Background: ActiGraph accelerometers can monitor sleep and physical activity (PA) during free-living, but there is a need to confirm agreement in outcomes between different models. Methods: Sleep and PA metrics from two ActiGraphs were compared after participants (N = 30) wore a GT9X and wGT3X-BT on their nondominant wrist for 7 days during free-living. PA metrics including total steps, counts, average acceleration—Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation, intensity gradient, the minimum acceleration value of the most active 10 and 30 min (M10, M30), time spent in activity intensities from vector magnitude (VM) counts, and ENMO cut points and sleep metrics (sleep period time window, sleep duration, sleep onset, and waking time) were compared. Results: Excellent agreement was evident for average acceleration-Mean Amplitude Deviation, counts, total steps, M10, and light PA (VM counts) with good agreement evident from the remaining PA metrics apart from moderate–vigorous PA (VM counts) which demonstrated moderate agreement. Mean bias for all PA metrics were low, as were the limits of agreement for the intensity gradient, average acceleration-Mean Amplitude Deviation, and inactive time (ENMO and VM counts). The limits of agreement for all other PA metrics were >10%. Excellent agreement, low mean bias, and narrow limits of agreement were evident for all sleep metrics. All sleep and PA metrics demonstrated equivalence (equivalence zone of ≤10%) apart from moderate–vigorous PA (ENMO) which needed an equivalence zone of 16%. Conclusions: Equivalent estimates of almost all PA and sleep metrics are provided from the GT9X and wGT3X-BT worn on the nondominant wrist.
背景：ActiGraph 加速度计可监测自由生活期间的睡眠和体力活动（PA），但需要确认不同型号之间的结果是否一致。方法： 对两种 ActiGraph 的睡眠和体力活动指标进行比较：参与者（N = 30）在自由生活期间的非支配手腕上佩戴 GT9X 和 wGT3X-BT 7 天后，比较了两种 ActiGraph 的睡眠和体力活动指标。比较了包括总步数、计数、平均加速度-欧氏负一（ENMO）和平均振幅偏差、强度梯度、最活跃的 10 分钟和 30 分钟（M10、M30）的最小加速度值、矢量幅度（VM）计数的活动强度时间、ENMO 切点在内的 PA 指标和睡眠指标（睡眠期时间窗、睡眠持续时间、睡眠开始时间和觉醒时间）。结果显示平均加速度-平均振幅偏差、计数、总步数、M10 和轻度 PA（VM 计数）的一致性非常好，除中度-剧烈 PA（VM 计数）的一致性一般外，其余 PA 指标的一致性也很好。所有 PA 指标的平均偏差都很低，强度梯度、平均加速度-平均振幅偏差和非活动时间（ENMO 和 VM 计数）的一致性限度也很低。所有其他 PA 指标的一致性均大于 10%。所有睡眠指标的一致性都非常好，平均偏差小，一致性范围窄。除了中度剧烈运动（ENMO）需要 16% 的等效区域外，所有睡眠和剧烈运动指标均显示出等效性（等效区域≤10%）。结论佩戴在非支配腕上的 GT9X 和 wGT3X-BT 可以提供几乎所有 PA 和睡眠指标的等效估计值。
{"title":"Comparison of Sleep and Physical Activity Metrics From Wrist-Worn ActiGraph wGT3X-BT and GT9X Accelerometers During Free-Living in Adults","authors":"Duncan S. Buchan","doi":"10.1123/jmpb.2023-0026","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0026","url":null,"abstract":"Background: ActiGraph accelerometers can monitor sleep and physical activity (PA) during free-living, but there is a need to confirm agreement in outcomes between different models. Methods: Sleep and PA metrics from two ActiGraphs were compared after participants (N = 30) wore a GT9X and wGT3X-BT on their nondominant wrist for 7 days during free-living. PA metrics including total steps, counts, average acceleration—Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation, intensity gradient, the minimum acceleration value of the most active 10 and 30 min (M10, M30), time spent in activity intensities from vector magnitude (VM) counts, and ENMO cut points and sleep metrics (sleep period time window, sleep duration, sleep onset, and waking time) were compared. Results: Excellent agreement was evident for average acceleration-Mean Amplitude Deviation, counts, total steps, M10, and light PA (VM counts) with good agreement evident from the remaining PA metrics apart from moderate–vigorous PA (VM counts) which demonstrated moderate agreement. Mean bias for all PA metrics were low, as were the limits of agreement for the intensity gradient, average acceleration-Mean Amplitude Deviation, and inactive time (ENMO and VM counts). The limits of agreement for all other PA metrics were >10%. Excellent agreement, low mean bias, and narrow limits of agreement were evident for all sleep metrics. All sleep and PA metrics demonstrated equivalence (equivalence zone of ≤10%) apart from moderate–vigorous PA (ENMO) which needed an equivalence zone of 16%. Conclusions: Equivalent estimates of almost all PA and sleep metrics are provided from the GT9X and wGT3X-BT worn on the nondominant wrist.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01Epub Date: 2024-04-02DOI: 10.1123/jmpb.2023-0038
Samuel R LaMunion, Robert J Brychta, Joshua R Freeman, Pedro F Saint-Maurice, Charles E Matthews, Asuka Ishihara, Kong Y Chen
{"title":"Characterizing ActiGraph's Idle Sleep Mode in Free-living Assessments of Physical Behavior.","authors":"Samuel R LaMunion, Robert J Brychta, Joshua R Freeman, Pedro F Saint-Maurice, Charles E Matthews, Asuka Ishihara, Kong Y Chen","doi":"10.1123/jmpb.2023-0038","DOIUrl":"10.1123/jmpb.2023-0038","url":null,"abstract":"","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11524531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marc Weitz, B. Morseth, L. Hopstock, Alexander Horsch
Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of objectively measured physical activity under free-living conditions. The total volume of moderate to vigorous physical activity (MVPA) was significantly reduced after post hoc auto-calibration for uniaxial and triaxial count data, as well as for Euclidean Norm Minus One and mean amplitude deviation raw data. Weekly estimates of MVPA were reduced on average by 5.5, 9.2, 45.8, and 4.8 min, respectively, when compared to the original uncalibrated estimates. Our results indicate a general trend of overestimating physical activity when using factory-calibrated sensors. In particular, the accuracy of estimates derived from the Euclidean Norm Minus One feature suffered from uncalibrated sensors. For all modalities, the more uncalibrated the sensor was, the more MVPA was overestimated. This might especially affect studies with lower sample sizes.
{"title":"Influence of Accelerometer Calibration on the Estimation of Objectively Measured Physical Activity: The Tromsø Study","authors":"Marc Weitz, B. Morseth, L. Hopstock, Alexander Horsch","doi":"10.1123/jmpb.2023-0019","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0019","url":null,"abstract":"Accelerometers are increasingly used to observe human behavior such as physical activity under free-living conditions. An important prerequisite to obtain reliable results is the correct calibration of the sensors. However, accurate calibration is often neglected, leading to potentially biased results. Here, we demonstrate and quantify the effect of accelerometer miscalibration on the estimation of objectively measured physical activity under free-living conditions. The total volume of moderate to vigorous physical activity (MVPA) was significantly reduced after post hoc auto-calibration for uniaxial and triaxial count data, as well as for Euclidean Norm Minus One and mean amplitude deviation raw data. Weekly estimates of MVPA were reduced on average by 5.5, 9.2, 45.8, and 4.8 min, respectively, when compared to the original uncalibrated estimates. Our results indicate a general trend of overestimating physical activity when using factory-calibrated sensors. In particular, the accuracy of estimates derived from the Euclidean Norm Minus One feature suffered from uncalibrated sensors. For all modalities, the more uncalibrated the sensor was, the more MVPA was overestimated. This might especially affect studies with lower sample sizes.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139631382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elizabeth Chun, I. Gaynanova, Edward L. Melanson, Kate Lyden
Introduction: Reducing sedentary time is associated with improved postprandial glucose regulation. However, it is not known if the timing of sedentary behavior (i.e., pre- vs. postmeal) differentially impacts postprandial glucose in older adults with overweight or obesity. Methods: In this secondary analysis, older adults (≥65 years) with overweight and obesity (body mass index ≥ 25 kg/m2) wore a continuous glucose monitor and a sedentary behavior monitor continuously in their real-world environments for four consecutive days on four separate occasions. Throughout each 4-day measurement period, participants followed a standardized eucaloric diet and recorded mealtimes in a diary. Glucose, sedentary behavior, and meal intake data were fused using sensor and diary timestamps. Mixed-effect linear regression models were used to evaluate the impact of sedentary timing relative to meal intake. Results: Premeal sedentary time was significantly associated with both the increase from premeal glucose to the postmeal peak (ΔG) and the percent of premeal glucose increase that was recovered 1-hr postmeal glucose peak (%Baseline Recovery; p < .05), with higher levels of premeal sedentary time leading to both a larger ΔG and a smaller %Baseline Recovery. Postmeal sedentary time was significantly associated with the time from meal intake to glucose peak (ΔT; p < .05), with higher levels of postmeal sedentary time leading to a longer time to peak. Conclusions: Pre- versus postmeal sedentary behavior differentially impacts postprandial glucose response in older adults with overweight or obesity, suggesting that the timing of sedentary behavior reductions might play an influential role on long-term glycemic control.
{"title":"Pre- Versus Postmeal Sedentary Duration—Impact on Postprandial Glucose in Older Adults With Overweight or Obesity","authors":"Elizabeth Chun, I. Gaynanova, Edward L. Melanson, Kate Lyden","doi":"10.1123/jmpb.2023-0032","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0032","url":null,"abstract":"Introduction: Reducing sedentary time is associated with improved postprandial glucose regulation. However, it is not known if the timing of sedentary behavior (i.e., pre- vs. postmeal) differentially impacts postprandial glucose in older adults with overweight or obesity. Methods: In this secondary analysis, older adults (≥65 years) with overweight and obesity (body mass index ≥ 25 kg/m2) wore a continuous glucose monitor and a sedentary behavior monitor continuously in their real-world environments for four consecutive days on four separate occasions. Throughout each 4-day measurement period, participants followed a standardized eucaloric diet and recorded mealtimes in a diary. Glucose, sedentary behavior, and meal intake data were fused using sensor and diary timestamps. Mixed-effect linear regression models were used to evaluate the impact of sedentary timing relative to meal intake. Results: Premeal sedentary time was significantly associated with both the increase from premeal glucose to the postmeal peak (ΔG) and the percent of premeal glucose increase that was recovered 1-hr postmeal glucose peak (%Baseline Recovery; p < .05), with higher levels of premeal sedentary time leading to both a larger ΔG and a smaller %Baseline Recovery. Postmeal sedentary time was significantly associated with the time from meal intake to glucose peak (ΔT; p < .05), with higher levels of postmeal sedentary time leading to a longer time to peak. Conclusions: Pre- versus postmeal sedentary behavior differentially impacts postprandial glucose response in older adults with overweight or obesity, suggesting that the timing of sedentary behavior reductions might play an influential role on long-term glycemic control.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanlin Wu, M. O'Brien, Alex Peddle, W. S. Daley, Beverly D. Schwartz, D. Kimmerly, Ryan J. Frayne
Introduction: Device-based monitors often classify all sedentary positions as the sitting posture, but sitting with bent or straight legs may exhibit unique physiological and biomechanical effects. The classifications of the specific nuances of sitting have not been understood. The purpose of this study was to validate a dual-monitor approach from a trimonitor configuration measuring knee-flexion angles compared to motion capture (criterion) during sitting in laboratory setting. Methods: Nineteen adults (12♀, 24 ± 4 years) wore three activPALs (torso, thigh, tibia) while 14 motion capture cameras simultaneously tracked 15 markers located on bony landmarks. Each participant completed a 45-s supine resting period and eight, 45-s seated trials at different knee flexion angles (15° increment between 0° and 105°, determined via goniometry), followed by 15 s of standing. Validity was assessed via Friedman’s test (adjusted p value = .006), mean absolute error, Bland–Altman analyses, equivalence testing, and intraclass correlation. Results: Compared to motion capture, the calculated angles from activPALs were not different during 15°–90° (all, p ≥ .009), underestimated at 105° (p = .002) and overestimated at 0°, as well as the supine position (both, p < .001). Knee angles between 15° and 105° exhibited a mean absolute error of ∼5°, but knee angles <15° exhibited larger degrees of error (∼10°). A proportional (β = −0.12, p < .001) bias was observed, but a fixed (0.5° ± 1.7°, p = .405) bias did not exist. In equivalence testing, the activPALs were statistically equivalent to motion capture across 30°–105°. Strong agreement between the activPALs and motion capture was observed (intraclass correlation = .97, p < .001). Conclusions: The usage of a three-activPAL configuration detecting seated knee-flexion angles in free-living conditions is promising.
{"title":"Criterion Validity of Accelerometers in Determining Knee-Flexion Angles During Sitting in a Laboratory Setting","authors":"Yanlin Wu, M. O'Brien, Alex Peddle, W. S. Daley, Beverly D. Schwartz, D. Kimmerly, Ryan J. Frayne","doi":"10.1123/jmpb.2023-0027","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0027","url":null,"abstract":"Introduction: Device-based monitors often classify all sedentary positions as the sitting posture, but sitting with bent or straight legs may exhibit unique physiological and biomechanical effects. The classifications of the specific nuances of sitting have not been understood. The purpose of this study was to validate a dual-monitor approach from a trimonitor configuration measuring knee-flexion angles compared to motion capture (criterion) during sitting in laboratory setting. Methods: Nineteen adults (12♀, 24 ± 4 years) wore three activPALs (torso, thigh, tibia) while 14 motion capture cameras simultaneously tracked 15 markers located on bony landmarks. Each participant completed a 45-s supine resting period and eight, 45-s seated trials at different knee flexion angles (15° increment between 0° and 105°, determined via goniometry), followed by 15 s of standing. Validity was assessed via Friedman’s test (adjusted p value = .006), mean absolute error, Bland–Altman analyses, equivalence testing, and intraclass correlation. Results: Compared to motion capture, the calculated angles from activPALs were not different during 15°–90° (all, p ≥ .009), underestimated at 105° (p = .002) and overestimated at 0°, as well as the supine position (both, p < .001). Knee angles between 15° and 105° exhibited a mean absolute error of ∼5°, but knee angles <15° exhibited larger degrees of error (∼10°). A proportional (β = −0.12, p < .001) bias was observed, but a fixed (0.5° ± 1.7°, p = .405) bias did not exist. In equivalence testing, the activPALs were statistically equivalent to motion capture across 30°–105°. Strong agreement between the activPALs and motion capture was observed (intraclass correlation = .97, p < .001). Conclusions: The usage of a three-activPAL configuration detecting seated knee-flexion angles in free-living conditions is promising.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139634467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Montoye, Kimberly A. Clevenger, Benjamin D. Boudreaux, Michael D. Schmidt
Data from ActiGraph accelerometers have long been imported into ActiLife software, where the company’s proprietary “activity counts” were generated in order to understand physical behavior metrics. In 2022, ActiGraph released an open-source method to generate activity counts from any raw, triaxial accelerometer data using Python, which has been translated into RStudio packages. However, it is unclear if outcomes are comparable when generated in ActiLife and RStudio. Therefore, the authors’ technical note systematically compared activity counts and related physical behavior metrics generated from ActiGraph accelerometer data using ActiLife or available packages in RStudio and provides example code to ease implementation of such analyses in RStudio. In addition to comparing triaxial activity counts, physical behavior outputs (sleep, sedentary behavior, light-intensity physical activity, and moderate- to vigorous-intensity physical activity) were compared using multiple nonwear algorithms, epochs, cut points, sleep scoring algorithms, and accelerometer placement sites. Activity counts and physical behavior outcomes were largely the same between ActiLife and the tested packages in RStudio. However, peculiarities in the application of nonwear algorithms to the first and last portions of a data file (that occurred on partial, first or last days of data collection), differences in rounding, and handling of counts values on the borderline of activity intensities resulted in small but inconsequential differences in some files. The hope is that researchers and both hardware and software manufacturers continue to push efforts toward transparency in data analysis and interpretation, which will enhance comparability across devices and studies and help to advance fields examining links between physical behavior and health.
{"title":"Comparability of 24-hr Activity Cycle Outputs From ActiGraph Counts Generated in ActiLife and RStudio","authors":"A. Montoye, Kimberly A. Clevenger, Benjamin D. Boudreaux, Michael D. Schmidt","doi":"10.1123/jmpb.2023-0047","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0047","url":null,"abstract":"Data from ActiGraph accelerometers have long been imported into ActiLife software, where the company’s proprietary “activity counts” were generated in order to understand physical behavior metrics. In 2022, ActiGraph released an open-source method to generate activity counts from any raw, triaxial accelerometer data using Python, which has been translated into RStudio packages. However, it is unclear if outcomes are comparable when generated in ActiLife and RStudio. Therefore, the authors’ technical note systematically compared activity counts and related physical behavior metrics generated from ActiGraph accelerometer data using ActiLife or available packages in RStudio and provides example code to ease implementation of such analyses in RStudio. In addition to comparing triaxial activity counts, physical behavior outputs (sleep, sedentary behavior, light-intensity physical activity, and moderate- to vigorous-intensity physical activity) were compared using multiple nonwear algorithms, epochs, cut points, sleep scoring algorithms, and accelerometer placement sites. Activity counts and physical behavior outcomes were largely the same between ActiLife and the tested packages in RStudio. However, peculiarities in the application of nonwear algorithms to the first and last portions of a data file (that occurred on partial, first or last days of data collection), differences in rounding, and handling of counts values on the borderline of activity intensities resulted in small but inconsequential differences in some files. The hope is that researchers and both hardware and software manufacturers continue to push efforts toward transparency in data analysis and interpretation, which will enhance comparability across devices and studies and help to advance fields examining links between physical behavior and health.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140515747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Two-thirds of children in the United States do not meet the National Physical Activity Guidelines, leaving a majority at higher risk for negative health outcomes. Novel, effective children’s physical activity (PA) interventions are urgently needed. Dog-facilitated PA (e.g., dog walking and active play) is a promising intervention target, as dogs support many of the known correlates of children’s PA. There is a need for accurate methods of quantifying dog-facilitated PA. Purpose: The study purpose was to determine the feasibility and acceptability of a novel method for quantifying the volume and intensity of dog-facilitated PA among dog-owning children. Methods: Children and their dog(s) wore ActiGraph accelerometers with a Bluetooth proximity feature for 7 days. Additionally, parents logged child PA with the family dog(s). Total minutes of dog-facilitated PA and percentage of overall daily moderate to vigorous PA performed with the dog were calculated. Results: Twelve children (mean age = 7.8 ± 2.9 years) participated. There was high feasibility, with 100% retention, valid device data (at least 4 days ≥6-hr wear time), and completion of daily parent log and questionnaire packets. On average, dog-facilitated PA contributed 22.9% (9.2 min) and 15.1% (7.3 min) of the overall daily moderate to vigorous PA for children according to Bluetooth proximity data and parent report, respectively. Conclusions: This pilot study demonstrated the feasibility of utilizing an accelerometer with a proximity feature to quantify dog-facilitated PA. Future research should use this protocol with a larger, more diverse sample to determine whether dog-facilitated PA contributes a clinically significant amount toward overall PA in dog-owning youth.
{"title":"The KID Study (Kids Interacting With Dogs): Piloting a Novel Approach for Measuring Dog-Facilitated Youth Physical Activity","authors":"Colleen J. Chase, S. Burkart, Katie Potter","doi":"10.1123/jmpb.2023-0014","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0014","url":null,"abstract":"Background: Two-thirds of children in the United States do not meet the National Physical Activity Guidelines, leaving a majority at higher risk for negative health outcomes. Novel, effective children’s physical activity (PA) interventions are urgently needed. Dog-facilitated PA (e.g., dog walking and active play) is a promising intervention target, as dogs support many of the known correlates of children’s PA. There is a need for accurate methods of quantifying dog-facilitated PA. Purpose: The study purpose was to determine the feasibility and acceptability of a novel method for quantifying the volume and intensity of dog-facilitated PA among dog-owning children. Methods: Children and their dog(s) wore ActiGraph accelerometers with a Bluetooth proximity feature for 7 days. Additionally, parents logged child PA with the family dog(s). Total minutes of dog-facilitated PA and percentage of overall daily moderate to vigorous PA performed with the dog were calculated. Results: Twelve children (mean age = 7.8 ± 2.9 years) participated. There was high feasibility, with 100% retention, valid device data (at least 4 days ≥6-hr wear time), and completion of daily parent log and questionnaire packets. On average, dog-facilitated PA contributed 22.9% (9.2 min) and 15.1% (7.3 min) of the overall daily moderate to vigorous PA for children according to Bluetooth proximity data and parent report, respectively. Conclusions: This pilot study demonstrated the feasibility of utilizing an accelerometer with a proximity feature to quantify dog-facilitated PA. Future research should use this protocol with a larger, more diverse sample to determine whether dog-facilitated PA contributes a clinically significant amount toward overall PA in dog-owning youth.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Montoye, Kimberly A. Clevenger, Benjamin D. Boudreaux, Michael D. Schmidt
Accelerometers are increasingly used to measure 24-hr movement behaviors but are sometimes removed intermittently (e.g., for sleep or bathing), resulting in missing data. This study compared physical behaviors between times a hip-placed accelerometer was worn versus not worn in a college student sample. Participants (n = 115) wore a hip-placed ActiGraph during waking times and a thigh-placed activPAL continuously for at least 7 days (mean ± SD 7.5 ± 1.1 days). Thirteen nonwear algorithms determined ActiGraph nonwear; days included in the analysis had to have at least 1 min where the ActiGraph classified nonwear while participant was classified as awake by the activPAL. activPAL data for steps, time in sedentary behaviors (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) from ActiGraph wear times were then compared with activPAL data from ActiGraph nonwear times. Participants took more steps (10.2–11.8 steps/min) and had higher proportions of MVPA (5.0%–5.9%) during ActiGraph wear time than nonwear time (3.1–8.0 steps/min, 0.8%–1.3% in MVPA). Effects were variable for SB (62.6%–66.9% of wear, 45.5%–76.2% of nonwear) and LPA (28.2%–31.5% of wear, 23.0%–53.2% of nonwear) depending on nonwear algorithm. Rescaling to a 12-hr day reduced SB and LPA error but increased MVPA error. Requiring minimum wear time (e.g., 600 min/day) reduced error but resulted in 10%–22% of days removed as invalid. In conclusion, missing data had minimal effect on MVPA but resulted in underestimation of SB and LPA. Strategies like scaling SB and LPA, but not MVPA, may improve physical behavior estimates from incomplete accelerometer data.
{"title":"Understanding Physical Behaviors During Periods of Accelerometer Wear and Nonwear in College Students","authors":"A. Montoye, Kimberly A. Clevenger, Benjamin D. Boudreaux, Michael D. Schmidt","doi":"10.1123/jmpb.2023-0034","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0034","url":null,"abstract":"Accelerometers are increasingly used to measure 24-hr movement behaviors but are sometimes removed intermittently (e.g., for sleep or bathing), resulting in missing data. This study compared physical behaviors between times a hip-placed accelerometer was worn versus not worn in a college student sample. Participants (n = 115) wore a hip-placed ActiGraph during waking times and a thigh-placed activPAL continuously for at least 7 days (mean ± SD 7.5 ± 1.1 days). Thirteen nonwear algorithms determined ActiGraph nonwear; days included in the analysis had to have at least 1 min where the ActiGraph classified nonwear while participant was classified as awake by the activPAL. activPAL data for steps, time in sedentary behaviors (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) from ActiGraph wear times were then compared with activPAL data from ActiGraph nonwear times. Participants took more steps (10.2–11.8 steps/min) and had higher proportions of MVPA (5.0%–5.9%) during ActiGraph wear time than nonwear time (3.1–8.0 steps/min, 0.8%–1.3% in MVPA). Effects were variable for SB (62.6%–66.9% of wear, 45.5%–76.2% of nonwear) and LPA (28.2%–31.5% of wear, 23.0%–53.2% of nonwear) depending on nonwear algorithm. Rescaling to a 12-hr day reduced SB and LPA error but increased MVPA error. Requiring minimum wear time (e.g., 600 min/day) reduced error but resulted in 10%–22% of days removed as invalid. In conclusion, missing data had minimal effect on MVPA but resulted in underestimation of SB and LPA. Strategies like scaling SB and LPA, but not MVPA, may improve physical behavior estimates from incomplete accelerometer data.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138614754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christophe Dausin, Sergio Ruiz-Carmona, Ruben De Bosscher, Kristel Janssens, Lieven Herbots, Hein Heidbuchel, Peter Hespel, Véronique Cornelissen, Rik Willems, André La Gerche, Guido Claessen, _ _
Background : Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies. Methods : Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week. Results : Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p < .01), but correlated strongly ( r = .81, p < .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse ( r = .92, p < .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions ( r = .20) and training hours ( r = −.12) reported by questionnaires. Conclusion : Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.
背景:尽管耐力运动员以电子方式记录他们的训练数据，但运动心脏病学的研究人员依靠问卷调查来量化训练负荷。这是由于量化大量训练文件的复杂性。我们的目标是开发一种半自动后处理工具来量化临床研究中的训练负荷。方法:收集两项前瞻性运动员心脏研究(运动员大师心脏研究和前瞻性运动员心脏研究)的训练数据。使用内部开发的软件，最大心率(MaxHR)和训练负荷从累积训练期间监测的心率计算出来。将实验室测得的MaxHR与现场测得的MaxHR进行比较。比较基于个人运动强度区的Lucia训练冲动评分和基于野外MaxHR的Edwards训练冲动评分。使用问卷来确定每周的培训课程和培训时数。结果:43名运动员使用佩戴在胸前的心率监测器记录了他们的训练过程，并被选中进行分析。实验室的MaxHR明显低于现场的MaxHR(183±12 bpm比188±13 bpm, p <.01)，但相关性强(r = .81, p <.01)具有可接受的一致性限制(±15.4 bpm)。Lucia训练冲动评分与Edwards训练冲动评分有极好的相关性(r = 0.92, p <。)。量化的培训课时数和培训时数与问卷报告的培训课时数(r = 0.20)和培训时数(r = - 0.12)不相关。结论:训练负荷半自动测量在大年龄组是可行的。与客观的训练负荷量化相比，标准的运动问卷不够准确。
{"title":"Semiautomatic Training Load Determination in Endurance Athletes","authors":"Christophe Dausin, Sergio Ruiz-Carmona, Ruben De Bosscher, Kristel Janssens, Lieven Herbots, Hein Heidbuchel, Peter Hespel, Véronique Cornelissen, Rik Willems, André La Gerche, Guido Claessen, _ _","doi":"10.1123/jmpb.2023-0016","DOIUrl":"https://doi.org/10.1123/jmpb.2023-0016","url":null,"abstract":"Background : Despite endurance athletes recording their training data electronically, researchers in sports cardiology rely on questionnaires to quantify training load. This is due to the complexity of quantifying large numbers of training files. We aimed to develop a semiautomatic postprocessing tool to quantify training load in clinical studies. Methods : Training data were collected from two prospective athlete’s heart studies (Master Athlete’s Heart study and Prospective Athlete Heart study). Using in-house developed software, maximal heart rate (MaxHR) and training load were calculated from heart rate monitored during cumulative training sessions. The MaxHR in the lab was compared with the MaxHR in the field. Lucia training impulse score, based on individually based exercise intensity zones, and Edwards training impulse, based on MaxHR in the field, were compared. A questionnaire was used to determine the number of training sessions and training hours per week. Results : Forty-three athletes recorded their training sessions using a chest-worn heart rate monitor and were selected for this analysis. MaxHR in the lab was significantly lower compared with MaxHR in the field (183 ± 12 bpm vs. 188 ± 13 bpm, p < .01), but correlated strongly ( r = .81, p < .01) with acceptable limits of agreement (±15.4 bpm). An excellent correlation was found between Lucia training impulse score and Edwards training impulse ( r = .92, p < .0001). The quantified number of training sessions and training hours did not correlate with the number of training sessions ( r = .20) and training hours ( r = −.12) reported by questionnaires. Conclusion : Semiautomatic measurement of training load is feasible in a wide age group. Standard exercise questionnaires are insufficiently accurate in comparison to objective training load quantification.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135347919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01Epub Date: 2023-08-30DOI: 10.1123/jmpb.2023-0011
Paul R Hibbing, Jordan A Carlson, Stacey L Simon, Edward L Melanson, Seth A Creasy
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 to Actiwatch, resulting in longer overnight intervals (by 1.49 hours/night for youth and 0.67 hours/night for adults; both p < 0.001). 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 < 0.001). Overnight intervals for both devices tended to overlap for lengthy periods (mean of 7.38 hours/night for youth and 7.69 hours/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 versus wrist movement for Actiwatch).
{"title":"Convergent validity of time in bed estimates from activPAL and Actiwatch in free-living youth and adults.","authors":"Paul R Hibbing, Jordan A Carlson, Stacey L Simon, Edward L Melanson, Seth A Creasy","doi":"10.1123/jmpb.2023-0011","DOIUrl":"10.1123/jmpb.2023-0011","url":null,"abstract":"<p><p>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 to Actiwatch, resulting in longer overnight intervals (by 1.49 hours/night for youth and 0.67 hours/night for adults; both p < 0.001). 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 < 0.001). Overnight intervals for both devices tended to overlap for lengthy periods (mean of 7.38 hours/night for youth and 7.69 hours/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 versus wrist movement for Actiwatch).</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82599724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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