Harmonization of three different accelerometers to classify the 24 h activity cycle.

Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.

Sedentary behavior and risk of cardiovascular disease and all-cause mortality in United States adults with hypertension.

BACKGROUND: Growing evidence has implicated sedentary behavior is associated with cardiovascular and all-cause mortality, independent of moderate to vigorous physical activity (MVPA). Contrary to national physical activity guidelines, reductions in sedentary behavior are not promoted as a lifestyle modification in hypertensive adults. This may be in part because of a paucity of evidence demonstrating that sedentary behavior confers morbidity and mortality risk in hypertensive adults. PURPOSE: To examine the association between device-measured sedentary behavior and risk of cardiovascular and all-cause mortality and in hypertensive adults. METHODS: Data for this analysis come from the 2003 to 2006 National Health and Nutrition Examination Survey, a nationally representative survey of US adults. Sedentary behavior and MVPA were assessed with an ActiGraph 7164 accelerometer. Hypertension was classified as blood pressure at least 140/≥90 mmHg or antihypertensive medication use. RESULTS: Median follow-up was 14.5 years. After adjusting for covariates and MVPA, greater time spent in sedentary behavior was associated with an increased risk of cardiovascular mortality [quartile 1: REF, quartile 2: hazard ratio = 1.41 [95% confidence interval (95% CI) 0.83-2.38], quartile 3: hazard ratio = 1.25 (95% CI 0.81-1.94), quartile 4: hazard ratio = 2.14 (95% CI 1.41-3.24); P trend <0.001]. Greater sedentary behavior was also associated with an increased risk of all-cause mortality [quartile 1: REF: quartile 2: hazard ratio = 1.13 (95% CI 0.83-1.52), quartile 3: hazard ratio = 1.33 (95% CI 1.00-1.78), quartile 4: hazard ratio = 2.06 (95% CI 1.60, 2.64); P trend <0.001]. CONCLUSION: Greater sedentary behavior is associated with increased risk of cardiovascular mortality and all-cause mortality among US adults with hypertension. These findings suggest reductions in sedentary behavior should be considered to reduce mortality risk in hypertensive adults.

Agreement between Fitbit and ActiGraph Estimates of Physical Activity in Young Children.

Physical activity (PA) estimates from the Fitbit Flex 2 were compared to those from the ActiGraph GT9X Link in 123 elementary school children. Steps and intensity-specific estimates of PA and 3-month PA change were calculated using two different ActiGraph cut-points (Evenson and Romanzini). Fitbit estimates were 35% higher for steps compared to the ActiGraph. Fitbit and ActiGraph intensity-specific estimates were closest for sedentary and light PA while estimates of moderate and vigorous PA varied substantially depending upon the ActiGraph cut-points used. Spearman correlations between device estimates were higher for steps (rs=.70) than for moderate (rs =.54 to .55) or vigorous (rs =.29 to .48) PA. There was low concordance between devices in assessing PA changes over time. Agreement between Fitbit Flex 2 and ActiGraph estimates may depend upon the cut-points used to classify PA intensity. However, there is fair to good agreement between devices in ranking children's steps and MVPA.

Validity of Wearable Activity Monitors during Cycling and Resistance Exercise.

INTRODUCTION: The use of wearable activity monitors has seen rapid growth; however, the mode and intensity of exercise could affect the validity of heart rate (HR) and caloric (energy) expenditure (EE) readings. There is a lack of data regarding the validity of wearable activity monitors during graded cycling regimen and a standard resistance exercise. The present study determined the validity of eight monitors for HR compared with an ECG and seven monitors for EE compared with a metabolic analyzer during graded cycling and resistance exercise. METHODS: Fifty subjects (28 women, 22 men) completed separate trials of graded cycling and three sets of four resistance exercises at a 10-repetition-maximum load. Monitors included the following: Apple Watch Series 2, Fitbit Blaze, Fitbit Charge 2, Polar H7, Polar A360, Garmin Vivosmart HR, TomTom Touch, and Bose SoundSport Pulse (BSP) headphones. HR was recorded after each cycling intensity and after each resistance exercise set. EE was recorded after both protocols. Validity was established as having a mean absolute percent error (MAPE) value of ≤10%. RESULTS: The Polar H7 and BSP were valid during both exercise modes (cycling: MAPE = 6.87%, R = 0.79; resistance exercise: MAPE = 6.31%, R = 0.83). During cycling, the Apple Watch Series 2 revealed the greatest HR validity (MAPE = 4.14%, R = 0.80). The BSP revealed the greatest HR accuracy during resistance exercise (MAPE = 6.24%, R = 0.86). Across all devices, as exercise intensity increased, there was greater underestimation of HR. No device was valid for EE during cycling or resistance exercise. CONCLUSIONS: HR from wearable devices differed at different exercise intensities; EE estimates from wearable devices were inaccurate. Wearable devices are not medical devices, and users should be cautious when using these devices for monitoring physiological responses to exercise.