Kimberly A. Clevenger, K. Mackintosh, M. McNarry, K. Pfeiffer, Alexander Montoye, J. Brønd
ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.
{"title":"Comparison of Child and Adolescent Physical Activity Levels From Open-Source Versus ActiGraph Counts","authors":"Kimberly A. Clevenger, K. Mackintosh, M. McNarry, K. Pfeiffer, Alexander Montoye, J. Brønd","doi":"10.1123/jmpb.2021-0057","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0057","url":null,"abstract":"ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90801584","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}
Liam P. Pellerine, D. Kimmerly, J. Fowles, M. O'Brien
The Physical Activity Vital Sign (PAVS) is a two-question assessment used to estimate habitual moderate to vigorous aerobic physical activity (MVPA). Previous studies have shown active adults cannot estimate the physical activity intensity properly. The initial purpose was to investigate the criterion validity of the PAVS for quantifying habitual MVPA in young adults meeting weekly MVPA guidelines (n = 140; 21 ± 3 years). A previously validated PiezoRx waist-worn accelerometer served as the criterion measure (wear time, 6.7 ± 0.6 days). All participants completed the PAVS once before wearing the PiezoRx. Standardized activity monitor validation procedures were followed. The PAVS (201 ± 142 min/week) underestimated (p < .001) MVPA compared to the PiezoRx (381 ± 155 min/week). To correct for this large error, the sample was divided into calibration model development (n = 70; 21 ± 3 years) and criterion validation (n = 70; 21 ± 3 years) groups. The PAVS score, age, gender, and body mass index outcomes from the development group were used to construct a multiple linear regression model-based calibrated PAVS (cPAVS) equation. In the validation group, the cPAVS was similar (p = .113; 352 ± 23 min/week) compared to accelerometry. Equivalence testing demonstrated the cPAVS, but not the PAVS, was equivalent to the PiezoRx. Despite achieving most statistical criteria, the PAVS and cPAVS still had high degrees of variability, preventing their use on an individual level. Alternative strategies are needed for the PAVS in an active young adult population. These results caution using the PAVS in active young adults and identify a case where obvious variabilities in accuracy conflict with statistically congruent results.
{"title":"Calibrating the Physical Activity Vital Sign to Estimate Habitual Moderate to Vigorous Physical Activity More Accurately in Active Young Adults: A Cautionary Tale","authors":"Liam P. Pellerine, D. Kimmerly, J. Fowles, M. O'Brien","doi":"10.1123/jmpb.2021-0055","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0055","url":null,"abstract":"The Physical Activity Vital Sign (PAVS) is a two-question assessment used to estimate habitual moderate to vigorous aerobic physical activity (MVPA). Previous studies have shown active adults cannot estimate the physical activity intensity properly. The initial purpose was to investigate the criterion validity of the PAVS for quantifying habitual MVPA in young adults meeting weekly MVPA guidelines (n = 140; 21 ± 3 years). A previously validated PiezoRx waist-worn accelerometer served as the criterion measure (wear time, 6.7 ± 0.6 days). All participants completed the PAVS once before wearing the PiezoRx. Standardized activity monitor validation procedures were followed. The PAVS (201 ± 142 min/week) underestimated (p < .001) MVPA compared to the PiezoRx (381 ± 155 min/week). To correct for this large error, the sample was divided into calibration model development (n = 70; 21 ± 3 years) and criterion validation (n = 70; 21 ± 3 years) groups. The PAVS score, age, gender, and body mass index outcomes from the development group were used to construct a multiple linear regression model-based calibrated PAVS (cPAVS) equation. In the validation group, the cPAVS was similar (p = .113; 352 ± 23 min/week) compared to accelerometry. Equivalence testing demonstrated the cPAVS, but not the PAVS, was equivalent to the PiezoRx. Despite achieving most statistical criteria, the PAVS and cPAVS still had high degrees of variability, preventing their use on an individual level. Alternative strategies are needed for the PAVS in an active young adult population. These results caution using the PAVS in active young adults and identify a case where obvious variabilities in accuracy conflict with statistically congruent results.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86307171","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}
The consequences of multiple sclerosis (MS), particularly gait and walking dysfunction, may obfuscate (i.e., make unclear in meaning) the measurement of physical activity using body-worn motion sensors, notably accelerometers. This paper is based on an invited keynote lecture given at the 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement, June 2022, and provides an overview of studies applying accelerometers for the measurement of physical activity behavior in MS. The overview includes initial research uncovering a conundrum with the interpretation of activity counts from accelerometers as a measure of physical activity. It then reviews research on calibration of accelerometer output based on its association with energy expenditure in yielding a biologically based metric for studying physical activity in MS. The paper concludes with other applications and lessons learned for guiding future research on physical activity measurement using accelerometry in MS and other populations with neurological diseases and conditions.
{"title":"Measurement of Physical Activity Using Accelerometry in Persons With Multiple Sclerosis","authors":"R. Motl","doi":"10.1123/jmpb.2022-0029","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0029","url":null,"abstract":"The consequences of multiple sclerosis (MS), particularly gait and walking dysfunction, may obfuscate (i.e., make unclear in meaning) the measurement of physical activity using body-worn motion sensors, notably accelerometers. This paper is based on an invited keynote lecture given at the 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement, June 2022, and provides an overview of studies applying accelerometers for the measurement of physical activity behavior in MS. The overview includes initial research uncovering a conundrum with the interpretation of activity counts from accelerometers as a measure of physical activity. It then reviews research on calibration of accelerometer output based on its association with energy expenditure in yielding a biologically based metric for studying physical activity in MS. The paper concludes with other applications and lessons learned for guiding future research on physical activity measurement using accelerometry in MS and other populations with neurological diseases and conditions.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84432231","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}
Mia S. Tackney, D. Ståhl, Elizabeth A. Williamson, J. Carpenter
In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.
{"title":"Missing Step Count Data? Step Away From the Expectation–Maximization Algorithm","authors":"Mia S. Tackney, D. Ståhl, Elizabeth A. Williamson, J. Carpenter","doi":"10.1123/jmpb.2022-0002","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0002","url":null,"abstract":"In studies that compare physical activity between groups of individuals, it is common for physical activity to be quantified by step count, which is measured by accelerometers or other wearable devices. Missing step count data often arise in these settings and can lead to bias or imprecision in the estimated effect if handled inappropriately. Replacing each missing value in accelerometer data with a single value using the Expectation–Maximization (EM) algorithm has been advocated in the literature, but it can lead to underestimation of variances and could seriously compromise study conclusions. We compare the performance in terms of bias and variance of two missing data methods, the EM algorithm and Multiple Imputation (MI), through a simulation study where data are generated from a parametric model to reflect characteristics of a trial on physical activity. We also conduct a reanalysis of the 2019 MOVE-IT trial. The EM algorithm leads to an underestimate of the variance of effects of interest, in both the simulation study and the reanalysis of the MOVE-IT trial. MI should be the preferred approach to handling missing data in accelerometer, which provides valid point and variance estimates.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84885446","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, Olivia Coolman, Amberly Keyes, Megan Ready, Jaedyn Shelton, Ethan Willett, Brian C. Rider
Background: Given the popularity of thigh-worn accelerometers, it is important to understand their reliability and validity. Purpose: Our study evaluated laboratory validity and free-living intermonitor reliability of the Fibion monitor and free-living intermonitor reliability of the activPAL monitor. Free-living comparability of the Fibion and activPAL monitors was also assessed. Methods: Nineteen adult participants wore Fibion monitors on both thighs while performing 11 activities in a laboratory setting. Then, participants wore Fibion and activPAL monitors on both thighs for 3 days during waking hours. Accuracy of the Fibion monitor was determined for recognizing lying/sitting, standing, slow walking, fast walking, jogging, and cycling. For the 3-day free-living wear, outputs from the Fibion monitors were compared, with similar analyses conducted for the activPAL monitors. Finally, free-living comparability of the Fibion and activPAL monitors was determined for nonwear, sitting, standing, stepping, and cycling. Results: The Fibion monitor had an overall accuracy of 85%–89%, with high accuracy (94%–100%) for detecting prone and supine lying, sitting, and standing but some misclassification among ambulatory activities and for left-/right-side lying with standing. Intermonitor reliability was similar for the Fibion and activPAL monitors, with best reliability for sitting but poorer reliability for activities performed least often (e.g., cycling). The Fibion and activPAL monitors were not equivalent for most tested metrics. Conclusion: The Fibion monitor appears suitable for assessment of sedentary and nonsedentary waking postures, and the Fibion and activPAL monitors have comparable intermonitor reliability. However, studies using thigh-worn monitors should use the same monitor brand worn on the same leg to optimize reliability.
{"title":"Evaluation of Two Thigh-Worn Accelerometer Brands in Laboratory and Free-Living Settings","authors":"A. Montoye, Olivia Coolman, Amberly Keyes, Megan Ready, Jaedyn Shelton, Ethan Willett, Brian C. Rider","doi":"10.1123/jmpb.2022-0012","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0012","url":null,"abstract":"Background: Given the popularity of thigh-worn accelerometers, it is important to understand their reliability and validity. Purpose: Our study evaluated laboratory validity and free-living intermonitor reliability of the Fibion monitor and free-living intermonitor reliability of the activPAL monitor. Free-living comparability of the Fibion and activPAL monitors was also assessed. Methods: Nineteen adult participants wore Fibion monitors on both thighs while performing 11 activities in a laboratory setting. Then, participants wore Fibion and activPAL monitors on both thighs for 3 days during waking hours. Accuracy of the Fibion monitor was determined for recognizing lying/sitting, standing, slow walking, fast walking, jogging, and cycling. For the 3-day free-living wear, outputs from the Fibion monitors were compared, with similar analyses conducted for the activPAL monitors. Finally, free-living comparability of the Fibion and activPAL monitors was determined for nonwear, sitting, standing, stepping, and cycling. Results: The Fibion monitor had an overall accuracy of 85%–89%, with high accuracy (94%–100%) for detecting prone and supine lying, sitting, and standing but some misclassification among ambulatory activities and for left-/right-side lying with standing. Intermonitor reliability was similar for the Fibion and activPAL monitors, with best reliability for sitting but poorer reliability for activities performed least often (e.g., cycling). The Fibion and activPAL monitors were not equivalent for most tested metrics. Conclusion: The Fibion monitor appears suitable for assessment of sedentary and nonsedentary waking postures, and the Fibion and activPAL monitors have comparable intermonitor reliability. However, studies using thigh-worn monitors should use the same monitor brand worn on the same leg to optimize reliability.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"217 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74950925","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}
Therese Lockenwitz Petersen, J. Brønd, E. Benfeldt, R. Jepsen
Background: Tape-mounted Axivity AX3 accelerometers are increasingly being used to monitor physical activity of individuals, but studies on the integrity and performance of diffe1rent attachment protocols are missing. Purpose: The purpose of this paper was to evaluate four attachment protocols with respect to skin reactions, adhesion, and wear time in children and adults using tape-mounted Axivity AX3 accelerometers and to evaluate the associated ease of handling. Methods: We used data from the Danish household-based population study, the Lolland-Falster Health Study. Participants were instructed to wear accelerometers for seven consecutive days and to complete a questionnaire on skin reactions and issues relating to adhesion. A one-way analysis of variance was used to examine differences in skin reactions and adhesion between the protocols. A Tukey post hoc test compared group means. Ease of handling was assessed throughout the data collection. Results: In total, 5,389 individuals were included (1,289 children and 4,100 adults). For both children and adults, skin reactions were most frequent in Protocols 1 and 2. Adhesion problems were most frequent in Protocol 3. Wear time was longest in Protocol 4. Skin reactions and adhesion problems were more frequent in children compared to adults. Adults achieved longest wear time. Discussion: Covering the skin completely with adhesive tape seemed to cause skin reactions. Too short pieces of fixation tape caused accelerometers to fall off. Protocols necessitating removal of remains of glue on the accelerometers required a lot of work. Conclusion: The last of the four protocols was superior in respect to skin reactions, adhesion, wear time, and ease of handling.
{"title":"Integrity and Performance of Four Tape Solutions for Mounting Accelerometry Devices: Lolland-Falster Health Study","authors":"Therese Lockenwitz Petersen, J. Brønd, E. Benfeldt, R. Jepsen","doi":"10.1123/jmpb.2022-0024","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0024","url":null,"abstract":"Background: Tape-mounted Axivity AX3 accelerometers are increasingly being used to monitor physical activity of individuals, but studies on the integrity and performance of diffe1rent attachment protocols are missing. Purpose: The purpose of this paper was to evaluate four attachment protocols with respect to skin reactions, adhesion, and wear time in children and adults using tape-mounted Axivity AX3 accelerometers and to evaluate the associated ease of handling. Methods: We used data from the Danish household-based population study, the Lolland-Falster Health Study. Participants were instructed to wear accelerometers for seven consecutive days and to complete a questionnaire on skin reactions and issues relating to adhesion. A one-way analysis of variance was used to examine differences in skin reactions and adhesion between the protocols. A Tukey post hoc test compared group means. Ease of handling was assessed throughout the data collection. Results: In total, 5,389 individuals were included (1,289 children and 4,100 adults). For both children and adults, skin reactions were most frequent in Protocols 1 and 2. Adhesion problems were most frequent in Protocol 3. Wear time was longest in Protocol 4. Skin reactions and adhesion problems were more frequent in children compared to adults. Adults achieved longest wear time. Discussion: Covering the skin completely with adhesive tape seemed to cause skin reactions. Too short pieces of fixation tape caused accelerometers to fall off. Protocols necessitating removal of remains of glue on the accelerometers required a lot of work. Conclusion: The last of the four protocols was superior in respect to skin reactions, adhesion, wear time, and ease of handling.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79107354","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}
Purpose: To determine accuracy of activPAL Technologies’ CREA algorithm to assess bedtime, wake time, and sleep time. Methods: As part of a larger study, 104 participants recorded nightly sleep logs (LOGs) and wore the activPAL accelerometer at the thigh and ActiGraph accelerometer at the hip for 24 hr/day, for seven consecutive days. For sleep LOGs, participants recorded nightly bed and daily wake times. Previously validated ActiGraph, proprietary activPAL, and the Winkler sleep algorithm were used to compute sleep variables. Eighty-seven participants provided 2+ days of valid data. Pearson correlations, paired samples t tests, and equivalency tests were used to examine relationships and differences between methods (activPAL vs. ActiGraph, activPAL vs. LOG, and activPAL vs. Winkler algorithm). Results: For screened data, moderately high to high correlations but significant mean differences were found between activPAL versus ActiGraph for bedtime (t86 = −6.80, p ≤ .01, r = .84), wake time (t86 = 4.80, p ≤ .01, r = .93), and sleep time (t86 = 7.99, p ≤ .01, r = .88). activPAL versus LOG comparisons also yielded significant mean differences and moderately high to high correlations for bedtime (t86 = −4.68, p ≤ .01, r = .82), wake time (t86 = 8.14, p ≤ .01, r = .93), and sleep time (t86 = 8.60, p ≤ .01, r = .72). Equivalency testing revealed that equivalency could not be claimed between activPAL versus LOG or activPAL versus ActiGraph comparisons, though the activPAL and Winkler algorithm were equivalent. Conclusion: The activPAL algorithm overestimated sleep time by detecting earlier bedtimes and later wake times. Because of the significant differences between algorithms, bedtime, wake time, and sleep time are not interchangeable between methods.
目的:确定activPAL Technologies的CREA算法评估就寝时间、清醒时间和睡眠时间的准确性。方法:作为一项更大规模研究的一部分，104名参与者记录了夜间睡眠日志(log)，并连续7天每天24小时在大腿上佩戴activPAL加速计，在臀部佩戴ActiGraph加速计。对于睡眠日志，参与者记录了每晚的睡眠时间和每天醒来的时间。使用先前验证的ActiGraph、专有的activPAL和Winkler睡眠算法来计算睡眠变量。87名参与者提供了2天以上的有效数据。使用Pearson相关性、配对样本t检验和等效性检验来检查方法之间的关系和差异(activPAL与ActiGraph、activPAL与LOG、activPAL与Winkler算法)。结果:对于筛选的数据，activPAL与ActiGraph在就寝时间之间存在中度至高度相关性，但平均差异显著(t86 = - 6.80, p≤)。01, r = 0.84)，唤醒时间(t86 = 4.80, p≤。0.01, r = 0.93)，睡眠时间(t86 = 7.99, p≤。01, r = .88)。activPAL与LOG的比较也产生了显著的平均差异和中度至高度的相关性(t86 = - 4.68, p≤)。01, r = .82)，唤醒时间(t86 = 8.14, p≤。0.01, r = .93)，睡眠时间(t86 = 8.60, p≤。01, r = .72)。等效性测试显示，尽管activPAL和Winkler算法是等效的，但activPAL与LOG或activPAL与ActiGraph之间的比较不能声称等效性。结论:activPAL算法通过检测较早的就寝时间和较晚的起床时间而高估了睡眠时间。由于算法之间的显著差异，就寝时间、醒来时间和睡眠时间在方法之间是不可互换的。
{"title":"Validity of a Novel Algorithm to Detect Bedtime, Wake Time, and Sleep Time in Adults","authors":"Kyle R. Leister, J. Garay, T. Barreira","doi":"10.1123/jmpb.2021-0027","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0027","url":null,"abstract":"Purpose: To determine accuracy of activPAL Technologies’ CREA algorithm to assess bedtime, wake time, and sleep time. Methods: As part of a larger study, 104 participants recorded nightly sleep logs (LOGs) and wore the activPAL accelerometer at the thigh and ActiGraph accelerometer at the hip for 24 hr/day, for seven consecutive days. For sleep LOGs, participants recorded nightly bed and daily wake times. Previously validated ActiGraph, proprietary activPAL, and the Winkler sleep algorithm were used to compute sleep variables. Eighty-seven participants provided 2+ days of valid data. Pearson correlations, paired samples t tests, and equivalency tests were used to examine relationships and differences between methods (activPAL vs. ActiGraph, activPAL vs. LOG, and activPAL vs. Winkler algorithm). Results: For screened data, moderately high to high correlations but significant mean differences were found between activPAL versus ActiGraph for bedtime (t86 = −6.80, p ≤ .01, r = .84), wake time (t86 = 4.80, p ≤ .01, r = .93), and sleep time (t86 = 7.99, p ≤ .01, r = .88). activPAL versus LOG comparisons also yielded significant mean differences and moderately high to high correlations for bedtime (t86 = −4.68, p ≤ .01, r = .82), wake time (t86 = 8.14, p ≤ .01, r = .93), and sleep time (t86 = 8.60, p ≤ .01, r = .72). Equivalency testing revealed that equivalency could not be claimed between activPAL versus LOG or activPAL versus ActiGraph comparisons, though the activPAL and Winkler algorithm were equivalent. Conclusion: The activPAL algorithm overestimated sleep time by detecting earlier bedtimes and later wake times. Because of the significant differences between algorithms, bedtime, wake time, and sleep time are not interchangeable between methods.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"87 2 Suppl 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88693515","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}
P. Husu, K. Tokola, H. Vähä-Ypyä, H. Sievänen, T. Vasankari
Background: Depression is a significant health problem, whereas higher physical activity (PA) associates with fewer depressive symptoms. We examined how self-reported depressive symptoms are associated with accelerometer-measured PA, standing, sedentary behavior, and time in bed (TIB) among 20- to 69-year-old men and women. Methods: The study is a part of the cross-sectional, population-based FinFit2017 study, in which depressive symptoms were assessed by modified nine-item Finnish version of the Patient Health Questionnaire, and physical behavior in terms of PA, sedentary behavior, standing, and TIB was assessed 24/7 by a triaxial accelerometer. During waking hours, the accelerometer was hip worn. Intensity of PA was analyzed by mean amplitude deviation and body posture by angle for posture estimation algorithms. During TIB, the device was wrist worn, and the analysis was based on the wrist movements. A total of 1,823 participants answered the nine-item Finnish version of the Patient Health Questionnaire and used the accelerometer 24 hr at least 4 days per week. Results: Men without depressive symptoms had on average more standing, light, and moderate to vigorous PA and steps, and less low and high movement TIB than the men with at least moderate symptoms, when age group, education, work status, marital status, and fitness were adjusted for. The asymptomatic women had more moderate to vigorous PA and steps and less high movement TIB than the women with at least moderate symptoms. Conclusions: Depressive symptoms were associated with lower levels of PA and longer TIB. It is important to identify these symptoms as early as possible to be able to initiate and target preventive actions, including PA promotion, to these symptomatic persons on time.
{"title":"Depressive Symptoms Are Associated With Accelerometer-Measured Physical Activity and Time in Bed Among Working-Aged Men and Women","authors":"P. Husu, K. Tokola, H. Vähä-Ypyä, H. Sievänen, T. Vasankari","doi":"10.1123/jmpb.2021-0058","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0058","url":null,"abstract":"Background: Depression is a significant health problem, whereas higher physical activity (PA) associates with fewer depressive symptoms. We examined how self-reported depressive symptoms are associated with accelerometer-measured PA, standing, sedentary behavior, and time in bed (TIB) among 20- to 69-year-old men and women. Methods: The study is a part of the cross-sectional, population-based FinFit2017 study, in which depressive symptoms were assessed by modified nine-item Finnish version of the Patient Health Questionnaire, and physical behavior in terms of PA, sedentary behavior, standing, and TIB was assessed 24/7 by a triaxial accelerometer. During waking hours, the accelerometer was hip worn. Intensity of PA was analyzed by mean amplitude deviation and body posture by angle for posture estimation algorithms. During TIB, the device was wrist worn, and the analysis was based on the wrist movements. A total of 1,823 participants answered the nine-item Finnish version of the Patient Health Questionnaire and used the accelerometer 24 hr at least 4 days per week. Results: Men without depressive symptoms had on average more standing, light, and moderate to vigorous PA and steps, and less low and high movement TIB than the men with at least moderate symptoms, when age group, education, work status, marital status, and fitness were adjusted for. The asymptomatic women had more moderate to vigorous PA and steps and less high movement TIB than the women with at least moderate symptoms. Conclusions: Depressive symptoms were associated with lower levels of PA and longer TIB. It is important to identify these symptoms as early as possible to be able to initiate and target preventive actions, including PA promotion, to these symptomatic persons on time.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88926022","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}
As smartphone and wearable device ownership increase, interest in their utility to monitor physical activity has risen concurrently. Numerous examples of the application of wearables in clinical and epidemiological research settings already exist. However, whether these devices are all suitable for physical activity surveillance is open for debate. In this article, we discuss four key issues specifically relevant to surveillance that we believe need to be tackled before consumer wearables can be considered for this measurement purpose: representative sampling, representative wear time, validity and reliability, and compatibility between devices. A recurring theme is how to deal with systematic biases by demographic groups. We suggest some potential solutions to the issues of concern such as providing individuals with standardized devices, considering summary metrics of physical activity less prone to wear time biases, and the development of a framework to harmonize estimates between device types and their inbuilt algorithms. We encourage collaborative efforts from researchers and consumer wearable manufacturers in this area. In the meantime, we caution against the use of consumer wearable device data for inference of population-level activity without the consideration of these issues.
{"title":"Considerations for the Use of Consumer-Grade Wearables and Smartphones in Population Surveillance of Physical Activity","authors":"T. Strain, K. Wijndaele, M. Pearce, S. Brage","doi":"10.1123/jmpb.2021-0046","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0046","url":null,"abstract":"As smartphone and wearable device ownership increase, interest in their utility to monitor physical activity has risen concurrently. Numerous examples of the application of wearables in clinical and epidemiological research settings already exist. However, whether these devices are all suitable for physical activity surveillance is open for debate. In this article, we discuss four key issues specifically relevant to surveillance that we believe need to be tackled before consumer wearables can be considered for this measurement purpose: representative sampling, representative wear time, validity and reliability, and compatibility between devices. A recurring theme is how to deal with systematic biases by demographic groups. We suggest some potential solutions to the issues of concern such as providing individuals with standardized devices, considering summary metrics of physical activity less prone to wear time biases, and the development of a framework to harmonize estimates between device types and their inbuilt algorithms. We encourage collaborative efforts from researchers and consumer wearable manufacturers in this area. In the meantime, we caution against the use of consumer wearable device data for inference of population-level activity without the consideration of these issues.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84347329","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: Running is a popular form of exercise, and its physiological effects are strongly modulated by speed. Accelerometry-based activity monitors are commonly used to measure physical activity in research, but no method exists to estimate running speed from only accelerometer data. Methods: Using three cohorts totaling 72 subjects performing treadmill and outdoor running, we developed linear, ridge, and gradient-boosted tree regression models to estimate running speed from raw accelerometer data from waist- or wrist-worn devices. To assess model performance in a real-world scenario, we deployed the best-performing model to data from 16 additional runners completing a 13-week training program while equipped with waist-worn accelerometers and commercially available foot pods. Results: Linear, ridge, and boosted tree models estimated speed with 12.0%, 11.6%, and 11.2% mean absolute percentage error, respectively, using waist-worn accelerometer data. Errors were greater using wrist-worn data, with linear, ridge, and boosted tree models achieving 13.8%, 14.0%, and 12.8% error. Across 663 free-living runs, speed was significantly associated with run duration (p = .009) and perceived run intensity (p = .008). Speed was nonsignificantly associated with fatigue (p = .07). Estimated speeds differed from foot pod measurements by 7.25%; associations and statistical significance were similar when speed was assessed via accelerometry versus via foot pod. Conclusion: Raw accelerometry data can be used to estimate running speed in free-living data with sufficient accuracy to detect associations with important measures of health and performance. Our approach is most useful in studies where research grade accelerometry is preferable to traditional global positioning system or foot pod-based measurements, such as in large-scale observational studies on physical activity.
{"title":"Estimating Running Speed From Wrist- or Waist-Worn Wearable Accelerometer Data: A Machine Learning Approach","authors":"John J. Davis, Blaise E. Oeding, A. Gruber","doi":"10.1123/jmpb.2022-0011","DOIUrl":"https://doi.org/10.1123/jmpb.2022-0011","url":null,"abstract":"Background: Running is a popular form of exercise, and its physiological effects are strongly modulated by speed. Accelerometry-based activity monitors are commonly used to measure physical activity in research, but no method exists to estimate running speed from only accelerometer data. Methods: Using three cohorts totaling 72 subjects performing treadmill and outdoor running, we developed linear, ridge, and gradient-boosted tree regression models to estimate running speed from raw accelerometer data from waist- or wrist-worn devices. To assess model performance in a real-world scenario, we deployed the best-performing model to data from 16 additional runners completing a 13-week training program while equipped with waist-worn accelerometers and commercially available foot pods. Results: Linear, ridge, and boosted tree models estimated speed with 12.0%, 11.6%, and 11.2% mean absolute percentage error, respectively, using waist-worn accelerometer data. Errors were greater using wrist-worn data, with linear, ridge, and boosted tree models achieving 13.8%, 14.0%, and 12.8% error. Across 663 free-living runs, speed was significantly associated with run duration (p = .009) and perceived run intensity (p = .008). Speed was nonsignificantly associated with fatigue (p = .07). Estimated speeds differed from foot pod measurements by 7.25%; associations and statistical significance were similar when speed was assessed via accelerometry versus via foot pod. Conclusion: Raw accelerometry data can be used to estimate running speed in free-living data with sufficient accuracy to detect associations with important measures of health and performance. Our approach is most useful in studies where research grade accelerometry is preferable to traditional global positioning system or foot pod-based measurements, such as in large-scale observational studies on physical activity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84115352","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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