Introduction: Wrist-worn accelerometer has gained popularity recently in commercial and research use for physical activity tracking. Yet, no consensus exists for standardized wrist-worn data processing, and physical activity data derived from wrist-worn accelerometer cannot be directly compared with data derived from the historically used hip-worn accelerometer. In this work, through a systematic review, we aim to identify and analyze discrepancies between wrist-worn versus hip-worn ActiGraph accelerometers in measuring adult physical activity. Methods: A systematic review was conducted on studies involving free-living data comparison between hip- and wrist-worn ActiGraph accelerometers among adult users. We assessed the population, study protocols, data processing criteria (axis, epoch, wear-time correction, etc.), and outcome measures (step count, sedentary activity time, moderate-to-vigorous physical activity, etc.). Step count and activity count discrepancy were analyzed using meta-analysis, while meta-analysis was not attempted for others due to heterogeneous data processing criteria among the studies. Results: We screened 235 studies with 19 studies qualifying for inclusion in the systematic review. Through meta-analysis, the wrist-worn sensor recorded, on average, 3,537 steps/day more than the hip-worn sensor. Regarding sedentary activity time and moderate-to-vigorous physical activity estimation, the wrist sensor consistently overestimates moderate-to-vigorous physical activity time while underestimating sedentary activity time, with discrepancies ranging from a dozen minutes to several hours. Discussions: Our findings quantified the substantial discrepancies between wrist and hip sensors. It calls attention to the need for a cautious approach to interpreting data from different wear locations. These results may also serve as a reference for data comparisons among studies using different sensor locations.
{"title":"A Comparison of Wrist- Versus Hip-Worn ActiGraph Sensors for Assessing Physical Activity in Adults: A Systematic Review","authors":"Nolan Gall, R. Sun, M. Smuck","doi":"10.1123/jmpb.2021-0045","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0045","url":null,"abstract":"Introduction: Wrist-worn accelerometer has gained popularity recently in commercial and research use for physical activity tracking. Yet, no consensus exists for standardized wrist-worn data processing, and physical activity data derived from wrist-worn accelerometer cannot be directly compared with data derived from the historically used hip-worn accelerometer. In this work, through a systematic review, we aim to identify and analyze discrepancies between wrist-worn versus hip-worn ActiGraph accelerometers in measuring adult physical activity. Methods: A systematic review was conducted on studies involving free-living data comparison between hip- and wrist-worn ActiGraph accelerometers among adult users. We assessed the population, study protocols, data processing criteria (axis, epoch, wear-time correction, etc.), and outcome measures (step count, sedentary activity time, moderate-to-vigorous physical activity, etc.). Step count and activity count discrepancy were analyzed using meta-analysis, while meta-analysis was not attempted for others due to heterogeneous data processing criteria among the studies. Results: We screened 235 studies with 19 studies qualifying for inclusion in the systematic review. Through meta-analysis, the wrist-worn sensor recorded, on average, 3,537 steps/day more than the hip-worn sensor. Regarding sedentary activity time and moderate-to-vigorous physical activity estimation, the wrist sensor consistently overestimates moderate-to-vigorous physical activity time while underestimating sedentary activity time, with discrepancies ranging from a dozen minutes to several hours. Discussions: Our findings quantified the substantial discrepancies between wrist and hip sensors. It calls attention to the need for a cautious approach to interpreting data from different wear locations. These results may also serve as a reference for data comparisons among studies using different sensor locations.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85762596","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: The purpose of this study was to evaluate the validity of the step count and Active Minutes features of the Garmin Vivofit Jr. 2 consumer activity monitor. Methods: Participants included 35 students (age 8–11) enrolled in an after-school physical activity (PA) and nutrition program. Participants wore an ActiGraph GT3x+ monitor on their waist and the Vivofit monitor on their wrist during the PA portion of the program. Data were collected across multiple sessions, resulting in 158 unique pairs of data. Pearson correlation, mean absolute percent error, and equivalence testing were performed to compare step count and minutes of activity (Vivofit Active Minutes vs ActiGraph moderate to vigorous PA) between the two monitors. Results: Moderate correlations were found between the monitors for steps (r = .65) and minutes (r = .43). Mean absolute percent error was 26% for steps and 43% for minutes, suggesting that there were high amounts of individual error. Equivalence testing showed significant agreement between the monitors for steps (p = .046), but not for minutes (p = .98). Conclusion: The Garmin Vivofit Jr. 2 shows acceptable validity for measurement of steps at a group level in a field-based setting, although the amount of individual variability must be considered. The Vivofit Jr. 2 was not valid for measurement of minutes of activity.
目的:本研究的目的是评估Garmin Vivofit Jr. 2消费者活动监测仪的步数和活动分钟特征的有效性。方法:参与者包括35名参加课后体育活动(PA)和营养计划的学生(8-11岁)。在项目的PA部分，参与者在腰上戴着ActiGraph GT3x+监测器，手腕上戴着Vivofit监测器。在多个会话中收集数据，产生158对独特的数据。进行Pearson相关性、平均绝对百分比误差和等效检验来比较两个监测器之间的步数和活动分钟数(Vivofit Active minutes vs ActiGraph中度至剧烈PA)。结果:步数(r = 0.65)与分钟数(r = 0.43)之间存在中度相关性。步数的平均绝对误差为26%，分钟数的平均绝对误差为43%，这表明存在很大的个人误差。等效性检验显示，监测器之间在步数(p = 0.046)上有显著的一致性，但在分钟数(p = 0.98)上没有显著的一致性。结论:Garmin Vivofit Jr. 2在以现场为基础的环境中，在群体水平上测量步数显示出可接受的有效性，尽管必须考虑个体变异的量。Vivofit Jr. 2不适用于测量活动分钟数。
{"title":"Validity of the Garmin Vivofit Jr. to Measure Physical Activity During a Youth After-School Program","authors":"K. Peyer, Kara C. Hamilton","doi":"10.1123/jmpb.2021-0039","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0039","url":null,"abstract":"Purpose: The purpose of this study was to evaluate the validity of the step count and Active Minutes features of the Garmin Vivofit Jr. 2 consumer activity monitor. Methods: Participants included 35 students (age 8–11) enrolled in an after-school physical activity (PA) and nutrition program. Participants wore an ActiGraph GT3x+ monitor on their waist and the Vivofit monitor on their wrist during the PA portion of the program. Data were collected across multiple sessions, resulting in 158 unique pairs of data. Pearson correlation, mean absolute percent error, and equivalence testing were performed to compare step count and minutes of activity (Vivofit Active Minutes vs ActiGraph moderate to vigorous PA) between the two monitors. Results: Moderate correlations were found between the monitors for steps (r = .65) and minutes (r = .43). Mean absolute percent error was 26% for steps and 43% for minutes, suggesting that there were high amounts of individual error. Equivalence testing showed significant agreement between the monitors for steps (p = .046), but not for minutes (p = .98). Conclusion: The Garmin Vivofit Jr. 2 shows acceptable validity for measurement of steps at a group level in a field-based setting, although the amount of individual variability must be considered. The Vivofit Jr. 2 was not valid for measurement of minutes of activity.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76085090","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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：