Pub Date : 2025-01-01Epub Date: 2025-06-10DOI: 10.1123/jmpb.2024-0051
Charles E Matthews, Pedro Saint-Maurice, Joshua R Freeman, Hayden A Hayes, Alaina H Shreves, Aiden Doherty, Eric T Hyde, Katie Ylarregui, Rena R Jones, Sarah K Keadle
Purpose: Given the limited real-world testing of algorithms for wrist-worn sensors to estimate sedentary time, we examined the performance of 21 algorithms in free-living adults.
Methods: Seventy-one adults (35-65 years) wore a GENEActiv (wrist) and an activPAL (thigh) sensor for up to 10 days. activPAL was our reference measure. We estimated sedentary time (hours/day) using 21 classification algorithms, including cut point and machine-learning methods. Valid days from each monitor were matched by date and mean values were calculated. Equivalence testing (±10%) and linear regression were used to compare each algorithm's estimate to the reference, over all participants and by sex and age.
Results: activPAL recorded a mean of 9.4 hours/d sedentary. Five of 21 algorithms (24%) estimated sedentary time within 10% (±0.94 hours) of the reference. Two of these methods employed machine-learning algorithms (Trost Extended, OxWearables) and three employed cut points (GGIR ENMO 40mg; Bakrania ENMO 32.6mg; Fraysse ENMOa 62.5mg). Variance explained in linear regression was relatively high for the machine-learning (R2=0.44-0.63) and cut point algorithms developed for younger (R2=0.30-0.64) and older (R2=0.45-0.66) adults. More accurate performance was noted for algorithms developed in studies using posture-based ground truth measures and conducted in free-living settings.
Conclusion: Fifteen of 21 (71%) algorithms produced estimates of sedentary time that were moderate-strongly correlated with the reference measure, but only five (24%) were within 10% of the reference. Free-living benchmarking studies like this can identify more accurate and precise algorithms to estimate sedentary time and identify characteristics of algorithm development studies that yield better results.
{"title":"Performance evaluation of algorithms to estimate daily sedentary time using wrist-worn sensors in free-living adults.","authors":"Charles E Matthews, Pedro Saint-Maurice, Joshua R Freeman, Hayden A Hayes, Alaina H Shreves, Aiden Doherty, Eric T Hyde, Katie Ylarregui, Rena R Jones, Sarah K Keadle","doi":"10.1123/jmpb.2024-0051","DOIUrl":"https://doi.org/10.1123/jmpb.2024-0051","url":null,"abstract":"<p><strong>Purpose: </strong>Given the limited real-world testing of algorithms for wrist-worn sensors to estimate sedentary time, we examined the performance of 21 algorithms in free-living adults.</p><p><strong>Methods: </strong>Seventy-one adults (35-65 years) wore a GENEActiv (wrist) and an activPAL (thigh) sensor for up to 10 days. activPAL was our reference measure. We estimated sedentary time (hours/day) using 21 classification algorithms, including cut point and machine-learning methods. Valid days from each monitor were matched by date and mean values were calculated. Equivalence testing (±10%) and linear regression were used to compare each algorithm's estimate to the reference, over all participants and by sex and age.</p><p><strong>Results: </strong>activPAL recorded a mean of 9.4 hours/d sedentary. Five of 21 algorithms (24%) estimated sedentary time within 10% (±0.94 hours) of the reference. Two of these methods employed machine-learning algorithms (Trost Extended, OxWearables) and three employed cut points (GGIR ENMO 40mg; Bakrania ENMO 32.6mg; Fraysse ENMOa 62.5mg). Variance explained in linear regression was relatively high for the machine-learning (R<sup>2</sup>=0.44-0.63) and cut point algorithms developed for younger (R<sup>2</sup>=0.30-0.64) and older (R<sup>2</sup>=0.45-0.66) adults. More accurate performance was noted for algorithms developed in studies using posture-based ground truth measures and conducted in free-living settings.</p><p><strong>Conclusion: </strong>Fifteen of 21 (71%) algorithms produced estimates of sedentary time that were moderate-strongly correlated with the reference measure, but only five (24%) were within 10% of the reference. Free-living benchmarking studies like this can identify more accurate and precise algorithms to estimate sedentary time and identify characteristics of algorithm development studies that yield better results.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981283","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}
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":"19 6","pages":""},"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: 2024-11-19DOI: 10.1123/jmpb.2024-0007
J B Gallagher, D E Boonstra, J D Borrowman, M Unke, M A Jones, C E Kline, B Barone Gibbs, K M Whitaker
Introduction: The purpose of this study was to compare estimates of 24-hour activity using the best practice of a thigh accelerometer (activPAL), wrist actigraphy (Actiwatch), and a sleep diary (PAL + watch + diary) to estimates from simpler procedures, such as the thigh accelerometer and diary (PAL + diary) or thigh monitor alone (PAL only) during pregnancy.
Methods: Data collected during the 2nd trimester from 40 randomly selected participants in the Pregnancy 24/7 cohort study were included. activPAL data were integrated with sleep time determined by wrist actigraphy (PAL + watch + diary) or diary-determined sleep (PAL + diary). In the PAL only analysis, average estimates were exported directly from the PAL software. Repeated measures ANOVA and intraclass correlations coefficients compared moderate-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time, sleep, and wear time across measurement approaches. Pairwise comparisons using a Bonferroni correction explored significant differences identified from the omnibus ANOVA.
Results: The three approaches arrived at consistent durations of physical activity (intraclass correlations coefficients > .95) but not for estimating sedentary behavior and sleep durations (intraclass correlations coefficients: .73-.82). PAL + diary overestimated MVPA by 2.3 min/day (p < .01) compared with PAL + diary + watch. PAL only overestimated sleep (25.3-29.0 min/day, p < .01) while underestimating MVPA (11.7-14.0 min/day, p < .01) compared with the other approaches.
Conclusions: Since the inclusion of the wrist actigraphy provided only slight differences in MVPA estimates, PAL + diary may provide acceptable estimates of 24-hour activity during pregnancy in future research. PAL only may be acceptable when exclusively interested in physical activity.
{"title":"Comparing Multiple Approaches to Estimate Physical Activity, Sedentary Behavior, and Sleep in Pregnancy.","authors":"J B Gallagher, D E Boonstra, J D Borrowman, M Unke, M A Jones, C E Kline, B Barone Gibbs, K M Whitaker","doi":"10.1123/jmpb.2024-0007","DOIUrl":"10.1123/jmpb.2024-0007","url":null,"abstract":"<p><strong>Introduction: </strong>The purpose of this study was to compare estimates of 24-hour activity using the best practice of a thigh accelerometer (activPAL), wrist actigraphy (Actiwatch), and a sleep diary (<i>PAL + watch + diary</i>) to estimates from simpler procedures, such as the thigh accelerometer and diary (<i>PAL + diary</i>) or thigh monitor alone (<i>PAL only</i>) during pregnancy.</p><p><strong>Methods: </strong>Data collected during the 2<sup>nd</sup> trimester from 40 randomly selected participants in the Pregnancy 24/7 cohort study were included. activPAL data were integrated with sleep time determined by wrist actigraphy (<i>PAL + watch + diary</i>) or diary-determined sleep (<i>PAL + diary</i>). In the <i>PAL only</i> analysis, average estimates were exported directly from the PAL software. Repeated measures ANOVA and intraclass correlations coefficients compared moderate-vigorous physical activity (MVPA), light physical activity (LPA), sedentary time, sleep, and wear time across measurement approaches. Pairwise comparisons using a Bonferroni correction explored significant differences identified from the omnibus ANOVA.</p><p><strong>Results: </strong>The three approaches arrived at consistent durations of physical activity (intraclass correlations coefficients > .95) but not for estimating sedentary behavior and sleep durations (intraclass correlations coefficients: .73-.82). PAL + diary overestimated MVPA by 2.3 min/day (p < .01) compared with PAL + diary + watch. PAL only overestimated sleep (25.3-29.0 min/day, p < .01) while underestimating MVPA (11.7-14.0 min/day, p < .01) compared with the other approaches.</p><p><strong>Conclusions: </strong>Since the inclusion of the wrist actigraphy provided only slight differences in MVPA estimates, <i>PAL + diary</i> may provide acceptable estimates of 24-hour activity during pregnancy in future research. <i>PAL only</i> may be acceptable when exclusively interested in physical activity.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416457","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}
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":"7 1","pages":""},"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}
Pub Date : 2024-01-01Epub Date: 2024-04-26DOI: 10.1123/jmpb.2023-0037
Brian C Helsel, Paul R Hibbing, Robert N Montgomery, Eric D Vidoni, Lauren T Ptomey, Jonathan Clutton, Richard A Washburn
Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph's Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package agcounts and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph's lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of agcounts and other open-source software.
{"title":"<i>agcounts</i>: An R Package to Calculate ActiGraph Activity Counts From Portable Accelerometers.","authors":"Brian C Helsel, Paul R Hibbing, Robert N Montgomery, Eric D Vidoni, Lauren T Ptomey, Jonathan Clutton, Richard A Washburn","doi":"10.1123/jmpb.2023-0037","DOIUrl":"10.1123/jmpb.2023-0037","url":null,"abstract":"<p><p>Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph's Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package <i>agcounts</i> and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph's lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of <i>agcounts</i> and other open-source software.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11636644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820310","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":"32 37","pages":""},"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":"11 1","pages":""},"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}
Pub Date : 2024-01-01Epub Date: 2024-09-10DOI: 10.1123/jmpb.2024-0002
Kelly R Evenson, Annie Green Howard, Fang Wen, Chongzhi Di, I-Min Lee
Understanding the simultaneous patterning of accelerometer-measured physical activity and sedentary behavior (physical behaviors) can inform targeted interventions. This cross-sectional study described multi-component patterns and correlates of physical behaviors using accelerometry among diverse postmenopausal women. The Women's Health Accelerometry Collaboration combined two United States-based cohorts of postmenopausal women with similar accelerometry protocols and measures. Women (n=22,612) 62 to 97 years enrolled in the Women's Health Study (n=16,742) and the Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study (n=5870) wore an ActiGraph GT3X+ accelerometer on their hip for one week. Awake-time accelerometry data were summarized using the accelerometer activity index into sedentary behavior, light (low, high), and moderate-to-vigorous physical activity. Latent class analysis was used to classify physical behavior hour-by-hour. Five unique patterns were identified with higher total volume of physical activity and lower sedentary behavior with each successively higher-class number based on percentage of the day in physical activity/sedentary behavior per hour over seven days. The percentage assignment was 16.3% class 1, 33.9% class 2, 20.2% class 3, 18.0% class 4, and 11.7% class 5. Median posterior probabilities ranged from 0.99-1.00. Younger age, higher education and general health, normal weight, never smokers, weekly drinking, and faster self-reported walking speed generally had higher class assignment compared to their counterparts. History of diabetes and cardiovascular disease generally had lower class assignment compared to those without these conditions. These results can inform targeted interventions based on common patterns of physical behaviors by time of day among postmenopausal women.
{"title":"Identifying Multicomponent Patterns and Correlates of Accelerometry-Assessed Physical Behaviors Among Postmenopausal Women: The Women's Health Accelerometry Collaboration.","authors":"Kelly R Evenson, Annie Green Howard, Fang Wen, Chongzhi Di, I-Min Lee","doi":"10.1123/jmpb.2024-0002","DOIUrl":"10.1123/jmpb.2024-0002","url":null,"abstract":"<p><p>Understanding the simultaneous patterning of accelerometer-measured physical activity and sedentary behavior (physical behaviors) can inform targeted interventions. This cross-sectional study described multi-component patterns and correlates of physical behaviors using accelerometry among diverse postmenopausal women. The Women's Health Accelerometry Collaboration combined two United States-based cohorts of postmenopausal women with similar accelerometry protocols and measures. Women (n=22,612) 62 to 97 years enrolled in the Women's Health Study (n=16,742) and the Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study (n=5870) wore an ActiGraph GT3X+ accelerometer on their hip for one week. Awake-time accelerometry data were summarized using the accelerometer activity index into sedentary behavior, light (low, high), and moderate-to-vigorous physical activity. Latent class analysis was used to classify physical behavior hour-by-hour. Five unique patterns were identified with higher total volume of physical activity and lower sedentary behavior with each successively higher-class number based on percentage of the day in physical activity/sedentary behavior per hour over seven days. The percentage assignment was 16.3% class 1, 33.9% class 2, 20.2% class 3, 18.0% class 4, and 11.7% class 5. Median posterior probabilities ranged from 0.99-1.00. Younger age, higher education and general health, normal weight, never smokers, weekly drinking, and faster self-reported walking speed generally had higher class assignment compared to their counterparts. History of diabetes and cardiovascular disease generally had lower class assignment compared to those without these conditions. These results can inform targeted interventions based on common patterns of physical behaviors by time of day among postmenopausal women.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"7 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12352512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877148","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}
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":"109 4","pages":""},"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}
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":"52 30","pages":""},"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}
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