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Feasibility and Validity of Assessing Low-Income, African American Older Adults’ Physical Activity and Sedentary Behavior Through Ecological Momentary Assessment

Jaclyn P. Maher, Kourtney Sappenfield, Heidi Scheer, Christine Zecca, Derek J. Hevel, and Laurie Kennedy-Malone

Ecological momentary assessment (EMA) is a methodological tool that can provide novel insights into the prediction and modeling of physical behavior; however, EMA has not been used to study physical activity (PA) or sedentary behavior (SB) among racial minority older adults. This study aimed to determine the feasibility and validity of an EMA protocol to assess racial minority older adults’ PA and SB. For 8 days, older adults (n = 91; 89% African American; 70% earning <$20,000/year) received six randomly prompted, smartphone-based EMA questionnaires per day and wore an activPAL monitor to measure PA and SB. The PA and SB were also self-reported through EMA. Participants were compliant with the EMA protocol on 92.4% of occasions. Participants were more likely to miss an EMA prompt in the afternoon compared to morning and on weekend days compared to weekdays. Participants were less likely to miss an EMA prompt when engaged in more device-based SB in the 30 min around the prompt. When participants self-reported PA, they engaged in less device-based PA in the 15 min after compared to the 15 min before the EMA prompt, suggesting possible reactance or disruption of PA. EMA-reported PA and SB were positively associated with device-based PA and SB in the 30 min around the EMA prompt, supporting criterion validity. Overall, the assessment of low-income, African American older adults’ PA and SB through EMA is feasible and valid, though physical behaviors may influence compliance and prompting may create reactivity.

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Implications and Recommendations for Equivalence Testing in Measures of Movement Behaviors: A Scoping Review

Myles W. O’Brien

Equivalence testing may provide complementary information to more frequently used statistical procedures because it determines whether physical behavior outcomes are statistically equivalent to criterion measures. A caveat of this procedure is the predetermined selection of upper and lower bounds of acceptable error around a specified zone of equivalence. With no clear guidelines available to assist researchers, these equivalence zones are arbitrarily selected. A scoping review of articles implementing equivalence testing was performed to determine the validity of physical behavior outcomes; the aim was to characterize how this procedure has been implemented and to provide recommendations. A literature search from five databases initially identified potentially 1,153 articles which resulted in the acceptance of 19 studies (20 arms) conducted in children/youth and 40 in adults (49 arms). Most studies were conducted in free-living conditions (children/youth = 13 arms; adults = 22 arms) and employed a ±10% equivalence zone. However, equivalence zones ranged from ±3% to ±25% with only a subset using absolute thresholds (e.g., ±1,000 steps/day). If these equivalence zones were increased or decreased by ±5%, 75% (15/20, children/youth) and 71% (35/49, adults), they would have exhibited opposing equivalence test outcomes (i.e., equivalent to nonequivalent or vice versa). This scoping review identifies the heterogeneous usage of equivalence testing in studies examining the accuracy of (in)activity measures. In the absence of evidence-based standardized equivalence criteria, presenting the percentage required to achieve statistical equivalence or using absolute thresholds as a proportion of the SD may be a better practice than arbitrarily selecting zones a priori.

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Should We Use Activity Tracker Data From Smartphones and Wearables to Understand Population Physical Activity Patterns?

Jacqueline L. Mair, Lawrence D. Hayes, Amy K. Campbell, and Nicholas Sculthorpe

Researchers, practitioners, and public health organizations from around the world are becoming increasingly interested in using data from consumer-grade devices such as smartphones and wearable activity trackers to measure physical activity (PA). Indeed, large-scale, easily accessible, and autonomous data collection concerning PA as well as other health behaviors is becoming ever more attractive. There are several benefits of using consumer-grade devices to collect PA data including the ability to obtain big data, retrospectively as well as prospectively, and to understand individual-level PA patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level PA. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from smartphones and wearable activity trackers to understand large-scale PA patterns and stimulate discussion among the scientific community on what the future holds with respect to PA measurement and surveillance.

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A Transparent Method for Step Detection Using an Acceleration Threshold

Scott W. Ducharme, Jongil Lim, Michael A. Busa, Elroy J. Aguiar, Christopher C. Moore, John M. Schuna Jr., Tiago V. Barreira, John Staudenmayer, Stuart R. Chipkin, and Catrine Tudor-Locke

Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply does not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and nonambulatory 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 to 2.2 m/s. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their nondominant wrist. Raw acceleration (g) signals from the anterior–posterior, medial–lateral, vertical, and vector magnitude directions were assessed separately for each device. Signals were demeaned across all activities and band-pass filtered (0.25, 2.5 Hz). 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 vector magnitude signal. These thresholds yielded low error for the waist (RMSE < 173 steps, ≤2.28 steps/min) and wrist (RMSE < 481 steps, ≤6.47 steps/min) across all activities, and outperformed ActiLife’s proprietary algorithm (RMSE = 1,312 and 2,913 steps, 17.29 and 38.06 steps/min 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.

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Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults

Chelsea Steel, Katie Crist, Amanda Grimes, Carolina Bejarano, Adrian Ortega, Paul R. Hibbing, Jasper Schipperijn, and Jordan A. Carlson

Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

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Volume 4 (2021): Issue S1 (Oct 2021)

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Impact of Reduced Sampling Rate on Accelerometer-Based Physical Activity Monitoring and Machine Learning Activity Classification

Scott Small, Sara Khalid, Paula Dhiman, Shing Chan, Dan Jackson, Aiden Doherty, and Andrew Price

Purpose: Lowering the sampling rate of accelerometers in physical activity research can dramatically increase study monitoring periods through longer battery life; however, the effect of reduced sampling rate on activity metric validity is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behavior types. Methods: Healthy adults wore sets of two Axivity AX3 accelerometers on the dominant wrist and hip for 24 hr. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate to vigorous activity, and behavioral activities were calculated and processed using both linear and nearest neighbor resampling. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results: Of the 54 total participants, 45 contributed >20 hr of hip wear time and 51 contributed >20 hr of wrist wear time. Strong correlation was observed between 25- and 100-Hz sampling rates in overall activity measurement (r = .97–.99), yet consistently lower activity was observed in data collected at 25 Hz (3.1%–13.9%). Reduced sleep and light activity and increased sedentary time was classified in 25-Hz data by machine learning models. Discrepancies were greater when linear interpolation resampling was used in postprocessing. Conclusions: The 25- and 100-Hz accelerometer data are highly correlated with predictable differences, which can be accounted for in interstudy comparisons. Sampling rate and resampling methods should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.

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Impact of ActiGraph Sampling Rate and Intermonitor Comparability on Measures of Physical Activity in Adults

Kimberly A. Clevenger, Jan Christian Brønd, Daniel Arvidsson, Alexander H.K. Montoye, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer

Background: ActiGraph is a commonly used, research-grade accelerometer brand, but there is little information regarding intermonitor comparability of newer models. In addition, while sampling rate has been shown to influence accelerometer metrics, its influence on measures of free-living physical activity has not been directly studied. Purpose: To examine differences in physical activity metrics due to intermonitor variability and chosen sampling rate. Methods: Adults (n = 20) wore two hip-worn ActiGraph wGT3X-BT monitors for 1 week, with one accelerometer sampling at 30 Hz and the other at 100 Hz, which was downsampled to 30 Hz. Activity intensity was classified using vector magnitude, Euclidean Norm Minus One (ENMO), and mean amplitude deviation (MAD) cut points. Equivalence testing compared outcomes. Results: There was a lack of intermonitor equivalence for ENMO, time in sedentary/light- or moderate-intensity activity according to ENMO cut points, and time in moderate-intensity activity according to MAD cut points. Between sampling rates, differences existed for time in moderate-intensity activity according to vector magnitude, ENMO, and MAD cut points, and time in sedentary/light-intensity activity according to ENMO cut points. While mean differences were small (0.1–1.7 percentage points), this would equate to differences in moderate-to vigorous-intensity activity over a 10-hr wear day of 3.6 (MAD) to 10.8 (ENMO) min/day for intermonitor comparisons or 3.6 (vector magnitude) to 5.4 (ENMO) min/day for sampling rate. Conclusions: Epoch-level intermonitor differences were larger than differences due to sampling rate, but both may impact outcomes such as time spent in each activity intensity. ENMO was the least comparable metric between monitors or sampling rates.

Open access

The 7th International Conference on Ambulatory Monitoring of Physical Activity and Movement

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Volume 4 (2021): Issue 3 (Sep 2021)