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 (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-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.

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.

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.

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).

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.

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.

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.

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.

A Framework to Evaluate Devices That Assess Physical Behavior.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

Walking cadence (steps/min) and intensity in 21-40 year olds: CADENCE-adults.

BACKGROUND: Previous studies have reported that walking cadence (steps/min) is associated with absolutely-defined intensity (metabolic equivalents; METs), such that cadence-based thresholds could serve as reasonable proxy values for ambulatory intensities. PURPOSE: To establish definitive heuristic (i.e., evidence-based, practical, rounded) thresholds linking cadence with absolutely-defined moderate (3 METs) and vigorous (6 METs) intensity. METHODS: In this laboratory-based cross-sectional study, 76 healthy adults (10 men and 10 women representing each 5-year age-group category between 21 and 40 years, BMI = 24.8 ± 3.4 kg/m2) performed a series of 5-min treadmill bouts separated by 2-min rests. Bouts began at 0.5 mph and increased in 0.5 mph increments until participants: 1) chose to run, 2) achieved 75% of their predicted maximum heart rate, or 3) reported a Borg rating of perceived exertion > 13. Cadence was hand-tallied, and intensity (METs) was measured using a portable indirect calorimeter. Optimal cadence thresholds for moderate and vigorous ambulatory intensities were identified using a segmented regression model with random coefficients, as well as Receiver Operating Characteristic (ROC) models. Positive predictive values (PPV) of candidate heuristic thresholds were assessed to determine final heuristic values. RESULTS: Optimal cadence thresholds for 3 METs and 6 METs were 102 and 129 steps/min, respectively, using the regression model, and 96 and 120 steps/min, respectively, using ROC models. Heuristic values were set at 100 steps/min (PPV of 91.4%), and 130 steps/min (PPV of 70.7%), respectively. CONCLUSIONS: Cadence thresholds of 100 and 130 steps/min can serve as reasonable heuristic thresholds representative of absolutely-defined moderate and vigorous ambulatory intensity, respectively, in 21-40 year olds. These values represent useful proxy values for recommending and modulating the intensity of ambulatory behavior and/or as measurement thresholds for processing accelerometer data. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT02650258 ).

Physical activity classification with dynamic discriminative methods.

A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g., lying down, sitting, standing, or walking) or intensity (e.g., sedentary, light, moderate, or vigorous). This can be formulated as a labeled classification problem, where the model relates a feature vector summarizing the accelerometer signal in a window of time to the activity type or intensity in that window. These data exhibit two key characteristics: (1) the activity classes in different time windows are not independent, and (2) the accelerometer features have moderately high dimension and follow complex distributions. Through a simulation study and applications to three datasets, we demonstrate that a model's classification performance is related to how it addresses these aspects of the data. Dynamic methods that account for temporal dependence achieve better performance than static methods that do not. Generative methods that explicitly model the distribution of the accelerometer signal features do not perform as well as methods that take a discriminative approach to establishing the relationship between the accelerometer signal and the activity class. Specifically, Conditional Random Fields consistently have better performance than commonly employed methods that ignore temporal dependence or attempt to model the accelerometer features.

Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.

We take a functional data approach to longitudinal studies with complex bivariate outcomes. This work is motivated by data from a physical activity study that measured 2 responses over time in 5-minute intervals. One response is the proportion of time active in each interval, a continuous proportions with excess zeros and ones. The other response, energy expenditure rate in the interval, is a continuous variable with excess zeros and skewness. This outcome is complex because there are 3 possible activity patterns in each interval (inactive, partially active, and completely active), and those patterns, which are observed, induce both nonrandom and random associations between the responses. More specifically, the inactive pattern requires a zero value in both the proportion for active behavior and the energy expenditure rate; a partially active pattern means that the proportion of activity is strictly between zero and one and that the energy expenditure rate is greater than zero and likely to be moderate, and the completely active pattern means that the proportion of activity is exactly one, and the energy expenditure rate is greater than zero and likely to be higher. To address these challenges, we propose a 3-part functional data joint modeling approach. The first part is a continuation-ratio model to reorder the ordinal valued 3 activity patterns. The second part models the proportions when they are in interval (0,1). The last component specifies the skewed continuous energy expenditure rate with Box-Cox transformations when they are greater than zero. In this 3-part model, the regression structures are specified as smooth curves measured at various time points with random effects that have a correlation structure. The smoothed random curves for each variable are summarized using a few important principal components, and the association of the 3 longitudinal components is modeled through the association of the principal component scores. The difficulties in handling the ordinal and proportional variables are addressed using a quasi-likelihood type approximation. We develop an efficient algorithm to fit the model that also involves the selection of the number of principal components. The method is applied to physical activity data and is evaluated empirically by a simulation study.

Methods to assess an exercise intervention trial based on 3-level functional data.

Motivated by data recording the effects of an exercise intervention on subjects' physical activity over time, we develop a model to assess the effects of a treatment when the data are functional with 3 levels (subjects, weeks and days in our application) and possibly incomplete. We develop a model with 3-level mean structure effects, all stratified by treatment and subject random effects, including a general subject effect and nested effects for the 3 levels. The mean and random structures are specified as smooth curves measured at various time points. The association structure of the 3-level data is induced through the random curves, which are summarized using a few important principal components. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed effects model framework for model fitting, prediction and inference. We develop an algorithm to fit the model iteratively with the Expectation/Conditional Maximization Either (ECME) version of the EM algorithm and eigenvalue decompositions. Selection of the number of principal components and handling incomplete data issues are incorporated into the algorithm. The performance of the Wald-type hypothesis test is also discussed. The method is applied to the physical activity data and evaluated empirically by a simulation study.

Hierarchical functional data with mixed continuous and binary measurements.

Motivated by objective measurements of physical activity, we take a functional data approach to longitudinal data with simultaneous measurement of a continuous and a binary outcomes. The regression structures are specified as smooth curves measured at various time-points with random effects that have a hierarchical correlation structure. The random effect curves for each variable are summarized using a few important principal components, and the association of the two longitudinal variables is modeled through the association of the principal component scores. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed effects model framework for model fitting, prediction and inference. Via a quasilikelihood type approximation for the binary component, we develop an algorithm to fit the model. Data-based transformation of the continuous variable and selection of the number of principal components are incorporated into the algorithm. The method is applied to the motivating physical activity data and is evaluated empirically by a simulation study. Extensions for different types of outcomes are also discussed.

Comparison of raw acceleration from the GENEA and ActiGraph™ GT3X+ activity monitors.

PURPOSE: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. METHODS: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. RESULTS: GENEA produced significantly higher (p < 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p < 0.05) prediction accuracy. Prediction accuracy was not compromised when interchangeably using FD models between monitors. CONCLUSIONS: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration.

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.

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.