects completed a structured protocol consisting of three sedentary, four household, and four ambulatory/exercise activities. All subjects began by lying on a bed for 10 min; 10 other activities were performed for 5 min each. Indirect calorimetry (COSMED) and researcher-counted steps were criterion measures for EE and step counts, respectively. The Omron HJ-720IT pedometer was used as a comparison of step count accuracy. EE and steps were compared with criterion measures using the Friedman repeated-measures nonparametric test and mean absolute percent error (MAPE).All PA monitors predicted EE within 8% of COSMED for sedentary activity but overestimated EE by 16%-40% during ambulatory activity. All monitors except the Fitbit Flex (within 8% of criterion) underestimated EE by 27%-34% during household activity. EE predictions were accompanied with MAPE10%. For household activity, the Fitbit Flex estimated steps within 10% of researcher-counted steps; all other monitors underestimated steps by 35%-64%. All monitors estimated steps within 4% of researcher-counted steps and displayed MAPE10% during ambulatory activity. The Omron underestimated household steps by 74% but was within 1% for ambulatory steps. All monitors severely underestimated EE and steps during cycling.Consumer-based PA monitors should be used cautiously for estimating EE, although they provide accurate measures of steps for structured ambulatory activity, similar to validated pedometers.