Search Results

Purpose: To investigate potential time drift between devices when using Global Positioning Systems (GPS) and accelerometers in field-based research. Methods: Six Qstarz BT-Q1000XT GPS trackers, activPAL3 accelerometers, and ActiGraph GT3X+ and GT3X accelerometers were tested over 1–3 waves, each lasting 9–14 days. Once per day an event marker was created on each pair of devices concurrently. The difference in seconds between the time stamps for each event marker were calculated between each pair of GPS and activPAL devices and GPS and ActiGraph devices. Mixed-effects linear regression tested time drift across days and waves and between two rooms/locations (in an inner room vs. on a windowsill in an outer room). Results: The GPS trackers remained within one second of the computer clock across days and waves and between rooms. The activPAL devices drifted an average of 8.38 seconds behind the GPS devices over 14 days (p < .001). The ActiGraph GT3X+ devices drifted an average of 11.67 seconds ahead of the GPS devices over 14 days (p < .001). The ActiGraph GT3X devices drifted an average of 28.83 seconds behind the GPS devices over 9 days (p < .001). Time drift did not differ across waves but did differ between rooms and across devices. Conclusions: Time drift between the GPS and accelerometer models tested was minimal and is unlikely to be problematic when addressing many common research questions. However, studies that require high levels of precision when matching short (e.g., 1-second) time intervals may benefit from consideration of time drift and potential adjustments.

Purpose: We investigated convergent validity of commonly used ActiGraph scoring methods with various activPAL scoring methods in youth and adults. Methods: Youth and adults wore an ActiGraph and activPAL simultaneously for 1–3 days. We compared moderate to vigorous physical activity (MVPA) estimates from the ActiGraph Evenson 15-s (youth) and Freedson 60-s (adult) cut-point scoring methods and four activPAL scoring methods based on metabolic equivalents (METs), step counts, vertical axis counts, and vector magnitude counts. All activPAL methods were applied to 15-s epochs for youth and 60-s epochs for adults, and the METs method was also applied to 1-s epochs. Epoch-level agreement was examined with classification tests (sensitivity, positive predictive value, and F1) using the ActiGraph methods as the referent. Day-level agreement was examined using tests of mean error, mean absolute error, and Spearman correlations. Results: Relative to ActiGraph methods, which indicated a mean MVPA of 41 min/day for youth and 24 min/day for adults, the activPAL METs method applied to 15-s epochs in youth and 60-s epochs in adults yielded the most comparable estimates of MVPA. Daily MVPA estimated from all other activPAL scoring methods generally had poor agreement with ActiGraph methods in youth and adults. Conclusion: When using the same epoch lengths between monitors, MVPA estimation via the activPAL METs scoring method appears to have good comparability to ActiGraph cut points at the group-level and moderate comparability at the individual-level in youth and adults. When using this scoring method, the activPAL appears to be appropriate for measuring daily minutes of MVPA in youth and adults.

Objectives: To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity– or nutrition-related behaviors and/or environments. Methods: Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. Results: Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). Conclusions: Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.

Actiwatch devices are often used to estimate time in bed (TIB) but recently became commercially unavailable. Thigh-worn activPAL devices could be a viable alternative. We tested convergent validity between activPAL (CREA algorithm) and Actiwatch devices. Data were from free-living samples comprising 47 youth (3–16 valid nights/participant) and 42 adults (6–26 valid nights/participant) who wore both devices concurrently. On average, activPAL predicted earlier bedtimes and later risetimes compared with Actiwatch, resulting in longer overnight intervals (by 1.49 hr/night for youth and 0.67 hr/night for adults; both p < .001). TIB interruptions were predicted less commonly by activPAL (mean <2 interruptions/night for both youth and adults) than Actiwatch (mean of 24–26 interruptions/night in both groups; both p < .001). Overnight intervals for both devices tended to overlap for lengthy periods (mean of 7.38 hr/night for youth and 7.69 hr/night for adults). Within these overlapping periods, the devices gave matching epoch-level TIB predictions an average of 87.9% of the time for youth and 84.3% of the time for adults. Most remaining epochs (11.8% and 15.1%, respectively) were classified as TIB by activPAL, but not Actiwatch. Overall, the devices had fair agreement during the overlapping periods but limited agreement when predicting interruptions, bedtime, or risetime. Future work should assess the criterion validity of activPAL devices to understand implications for health research. The present findings demonstrate that activPAL is not interchangeable with Actiwatch, which is consistent with their differing foundations (thigh inclination for activPAL vs. wrist movement for Actiwatch).

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.