Cadence (steps/min) and relative intensity in 61 to 85-year-olds: the CADENCE-Adults study.

BACKGROUND: We previously demonstrated that a heuristic (i.e., evidence-based, rounded yet practical) cadence threshold of ≥ 100 steps/min was associated with absolutely-defined moderate intensity physical activity (i.e., ≥ 3 metabolic equivalents [METs]) in older adults 61-85 years of age. Although it was difficult to ascertain achievement of absolutely-defined vigorous (6 METs) intensity, ≥ 130 steps/min was identified as a defensible threshold for this population. However, little evidence exists regarding cadence thresholds and relatively-defined moderate intensity indicators, including ≥ 64% heart rate [HR] maximum [HRmax = 220-age], ≥ 40% HR reserve [HRR = HRmax-HRresting], and ≥ 12 Borg Scale Rating of Perceived Exertion [RPE]; or vigorous intensity indicators including ≥ 77%HRmax, ≥ 60%HRR, and ≥ 14 RPE. PURPOSE: To analyze the relationship between cadence and relatively-defined physical activity intensity and identify relatively-defined moderate and vigorous heuristic cadence thresholds for older adults 61-85 years of age. METHODS: Ninety-seven ostensibly healthy adults (72.7 ± 6.9 years; 49.5% women) completed up to nine 5-min treadmill walking bouts beginning at 0.5 mph (0.8 km/h) and progressing by 0.5 mph speed increments (with 2-min rest between bouts). Directly-observed (and video-recorded) steps were hand-counted, HR was measured using a chest-strapped monitor, and in the final minute of each bout, participants self-reported RPE. Segmented mixed model regression and Receiver Operating Characteristic (ROC) curve analyses identified optimal cadence thresholds associated with relatively-defined moderate (≥ 64%HRmax, ≥ 40%HRR, and ≥ 12 RPE) and vigorous (≥ 77%HRmax, ≥ 60%HRR, and ≥ 14 RPE) intensities. A compromise between the two analytical methods, including Youden's Index (a sum of sensitivity and specificity), positive and negative predictive values, and overall accuracy, yielded final heuristic cadences. RESULTS: Across all relatively-defined moderate intensity indicators, segmented regression models and ROC curve analyses identified optimal cadence thresholds ranging from 105.9 to 112.8 steps/min and 102.0-104.3 steps/min, respectively. Comparable values for vigorous intensity indicators ranged between126.1-132.1 steps/min and 106.7-116.0 steps/min, respectively. Regardless of the relatively-defined intensity indicator, the overall best heuristic cadence threshold aligned with moderate intensity was ≥ 105 steps/min. Vigorous intensity varied between ≥ 115 (greater sensitivity) or ≥ 120 (greater specificity) steps/min. CONCLUSIONS: Heuristic cadence thresholds align with relatively-defined intensity indicators and can be useful for studying and prescribing older adults' physiological response to, and/or perceived experience of, ambulatory physical activity. TRIAL REGISTRATION: Clinicaltrials.gov NCT02650258. Registered 24 December 2015.

Cadence (steps/min) as an indicator of the walk-to-run transition.

BACKGROUND: Humans naturally transition from walking to running at a point known as the walk-to-run transition (WRT). The WRT commonly occurs at a speed of ∼2.1 m/s (m/s) or a Froude number (dimensionless value considering leg length) of 0.5. Emerging evidence suggests the WRT can also be classified using a cadence of 140 steps/min. An accurate cadence-based WRT metric would aid in classifying wearable technology minute-level step metrics as walking vs. running. PURPOSE: To evaluate performance of 1) WRT predictors directly identified from a treadmill-based dataset of sequentially faster bouts, and 2) accepted WRT predictors compiled from previous literature. METHODS: Twenty-eight adults (71.4% men; age = 36.6 ± 12.8 years, BMI = 26.2 ± 4.7 kg/m2) completed a series of five-minute treadmill walking bouts increasing in 0.2 m/s increments until they freely chose to run. Optimal WRT values for speed, Froude number, and cadence were identified using receiver operating characteristic (ROC) curve analyses. WRT value performance was evaluated via classification accuracy metrics. RESULTS: Overall accuracies (metric, percent) according to WRT predictors from previous literature were: speed (2.1 m/s, 55.0%), Froude number (0.5, 76.8%), and cadence (140 steps/min, 91.1%), and those from the dataset herein were: speed (1.9 and 2.0 m/s, 78.6%), Froude number (0.68, 77.3%), and cadence (134, 139, and 141 steps/min, 92.9%). The three equally accurate cadence values support a heuristic range of cadence-based WRT values in young and middle-aged adults: 135-140 steps/min. SIGNIFICANCE: A tight range of cadence values performed better as WRT predictors compared to either previously reported or directly identified speed or Froude number values. These findings have important implications for gait classification, especially considering cadence is a simple metric which can be readily assessed across settings using direct observation or wearable technologies.

Update and Novel Validation of a Pregnancy Physical Activity Questionnaire.

The aim of this study was to update and validate the Pregnancy Physical Activity Questionnaire (PPAQ), using novel and innovative accelerometer and wearable camera measures in a free-living setting, to improve the measurement performance of this method for self-reporting physical activity. A prospective cohort of 50 eligible pregnant women were enrolled in early pregnancy (mean = 14.9 weeks' gestation). In early, middle, and late pregnancy, participants completed the updated PPAQ and, for 7 days, wore an accelerometer (GT3X-BT; ActiGraph, Pensacola, Florida) on the nondominant wrist and a wearable camera (Autographer; OMG Life (defunct)). At the end of the 7-day period, participants repeated the PPAQ. Spearman correlations between the PPAQ and accelerometer data ranged from 0.37 to 0.44 for total activity, 0.17 to 0.53 for moderate- to vigorous-intensity activity, 0.19 to 0.42 for light-intensity activity, and 0.23 to 0.45 for sedentary behavior. Spearman correlations between the PPAQ and wearable camera data ranged from 0.52 to 0.70 for sports/exercise and from 0.26 to 0.30 for transportation activity. Reproducibility scores ranged from 0.70 to 0.92 for moderate- to vigorous-intensity activity and from 0.79 to 0.91 for sports/exercise, and were comparable across other domains of physical activity. The PPAQ is a reliable instrument and a valid measure of a broad range of physical activities during pregnancy.

Cadence-based classification of moderate-intensity overground walking in 41- to 85-year-old adults.

BACKGROUND: Walking cadence (steps/min) has emerged as a valid proxy of physical activity intensity, with consensus across numerous laboratory-based treadmill studies that ≥100 steps/min approximates absolutely defined moderate intensity (≥3 metabolic equivalents; METs). We recently reported that this cadence threshold had a classification accuracy of 73.3% for identifying moderate intensity during preferred pace overground walking in young adults. The purpose of this study was to evaluate and compare the performance of a cadence threshold of ≥100 steps/min for correctly classifying moderate intensity during overground walking in middle- and older-aged adults. METHODS: Participants (N = 174, 48.3% female, 41-85 years of age) completed laboratory-based cross-sectional study involving an indoor 5-min overground walking trial at their preferred pace. Steps were manually counted and converted to cadence (total steps/5 min). Intensity was measured using indirect calorimetry and expressed as METs. Classification accuracy (sensitivity, specificity, accuracy) of a cadence threshold of ≥100 steps/min to identify individuals walking at ≥3 METs was calculated. RESULTS: The ≥100 steps/min threshold demonstrated accuracy of 74.7% for classifying moderate intensity. When comparing middle- vs. older-aged adults, similar accuracy (73.4% vs. 75.8%, respectively) and specificity (33.3% vs. 34.5%) were observed. Sensitivity was high, but was lower for middle- vs. older-aged adults (85.2% vs. 93.9%, respectively). CONCLUSION: A cadence threshold of ≥100 steps/min accurately identified moderate-intensity overground walking. Furthermore, accuracy was similar when comparing middle- and older-aged adults. These findings extend our previous analysis in younger adults and confirm the appropriateness of applying this cadence threshold across the adult lifespan.

The impact of the COVID-19 pandemic on physical activity and sedentary behavior during pregnancy: a prospective study.

BACKGROUND: Prior studies evaluating the impact of the COVID-19 pandemic on pregnancy physical activity (PA) have largely been limited to internet-based surveys not validated for use in pregnancy. METHODS: This study used data from the Pregnancy PA Questionnaire Validation study conducted from 2019-2021. A prospective cohort of 50 pregnant women completed the Pregnancy PA Questionnaire (PPAQ), validated for use in pregnancy, in early, mid, and late pregnancy and wore an ActiGraph GT3X-BT for seven days. COVID-19 impact was defined using a fixed date of onset (March 13, 2020) and a self-reported date. Multivariable linear mixed effects regression models adjusted for age, early pregnancy BMI, gestational age, and parity. RESULTS: Higher sedentary behavior (14.2 MET-hrs/wk, 95% CI: 2.3, 26.0) and household/caregiving PA (34.4 MET-hrs/wk, 95% CI: 8.5, 60.3 and 25.9 MET-hrs/wk, 95% CI: 0.9, 50.9) and lower locomotion (-8.0 h/wk, 95% CI: -15.7, -0.3) and occupational PA (-34.5 MET-hrs/wk, 95% CI: -61.9, -7.0 and -30.6 MET-hrs/wk, 95% CI: -51.4, -9.8) was observed in middle and late pregnancy, respectively, after COVID-19 vs. before. There was no impact on steps/day or meeting American College of Obstetricians and Gynecologists guidelines. CONCLUSIONS: Proactive approaches for the promotion of pregnancy PA during pandemic-related restrictions are critically needed.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study.

BACKGROUND: Standardized validation indices (i.e., accuracy, bias, and precision) provide a comprehensive comparison of step counting wearable technologies. PURPOSE: To expand a previously published child/youth catalog of validity indices to include adults (21-40, 41-60 and 61-85 years of age) assessed across a range of treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]) and device wear locations (ankle, thigh, waist, and wrist). METHODS: Two hundred fifty-eight adults (52.5 ± 18.7 years, 49.6% female) participated in this laboratory-based study and performed a series of 5-min treadmill bouts while wearing multiple devices; 21 devices in total were evaluated over the course of this multi-year cross-sectional study (2015-2019). The criterion measure was directly observed steps. Computed validity indices included accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV). RESULTS: Over the range of normal speeds, 15 devices (Actical, waist-worn ActiGraph GT9X, activPAL, Apple Watch Series 1, Fitbit Ionic, Fitbit One, Fitbit Zip, Garmin vivoactive 3, Garmin vivofit 3, waist-worn GENEActiv, NL-1000, PiezoRx, Samsung Gear Fit2, Samsung Gear Fit2 Pro, and StepWatch) performed at < 5% MAPE. The wrist-worn ActiGraph GT9X displayed the worst accuracy across normal speeds (MAPE = 52%). On average, accuracy was compromised across slow walking speeds for all wearable technologies (MAPE = 40%) while all performed best across normal speeds (MAPE = 7%). When analyzing the data by wear locations, the ankle and thigh demonstrated the best accuracy (both MAPE = 1%), followed by the waist (3%) and the wrist (15%) across normal speeds. There were significant effects of speed, wear location, and age group on accuracy and bias (both p < 0.001) and precision (p ≤ 0.045). CONCLUSIONS: Standardized validation indices cataloged by speed, wear location, and age group across the adult lifespan facilitate selecting, evaluating, or comparing performance of step counting wearable technologies. Speed, wear location, and age displayed a significant effect on accuracy, bias, and precision. Overall, reduced performance was associated with very slow walking speeds (0.8 to 3.2 km/h). Ankle- and thigh-located devices logged the highest accuracy, while those located at the wrist reported the worst accuracy. TRIAL REGISTRATION: Clinicaltrials.gov NCT02650258. Registered 24 December 2015.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography.

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Higher 24-h Total Movement Activity Percentile Is Associated with Better Cognitive Performance in U.S. Older Adults.

PURPOSE: This study aimed to assess the association of a wrist-worn, device-based metric of 24-h movement with cognitive function and subjective cognitive complaints among older adults, 60 yr and older. METHODS: This is a cross-sectional analysis of the 2011-2012 and 2013-2014 National Health and Nutrition Examination Survey (NHANES) cycles. A wrist-worn ActiGraph GT3X+ accelerometer captured total 24-h movement activity, analyzed as Monitor-Independent Movement Summary units (MIMS-units), and quantified into placement based on an age- and sex-standardized percentile. Cognitive tests in the domains of memory, language/verbal fluency, and executive performance were administered. Test-specific cognitive z -scores were generated. Subjective cognitive complaints included perceived difficulty remembering and confusion/memory loss. RESULTS: The analytical sample included 2708 U.S. older adults (69.5 ± 0.2 yr, 55% female, 20.9% non-White). Multivariable linear regressions revealed those in quartiles 3 (50th-74th percentile) and 4 (≥75th percentile) for their age and sex had higher cognitive function z -scores across all domains compared with those in quartile 1. Logistic regressions demonstrated those in quartiles 3 and 4 also had lower odds of reporting difficulty remembering (adjusted odds ratio [AOR] = 0.52, 95% confidence interval [CI] = 0.31-0.89; AOR = 0.57, 95% CI = 0.37-0.88) and confusion/memory loss (AOR = 0.49, 95% CI = 0.27-0.91; AOR = 0.49, 95% CI = 0.27-0.98), respectively, compared with those in quartile 1. CONCLUSIONS: In a representative sample of U.S. older adults, higher cognitive functioning occurs among those that perform total 24-h movement activity at or above the 50th percentile for their age and sex. Future studies should consider movement behaviors across a 24-h period on cognitive health outcomes in older adults. More research exploring prospective associations of MIMS-units and time-use behaviors across midlife and older adulthood that may affect cognitive functioning across diverse populations is needed.

Walking cadence (steps/min) and intensity in 61-85-year-old adults: the CADENCE-Adults study.

BACKGROUND: Heuristic (i.e., evidence-based, rounded) cadences of ≥100 and ≥ 130 steps/min have consistently corresponded with absolutely-defined moderate (3 metabolic equivalents [METs]) and vigorous (6 METs) physical activity intensity, respectively, in adults 21-60 years of age. There is no consensus regarding similar thresholds in older adults. PURPOSE: To provide heuristic cadence thresholds for 3, 4, 5, and 6 METs in 61-85-year-old adults. METHODS: Ninety-eight community-dwelling ambulatory and ostensibly healthy older adults (age = 72.6 ± 6.9 years; 49% women) walked on a treadmill for a series of 5-min bouts (beginning at 0.5 mph with 0.5 mph increments) in this laboratory-based cross-sectional study until: 1) transitioning to running, 2) reaching ≥75% of their age-predicted maximum heart rate, or 3) reporting a Borg rating of perceived exertion > 13. Cadence was directly observed and hand-tallied. Intensity (oxygen uptake [VO2] mL/kg/min) was assessed with indirect calorimetry and converted to METs (1 MET = 3.5 mL/kg/min). Cadence thresholds were identified via segmented mixed effects model regression and using Receiver Operating Characteristic (ROC) curves. Final heuristic cadence thresholds represented an analytical compromise based on classification accuracy (sensitivity, specificity, positive and negative predictive value, and overall accuracy). RESULTS: Cadences of 103.1 (95% Prediction Interval: 70.0-114.2), 116.4 (105.3-127.4), 129.6 (118.6-140.7), and 142.9 steps/min (131.8-148.4) were identified for 3, 4, 5, and 6 METs, respectively, based on the segmented regression. Comparable values based on ROC analysis were 100.3 (95% Confidence Intervals: 95.7-103.1), 111.5 (106.1-112.9), 116.0 (112.4-120.2), and 128.6 steps/min (128.3-136.4). Heuristic cadence thresholds of 100, 110, and 120 were associated with 3, 4, and 5 METs. Data to inform a threshold for ≥6 METs was limited, as only 6/98 (6.0%) participants achieved this intensity. CONCLUSIONS: Consistent with previous data collected from 21-40 and 41-60-year-old adults, heuristic cadence thresholds of 100, 110, and 120 steps/min were associated with 3, 4, and 5 METs, respectively, in 61-85-year-old adults. Most older adults tested did not achieve the intensity of ≥6 METs; therefore, our data do not support establishing thresholds corresponding with this intensity level. TRIAL REGISTRATION: Clinicaltrials.gov NCT02650258 . Registered 24 December 2015.

Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions.

PURPOSE: To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications. METHODS: Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification. RESULTS: 43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively. CONCLUSION: WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.

Accelerometer Calibration: The Importance of Considering Functionality.

PURPOSE: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions. METHODS: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson's disease (n = 17), and stroke (n =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit. RESULTS: All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition. CONCLUSION: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-Kids study.

BACKGROUND: Wearable technologies play an important role in measuring physical activity (PA) and promoting health. Standardized validation indices (i.e., accuracy, bias, and precision) compare performance of step counting wearable technologies in young people. PURPOSE: To produce a catalog of validity indices for step counting wearable technologies assessed during different treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]), wear locations (waist, wrist/arm, thigh, and ankle), and age groups (children, 6-12 years; adolescents, 13-17 years; young adults, 18-20 years). METHODS: One hundred seventeen individuals (13.1 ± 4.2 years, 50.4% female) participated in this cross-sectional study and completed 5-min treadmill bouts (0.8 km/h to 8.0 km/h) while wearing eight devices (Waist: Actical, ActiGraph GT3X+, NL-1000, SW-200; Wrist: ActiGraph GT3X+; Arm: SenseWear; Thigh: activPAL; Ankle: StepWatch). Directly observed steps served as the criterion measure. Accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV) were computed. RESULTS: Five of the eight tested wearable technologies (i.e., Actical, waist-worn ActiGraph GT3X+, activPAL, StepWatch, and SW-200) performed at < 5% MAPE over the range of normal speeds. More generally, waist (MAPE = 4%), thigh (4%) and ankle (5%) locations displayed higher accuracy than the wrist location (23%) at normal speeds. On average, all wearable technologies displayed the lowest accuracy across slow speeds (MAPE = 50.1 ± 35.5%), and the highest accuracy across normal speeds (MAPE = 15.9 ± 21.7%). Speed and wear location had a significant effect on accuracy and bias (P < 0.001), but not on precision (P > 0.05). Age did not have any effect (P > 0.05). CONCLUSIONS: Standardized validation indices focused on accuracy, bias, and precision were cataloged by speed, wear location, and age group to serve as important reference points when selecting and/or evaluating device performance in young people moving forward. Reduced performance can be expected at very slow walking speeds (0.8 to 3.2 km/h) for all devices. Ankle-worn and thigh-worn devices demonstrated the highest accuracy. Speed and wear location had a significant effect on accuracy and bias, but not precision. TRIAL REGISTRATION: Clinicaltrials.gov NCT01989104 . Registered November 14, 2013.

US Population-referenced Percentiles for Wrist-Worn Accelerometer-derived Activity.

PURPOSE: This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all recorded movement, regardless of the purpose, context, or intensity. METHODS: Wrist-worn accelerometer data from the combined 2011-2014 National Health and Nutrition Examination Survey cycles and the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey were used for this analysis. Monitor-Independent Movement Summary units (MIMS-units) from raw triaxial accelerometer data were used. We removed the partial first and last assessment days and days with ≥5% nonwear time. Participants with ≥1 valid day were included. Mean MIMS-units were calculated across all valid days. Percentile tables and smoothed curves of daily MIMS-units were calculated for each age and sex using the Generalized Additive Models for Location Shape and Scale. RESULTS: The analytical sample included 14,705 participants age ≥3 yr. The MIMS-unit activity among youth was similar for both sexes, whereas adult females generally had higher MIMS-unit activity than did males. Median daily MIMS-units peaked at age 6 yr for both sexes (males, 20,613; females, 20,706). Lowest activity was observed for males and females 80+ yr of age: 8799 and 9503, respectively. CONCLUSIONS: Population referenced MIMS-unit percentiles for US youth and adults are a novel means of characterizing total activity volume. By using MIMS-units, we provide a standardized reference that can be applied across various wrist-worn accelerometer devices. Further work is needed to link these metrics to activity intensity categories and health outcomes.

Cadence (steps/min) and relative intensity in 21 to 60-year-olds: the CADENCE-adults study.

BACKGROUND: Heuristic cadence (steps/min) thresholds of ≥100 and ≥ 130 steps/min correspond with absolutely-defined moderate (3 metabolic equivalents [METs]; 1 MET = 3.5 mL O2·kg- 1·min- 1) and vigorous (6 METs) intensity, respectively. Scarce evidence informs cadence thresholds for relatively-defined moderate (≥ 64% heart rate maximum [HRmax = 220-age], ≥ 40%HR reserve [HRR = HRmax -HRresting, and ≥ 12 Rating of Perceived Exertion [RPE]); or vigorous intensity (≥ 77%HRmax, ≥ 60%HRR, and ≥ 14 RPE). PURPOSE: To identify heuristic cadence thresholds corresponding with relatively-defined moderate and vigorous intensity in 21-60-year-olds. METHODS: In this cross-sectional study, 157 adults (40.4 ± 11.5 years; 50.6% men) completed up to twelve 5-min treadmill bouts, beginning at 0.5 mph and increasing by 0.5 mph. Steps were directly observed, HR was measured with chest-worn monitors, and RPE was queried in the final minute of each bout. Segmented mixed model regression and Receiver Operating Characteristic (ROC) curve analyses identified optimal cadence thresholds, stratified by age (21-30, 31-40, 41-50, and 51-60 years). Reconciliation of the two analytical models, including trade-offs between sensitivity, specificity, positive and negative predictive values, and overall accuracy, yielded final heuristic cadences. RESULTS: Across all moderate intensity indicators, the segmented regression models estimated optimal cadence thresholds ranging from 123.8-127.5 (ages 21-30), 121.3-126.0 (ages 31-40), 117.7-122.7 (ages 41-50), and 113.3-116.1 steps/min (ages 51-60). Corresponding values for vigorous intensity were 140.3-144.1, 140.2-142.6, 139.3-143.6, and 131.6-132.8 steps/min, respectively. ROC analysis estimated chronologically-arranged age groups' cadence thresholds ranging from 114.5-118, 113.5-114.5, 104.6-112.9, and 103.6-106.0 across all moderate intensity indicators, and 127.5, 121.5, 117.2-123.2, and 113.0 steps/min, respectively, for vigorous intensity. CONCLUSIONS: Heuristic cadence thresholds corresponding to relatively-defined moderate intensity for the chronologically-arranged age groups were ≥ 120, 120, 115, and 105 steps/min, regardless of the intensity indicator (i.e., % HRmax, %HRR, or RPE). Corresponding heuristic values for vigorous intensity indicators were ≥ 135, 130, 125, and 120 steps/min. These cadences are useful for predicting/programming intensity aligned with age-associated differences in physiological response to, and perceived experiences of, moderate and/or vigorous intensity. TRIAL REGISTRATION: Clinicaltrials.gov NCT02650258 . Registered 24 December 2015.

A Transparent Method for Step Detection using an Acceleration Threshold.

Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply do not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and non-ambulatory activities. Seventy-five adults (21-39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22-2.2ms-1. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their non-dominant wrist. Raw acceleration (g) signals from the anterior-posterior, medial-lateral, vertical, and vector magnitude (VM) directions were assessed separately for each device. Signals were demeaned across all activities and bandpass filtered [0.25, 2.5Hz]. Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold=0.0267g) and wrist (threshold=0.0359g) using the VM signal. These thresholds yielded low error for the waist (RMSE<173 steps, ≤2.28 steps/minute) and wrist (RMSE<481 steps, ≤6.47 steps/minute) across all activities, and outperformed ActiLife's proprietary algorithm (RMSE=1312 and 2913 steps, 17.29 and 38.06 steps/minute for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.

Walking cadence (steps/min) and intensity in 41 to 60-year-old adults: the CADENCE-adults study.

BACKGROUND: In younger adults (i.e., those < 40 years of age) a walking cadence of 100 steps/min is a consistently supported threshold indicative of absolutely-defined moderate intensity ambulation (i.e., ≥ 3 metabolic equivalents; METs). Less is known about the cadence-intensity relationship in adults of middle-age. PURPOSE: To establish heuristic (i.e., evidence-based, practical, rounded) cadence thresholds for absolutely-defined moderate (3 METs) and vigorous (6 METs) intensity in adults 41 to 60 years of age. METHODS: In this cross-sectional study, 80 healthy adults of middle-age (10 men and 10 women representing each 5-year age-group between 41 to 60 years; body mass index = 26.0 ± 4.0 kg/m2) walked on a treadmill for 5-min bouts beginning at 0.5 mph and increasing in 0.5 mph increments. Performance termination criteria included: 1) transitioning to running, 2) reaching 75% of age-predicted maximum heart rate, or 3) reporting a Borg rating of perceived exertion > 13. Cadence was directly observed (i.e., hand tallied). Intensity (i.e., oxygen uptake [VO2] mL/kg/min) was assessed with an indirect calorimeter and converted to METs (1 MET = 3.5 mL/kg/min). A combination of segmented regression and Receiver Operating Characteristic (ROC) modeling approaches was used to identify optimal cadence thresholds. Final heuristic thresholds were determined based on an evaluation of classification accuracy (sensitivity, specificity, positive and negative predictive value, overall accuracy). RESULTS: The regression model identified 101.7 (95% Predictive Interval [PI]: 54.9-110.6) and 132.1 (95% PI: 122.0-142.2) steps/min as optimal cadence thresholds for 3 METs and 6 METs, respectively. Corresponding values based on ROC models were 98.5 (95% Confidence Intervals [CI]: 97.1-104.9) and 117.3 (95% CI: 113.1-126.1) steps/min. Considering both modeling approaches, the selected heuristic thresholds for moderate and vigorous intensity were 100 and 130 steps/min, respectively. CONCLUSIONS: Consistent with our previous report in 21 to 40-year-old adults, cadence thresholds of 100 and 130 steps/min emerged as heuristic values associated with 3 and 6 METs, respectively, in 41 to 60-year-old adults. These values were selected based on their utility for public health messaging and on the trade-offs in classification accuracy parameters from both statistical methods. Findings will need to be confirmed in older adults and in free-living settings.

Identification of Latent Classes of Motor Performance in a Heterogenous Population of Adults.

OBJECTIVE: To determine classes of motor performance based on community deployable motor impairment and functional tests in a heterogeneous adult population. DESIGN: Sixteen tests of limb-specific and whole-body measures of motor impairment and function were obtained. Linear regression analysis was used to dichotomize performance on each test as falling within or outside the age- and sex-predicted values. Latent class analysis was used to determine 3 classes of motor performance. The chi-square test of association and the Fisher exact test were used for categorical variables, and analysis of variance and the Kruskal-Wallis test were used for continuous variables to evaluate the relationship between demographic characteristics and latent classes. SETTING: General community. PARTICIPANTS: Individuals (N=118; 50 men) participated in the study. Quota sampling was used to recruit individuals who self-identified as healthy (n=44) or currently living with a preexisting chronic health condition, including arthritis (n=19), multiple sclerosis (n=18), Parkinson disease (n=17), stroke (n=18), or low functioning (n=2). INTERVENTION: Not applicable. MAIN OUTCOME MEASURE: Latent classes of motor performance. RESULTS: Across the entire sample, 3 latent classes of motor performance were determined that clustered individuals with motor performance falling: (1) within predicted values on most of the tests (expected class), (2) outside predicted values on some of the tests (moderate class), and (3) outside predicted values on most of the tests (severe class).The ability to distinguish between the respective classes based on the percent chance of falling outside predicted values was achieved using the following community deployable motor performance tests: 10-meter walk test (22%, 80%, and 100%), 6-minute walk test (14.5%, 37.5%, and 100%), grooved pegboard test (23%, 38%, and 100%), and modified physical performance test (3%, 54%, and 96%). CONCLUSIONS: In this heterogeneous group of adults, we found 3 distinct classes of motor performance, with the sample clustering into an expected test score group, a moderate test score deficiency group, and a severed test score deficiency group. Based on the motor performance tests, we established that community deployable, easily administered testing could accurately predict the established clusters of motor performance.

Estimating Sedentary Time from a Hip- and Wrist-Worn Accelerometer.

PURPOSE: This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG). METHODS: Forty-eight young (18-25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models. RESULTS: On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5-2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods. CONCLUSION: Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.

Metrics of Diabetes Risk Are Only Minimally Improved by Exercise Training in Postmenopausal Breast Cancer Survivors.

CONTEXT: Insulin resistance is a risk factor for breast cancer recurrence. How exercise training changes fasting and postglucose insulin resistance in breast cancer survivors is unknown. OBJECTIVE: To evaluate exercise-induced changes in postglucose ingestion insulin concentrations, insulin resistance, and their associations with cancer-relevant biomarkers in breast cancer survivors. SETTING: The University of Massachusetts Kinesiology Department. PARTICIPANTS: 15 postmenopausal breast cancer survivors not meeting the physical activity guidelines (150 min/week of exercise). INTERVENTION: A supervised 12-week aerobic exercise program (60 min/day, 3-4 days/week). MAIN OUTCOME MEASURES: Postglucose ingestion insulin was determined by peak insulin and area under the insulin curve (iAUC) during a 5-sample oral glucose tolerance test. Insulin sensitivity was estimated from the Matsuda composite insulin sensitivity index (C-ISI). Changes in fitness and body composition were determined from submaximal VO2peak and dual energy X-ray absorptiometry. RESULTS: Participants averaged 156.8 ± 16.6 min/week of supervised exercise. Estimated VO2peak significantly increased (+2.8 ± 1.4 mL/kg/min, P < .05) and body weight significantly decreased (-1.1 ± 0.8 kg, P < .05) following the intervention. There were no differences in fasting insulin, iAUC, C-ISI, or peak insulin following the intervention. Insulin was only significantly lower 120 min following glucose consumption (68.8 ± 34.5 vs 56.2 ± 31.9 uU/mL, P < .05), and there was a significant interaction with past/present aromatase inhibitor (AI) use for peak insulin (-11.99 non-AI vs +13.91 AI uU/mL) and iAUC (-24.03 non-AI vs +32.73 AI uU/mL). CONCLUSIONS: Exercise training had limited overall benefits on insulin concentrations following glucose ingestion in breast cancer survivors but was strongly influenced by AI use.