E. Inan-Eroglu, Bo-Huei Huang, L. Shepherd, N. Pearson, A. Koster, Peter Palm, P. Cistulli, M. Hamer, E. Stamatakis
Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level.
{"title":"Comparison of a Thigh-Worn Accelerometer Algorithm With Diary Estimates of Time in Bed and Time Asleep: The 1970 British Cohort Study","authors":"E. Inan-Eroglu, Bo-Huei Huang, L. Shepherd, N. Pearson, A. Koster, Peter Palm, P. Cistulli, M. Hamer, E. Stamatakis","doi":"10.1123/JMPB.2020-0033","DOIUrl":"https://doi.org/10.1123/JMPB.2020-0033","url":null,"abstract":"Background: Thigh-worn accelerometers have established reliability and validity for measurement of free-living physical activity-related behaviors. However, comparisons of methods for measuring sleep and time in bed using the thigh-worn accelerometer are rare. The authors compared the thigh-worn accelerometer algorithm that estimates time in bed with the output of a sleep diary (time in bed and time asleep). Methods: Participants (N = 5,498), from the 1970 British Cohort Study, wore an activPAL device on their thigh continuously for 7 days and completed a sleep diary. Bland–Altman plots and Pearson correlation coefficients were used to examine associations between the algorithm derived and diary time in bed and asleep. Results: The algorithm estimated acceptable levels of agreement with time in bed when compared with diary time in bed (mean bias of −11.4 min; limits of agreement −264.6 to 241.8). The algorithm-derived time in bed overestimated diary sleep time (mean bias of 55.2 min; limits of agreement −204.5 to 314.8 min). Algorithm and sleep diary are reasonably correlated (ρ = .48, 95% confidence interval [.45, .52] for women and ρ = .51, 95% confidence interval [.47, .55] for men) and provide broadly comparable estimates of time in bed but not for sleep time. Conclusions: The algorithm showed acceptable estimates of time in bed compared with diary at the group level. However, about half of the participants were outside of the ±30 min difference of a clinically relevant limit at an individual level.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78017928","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}
J. McVeigh, Jennifer Ellis, Caitlin Ross, Kim Tang, Phoebe Wan, Rhiannon E Halse, S. Dhaliwal, D. Kerr, L. Straker
Activity trackers provide real-time sedentary behavior (SB) and physical activity (PA) data enabling feedback to support behavior change. The validity of activity trackers in an obese population in a free-living environment is largely unknown. This study determined the convergent validity of the Fitbit Charge 2 in measuring SB and PA in overweight adults. The participants (n = 59; M ± SD: age = 48 ± 11 years; body mass index = 34 ± 4 kg/m2) concurrently wore a Charge 2 and ActiGraph GT3X+ accelerometer for 8 days. The same waking wear periods were analyzed, and standard cut points for GT3X+ and proprietary algorithms for the Charge 2, together with a daily step count, were used. Associations between outputs, mean difference (MD) and limits of agreement (LOA), and relative differences were assessed. There was substantial association between devices (intraclass correlation coefficients from .504, 95% confidence interval [.287, .672] for SB, to .925, 95% confidence interval [.877, .955] for step count). In comparison to the GT3X+, the Charge 2 overestimated SB (MD = 37, LOA = −129 to 204 min/day), moderate to vigorous PA (MD = 15, LOA = −49 to 79 min/day), and steps (MD = 1,813, LOA = −1,066 to 4,691 steps/day), and underestimated light PA (MD = −32, LOA = −123 to 58 min/day). The Charge 2 may be a useful tool for self-monitoring of SB and PA in an overweight population, as mostly good agreement was demonstrated with the GT3X+. However, there were mean and relative differences, and the implications of these need to be considered for overweight adult populations who are already at risk of being highly sedentary and insufficiently active.
{"title":"Convergent Validity of the Fitbit Charge 2 to Measure Sedentary Behavior and Physical Activity in Overweight and Obese Adults","authors":"J. McVeigh, Jennifer Ellis, Caitlin Ross, Kim Tang, Phoebe Wan, Rhiannon E Halse, S. Dhaliwal, D. Kerr, L. Straker","doi":"10.1123/JMPB.2020-0014","DOIUrl":"https://doi.org/10.1123/JMPB.2020-0014","url":null,"abstract":"Activity trackers provide real-time sedentary behavior (SB) and physical activity (PA) data enabling feedback to support behavior change. The validity of activity trackers in an obese population in a free-living environment is largely unknown. This study determined the convergent validity of the Fitbit Charge 2 in measuring SB and PA in overweight adults. The participants (n = 59; M ± SD: age = 48 ± 11 years; body mass index = 34 ± 4 kg/m2) concurrently wore a Charge 2 and ActiGraph GT3X+ accelerometer for 8 days. The same waking wear periods were analyzed, and standard cut points for GT3X+ and proprietary algorithms for the Charge 2, together with a daily step count, were used. Associations between outputs, mean difference (MD) and limits of agreement (LOA), and relative differences were assessed. There was substantial association between devices (intraclass correlation coefficients from .504, 95% confidence interval [.287, .672] for SB, to .925, 95% confidence interval [.877, .955] for step count). In comparison to the GT3X+, the Charge 2 overestimated SB (MD = 37, LOA = −129 to 204 min/day), moderate to vigorous PA (MD = 15, LOA = −49 to 79 min/day), and steps (MD = 1,813, LOA = −1,066 to 4,691 steps/day), and underestimated light PA (MD = −32, LOA = −123 to 58 min/day). The Charge 2 may be a useful tool for self-monitoring of SB and PA in an overweight population, as mostly good agreement was demonstrated with the GT3X+. However, there were mean and relative differences, and the implications of these need to be considered for overweight adult populations who are already at risk of being highly sedentary and insufficiently active.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84561819","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}
Grainne Hayes, K. Dowd, C. MacDonncha, Alan Donnely
Background: Multiple activity monitors are utilized for the estimation of moderate- to vigorous-intensity physical activity in youth. Due to differing methodological approaches, results are not comparable when developing thresholds for the determination of moderate- to vigorous-intensity physical activity. This study aimed to develop and validate count-to-activity thresholds for 1.5, 3, and 6 metabolic equivalents of task in five of the most commonly used activity monitors in adolescent research. Methods: Fifty-two participants (mean age = 16.1 [0.78] years) selected and performed activities of daily living while wearing a COSMED K4b2 and five activity monitors; ActiGraph GT1M, ActiGraph wGT3X-BT, activPAL3 micro, activPAL, and GENEActiv. Receiver-operating-characteristic analysis was used to examine the area under the curve and to define count-to-activity thresholds for the vertical axis (all monitors) and the sum of the vector magnitude (ActiGraph wGT3X-BT and activPAL3 micro) for 15 s (all monitors) and 60 s (ActiGraph monitors) epochs. Results: All developed count-to-activity thresholds demonstrated high levels of sensitivity and specificity. When cross-validated in an independent group (N = 20), high levels of sensitivity and specificity generally remained (≥73.1%, intensity and monitor dependent). Conclusions: This study provides researchers with the opportunity to analyze and cross-compare data from different studies that have not employed the same motion sensors.
{"title":"Simultaneous Validation of Count-to-Activity Thresholds for Five Commonly Used Activity Monitors in Adolescent Research: A Step Toward Data Harmonization","authors":"Grainne Hayes, K. Dowd, C. MacDonncha, Alan Donnely","doi":"10.1123/jmpb.2021-0023","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0023","url":null,"abstract":"Background: Multiple activity monitors are utilized for the estimation of moderate- to vigorous-intensity physical activity in youth. Due to differing methodological approaches, results are not comparable when developing thresholds for the determination of moderate- to vigorous-intensity physical activity. This study aimed to develop and validate count-to-activity thresholds for 1.5, 3, and 6 metabolic equivalents of task in five of the most commonly used activity monitors in adolescent research. Methods: Fifty-two participants (mean age = 16.1 [0.78] years) selected and performed activities of daily living while wearing a COSMED K4b2 and five activity monitors; ActiGraph GT1M, ActiGraph wGT3X-BT, activPAL3 micro, activPAL, and GENEActiv. Receiver-operating-characteristic analysis was used to examine the area under the curve and to define count-to-activity thresholds for the vertical axis (all monitors) and the sum of the vector magnitude (ActiGraph wGT3X-BT and activPAL3 micro) for 15 s (all monitors) and 60 s (ActiGraph monitors) epochs. Results: All developed count-to-activity thresholds demonstrated high levels of sensitivity and specificity. When cross-validated in an independent group (N = 20), high levels of sensitivity and specificity generally remained (≥73.1%, intensity and monitor dependent). Conclusions: This study provides researchers with the opportunity to analyze and cross-compare data from different studies that have not employed the same motion sensors.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88294335","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 : 2021-01-01Epub Date: 2021-02-24DOI: 10.1123/jmpb.2020-0038
Julian Martinez, Autumn Decker, Chi C Cho, Aiden Doherty, Ann M Swartz, John W Staudenmayer, Scott J Strath
Purpose: To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications.
Methods: Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification.
Results: 43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively.
Conclusion: WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.
{"title":"Validation of Wearable Camera Still Images to Assess Posture in Free-Living Conditions.","authors":"Julian Martinez, Autumn Decker, Chi C Cho, Aiden Doherty, Ann M Swartz, John W Staudenmayer, Scott J Strath","doi":"10.1123/jmpb.2020-0038","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0038","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the convergent validity of body worn wearable camera (WC) still-images (IMGs) for determining posture compared with activPAL (AP) classifications.</p><p><strong>Methods: </strong>Participants (n=16, mean age 46.7±23.8yrs, 9F) wore an Autographer WC above the xyphoid process and an AP during three, 2hr free-living visits. IMGs were captured on average 8.47 seconds apart and were annotated with output consisting of events, transitory states, unknown and gaps. Events were annotations that matched AP classifications (sit, stand and move) consisting of at least 3 IMGs, transitory states were posture annotations fewer than 3 IMGs, unknown were IMGs that could not be accurately classified, and gaps were time between annotations. For analyses, annotation and AP output were converted to one-sec epochs and matched second-by-second. Total and average length of visits and events are reported in minutes. Bias and 95% CIs for event posture times from IMGs to AP posture times were calculated to determine accuracy and precision. Confusion matrices using total AP posture times were computed to determine misclassification.</p><p><strong>Results: </strong>43 visits were analyzed with a total visit and event time of 5027.73 and 4237.23 minutes and average visit and event lengths being 116.92 and 98.54 minutes, respectively. Bias was not statistically significant for sitting but significant for standing and movement (0.84, -6.87 and 6.04 minutes). From confusion matrices, IMGs correctly classified sitting, standing and movement 85.69%, 54.87%, and 69.41% of total AP time, respectively.</p><p><strong>Conclusion: </strong>WC IMGs provide a good estimation of overall sitting time but underestimate standing and overestimate movement time. Future work is warranted to improve posture classifications and examine IMG accuracy and precision in assessing activity type behaviors.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":" ","pages":"47-52"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320753/pdf/nihms-1672918.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39274380","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}
Equivalence testing may provide complementary information to more frequently used statistical procedures because it determines whether physical behavior outcomes are statistically equivalent to criterion measures. A caveat of this procedure is the predetermined selection of upper and lower bounds of acceptable error around a specified zone of equivalence. With no clear guidelines available to assist researchers, these equivalence zones are arbitrarily selected. A scoping review of articles implementing equivalence testing was performed to determine the validity of physical behavior outcomes; the aim was to characterize how this procedure has been implemented and to provide recommendations. A literature search from five databases initially identified potentially 1,153 articles which resulted in the acceptance of 19 studies (20 arms) conducted in children/youth and 40 in adults (49 arms). Most studies were conducted in free-living conditions (children/youth = 13 arms; adults = 22 arms) and employed a ±10% equivalence zone. However, equivalence zones ranged from ±3% to ±25% with only a subset using absolute thresholds (e.g., ±1,000 steps/day). If these equivalence zones were increased or decreased by ±5%, 75% (15/20, children/youth) and 71% (35/49, adults), they would have exhibited opposing equivalence test outcomes (i.e., equivalent to nonequivalent or vice versa). This scoping review identifies the heterogeneous usage of equivalence testing in studies examining the accuracy of (in)activity measures. In the absence of evidence-based standardized equivalence criteria, presenting the percentage required to achieve statistical equivalence or using absolute thresholds as a proportion of the SD may be a better practice than arbitrarily selecting zones a priori.
{"title":"Implications and Recommendations for Equivalence Testing in Measures of Movement Behaviors: A Scoping Review","authors":"M. O'Brien","doi":"10.1123/jmpb.2021-0021","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0021","url":null,"abstract":"Equivalence testing may provide complementary information to more frequently used statistical procedures because it determines whether physical behavior outcomes are statistically equivalent to criterion measures. A caveat of this procedure is the predetermined selection of upper and lower bounds of acceptable error around a specified zone of equivalence. With no clear guidelines available to assist researchers, these equivalence zones are arbitrarily selected. A scoping review of articles implementing equivalence testing was performed to determine the validity of physical behavior outcomes; the aim was to characterize how this procedure has been implemented and to provide recommendations. A literature search from five databases initially identified potentially 1,153 articles which resulted in the acceptance of 19 studies (20 arms) conducted in children/youth and 40 in adults (49 arms). Most studies were conducted in free-living conditions (children/youth = 13 arms; adults = 22 arms) and employed a ±10% equivalence zone. However, equivalence zones ranged from ±3% to ±25% with only a subset using absolute thresholds (e.g., ±1,000 steps/day). If these equivalence zones were increased or decreased by ±5%, 75% (15/20, children/youth) and 71% (35/49, adults), they would have exhibited opposing equivalence test outcomes (i.e., equivalent to nonequivalent or vice versa). This scoping review identifies the heterogeneous usage of equivalence testing in studies examining the accuracy of (in)activity measures. In the absence of evidence-based standardized equivalence criteria, presenting the percentage required to achieve statistical equivalence or using absolute thresholds as a proportion of the SD may be a better practice than arbitrarily selecting zones a priori.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88873808","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 : 2021-01-01Epub Date: 2021-04-22DOI: 10.1123/jmpb.2020-0045
Jordan A Carlson, Fatima Tuz-Zahra, John Bellettiere, Nicola D Ridgers, Chelsea Steel, Carolina Bejarano, Andrea Z LaCroix, Dori E Rosenberg, Mikael Anne Greenwood-Hickman, Marta M Jankowska, Loki Natarajan
Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups.
Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health.
Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0-15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods.
Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5-2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.
背景:作者评估了参与者日记与两种自动算法之间的一致性，这些算法应用于activPAL (PAL Technologies Ltd, Glasgow, United Kingdom)数据，用于对三个年龄组的清醒穿着时间进行分类。方法:研究1涉及20名青少年和23名成年人，根据协议，偶尔移除激活pal以产生非磨损期。研究2涉及744名连续佩戴activPAL的老年人。两项研究都涉及多个评估日。在床上、床下和不穿衣服的时间记录在参与者日记中。CREA (PAL处理套件)和ProcessingPAL(二次应用)算法估计出床外磨损时间。研究人员调查了算法和日记之间的第二和一天的一致性，以及久坐变量与自评健康的关系。结果:与日记相比，CREA分类床外磨损时间的总体准确度为89.7%(研究1)至95%(研究2)，ProcessingPAL分类床外磨损时间的总体准确度为89.4%(研究1)至93%(研究2)。两种算法均检测到超过90%的非磨损时间发生在非磨损时间>165 min，结论:自动清醒磨损时间分类算法与非短(≤2.5-2.75 hr)非磨损天数的日志信息相似。由于日志和算法数据都可能存在不准确性，因此最佳实践可能涉及将日志和算法输出集成在一起。
{"title":"Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults.","authors":"Jordan A Carlson, Fatima Tuz-Zahra, John Bellettiere, Nicola D Ridgers, Chelsea Steel, Carolina Bejarano, Andrea Z LaCroix, Dori E Rosenberg, Mikael Anne Greenwood-Hickman, Marta M Jankowska, Loki Natarajan","doi":"10.1123/jmpb.2020-0045","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0045","url":null,"abstract":"<p><strong>Background: </strong>The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups.</p><p><strong>Methods: </strong>Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health.</p><p><strong>Results: </strong>The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while <11% occurring in periods ≤165 min was detected. For the daily variables, the mean absolute errors for each algorithm were generally within 0-15% of the diary mean. Most Spearman correlations were very large (≥.81). The mean absolute errors and correlations were less favorable for days on which any nonwear time had occurred. The associations between sedentary variables and self-rated health were similar across processing methods.</p><p><strong>Conclusion: </strong>The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5-2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.</p>","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"4 2","pages":"151-162"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386818/pdf/nihms-1716010.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39356557","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}
Researchers, practitioners, and public health organizations from around the world are becoming increasingly interested in using data from consumer-grade devices such as smartphones and wearable activity trackers to measure physical activity (PA). Indeed, large-scale, easily accessible, and autonomous data collection concerning PA as well as other health behaviors is becoming ever more attractive. There are several benefits of using consumer-grade devices to collect PA data including the ability to obtain big data, retrospectively as well as prospectively, and to understand individual-level PA patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level PA. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from smartphones and wearable activity trackers to understand large-scale PA patterns and stimulate discussion among the scientific community on what the future holds with respect to PA measurement and surveillance.
{"title":"Should We Use Activity Tracker Data From Smartphones and Wearables to Understand Population Physical Activity Patterns?","authors":"J. Mair, L. Hayes, A. Campbell, N. Sculthorpe","doi":"10.1123/jmpb.2021-0012","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0012","url":null,"abstract":"Researchers, practitioners, and public health organizations from around the world are becoming increasingly interested in using data from consumer-grade devices such as smartphones and wearable activity trackers to measure physical activity (PA). Indeed, large-scale, easily accessible, and autonomous data collection concerning PA as well as other health behaviors is becoming ever more attractive. There are several benefits of using consumer-grade devices to collect PA data including the ability to obtain big data, retrospectively as well as prospectively, and to understand individual-level PA patterns over time and in response to natural events. However, there are challenges related to representativeness, data access, and proprietary algorithms that, at present, limit the utility of this data in understanding population-level PA. In this brief report we aim to highlight the benefits, as well as the limitations, of using existing data from smartphones and wearable activity trackers to understand large-scale PA patterns and stimulate discussion among the scientific community on what the future holds with respect to PA measurement and surveillance.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73975717","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}
M. Lopes, B. Costa, L. Malheiros, R. Costa, Ana C. F. Souza, I. Crochemore-Silva, K. Silva
This study (a) compared accelerometer wear time and compliance between distinct wrist-worn accelerometer data collection plans, (b) analyzed participants’ perception of using accelerometers, and (c) identified sociodemographic and behavioral correlates of accelerometer compliance. A sample of high school students (n = 143) wore accelerometers attached to the wrist by a disposable polyvinyl chloride (PVC) wristband or a reusable fabric wristband for 24 hr over 6 days. Those who wore the reusable fabric band, but not their peers, were instructed to remove the device during water-based activities. Participants answered a questionnaire about sociodemographic and behavioral characteristics and reported their experience wearing the accelerometer. We computed non-wear time and checked participants’ compliance with wear-time criteria (i.e., at least three valid weekdays and one valid weekend day) considering two valid day definitions separately (i.e., at least 16 and 23 hours of accelerometer data). Participants who wore a disposable band had greater compliance compared with those who wore a reusable band for both 16-hr (93% vs. 76%, respectively) and 23-hr valid day definitions (91% vs. 50%, respectively). High schoolers with the following characteristics were less likely to comply with wear time criteria if they (a) engaged in labor-intensive activities, (b) perceived that wearing the monitor hindered their daily activities, or (c) felt ashamed while wearing the accelerometer. In conclusion, the data collection plan composed of using disposable wristbands and not removing the monitor resulted in greater 24-hr accelerometer wear time and compliance. However, a negative experience in using the accelerometer may be a barrier to high schoolers’ adherence to rigorous protocols.
{"title":"Correlates of the Adherence to a 24-hr Wrist-Worn Accelerometer Protocol in a Sample of High School Students","authors":"M. Lopes, B. Costa, L. Malheiros, R. Costa, Ana C. F. Souza, I. Crochemore-Silva, K. Silva","doi":"10.1123/jmpb.2020-0062","DOIUrl":"https://doi.org/10.1123/jmpb.2020-0062","url":null,"abstract":"This study (a) compared accelerometer wear time and compliance between distinct wrist-worn accelerometer data collection plans, (b) analyzed participants’ perception of using accelerometers, and (c) identified sociodemographic and behavioral correlates of accelerometer compliance. A sample of high school students (n = 143) wore accelerometers attached to the wrist by a disposable polyvinyl chloride (PVC) wristband or a reusable fabric wristband for 24 hr over 6 days. Those who wore the reusable fabric band, but not their peers, were instructed to remove the device during water-based activities. Participants answered a questionnaire about sociodemographic and behavioral characteristics and reported their experience wearing the accelerometer. We computed non-wear time and checked participants’ compliance with wear-time criteria (i.e., at least three valid weekdays and one valid weekend day) considering two valid day definitions separately (i.e., at least 16 and 23 hours of accelerometer data). Participants who wore a disposable band had greater compliance compared with those who wore a reusable band for both 16-hr (93% vs. 76%, respectively) and 23-hr valid day definitions (91% vs. 50%, respectively). High schoolers with the following characteristics were less likely to comply with wear time criteria if they (a) engaged in labor-intensive activities, (b) perceived that wearing the monitor hindered their daily activities, or (c) felt ashamed while wearing the accelerometer. In conclusion, the data collection plan composed of using disposable wristbands and not removing the monitor resulted in greater 24-hr accelerometer wear time and compliance. However, a negative experience in using the accelerometer may be a barrier to high schoolers’ adherence to rigorous protocols.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83400986","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}
Nicholas R. Lamoureux, P. Hibbing, Charles Matthews, G. Welk
Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.
{"title":"Integration of Report-Based Methods to Enhance the Interpretation of Monitor-Based Research: Results From the Free-Living Activity Study for Health Project","authors":"Nicholas R. Lamoureux, P. Hibbing, Charles Matthews, G. Welk","doi":"10.1123/jmpb.2021-0029","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0029","url":null,"abstract":"Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85973950","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}
Emily W Flanagan, N. Broskey, R. Regterschot, M. Hellemons, J. Aerts, Sarah Richardson, L. Allan, A. Yarnall, X. Janssen, A. Okely, Mohammad Sorowar Hossain, Katherine L. McKee, K. Pfeiffer, Amber Pearson, Andrea Moosreiner, S. Burkart, R. Dugger, Hannah Parker, R. Weaver, B. Armstrong, E. Adams, Paul Jacob, R. Marchand, Andrew Meyer, E. Hampp, Elaine Justice, K. Taylor, Kelly Luttazi, M. Verstraete, Ricardo Antunes
The gold-standards for measuring energy expenditure (EE) under laboratory and free-living settings are whole-room indirect calorimeters and doubly labeled water (DLW), respectively These methods of measuring EE are generally used for quantifying differences in EE within individuals or across populations and can also be used as criterion measures to develop and validate wearable activity monitors for estimating EE Conversely, there can be added benefits of integrating wearable devices in EE studies using room calorimetry and DLW In EE studies aimed at measuring total EE, device-based measures add a dimension of context due to the fine temporal resolution and sensitivity to detect movement intensity which can be used to parse the individual contributors to total EE The focus of this workshop is to introduce the when, why, and how to integrate wearables to EE studies using room calorimeters and DLW For example, wearable monitors can be utilized during room calorimetry to better inform components of EE (resting, thermic effect of feeding, activity, etc ) Doubly labeled water studies give an average estimate of total daily energy expenditure over an assessment period Pairing wearable monitors with DLW, researchers can gain insight into day-to-day, weekday vs weekend, or inter-day variability in physical activity which may influence overall EE 1 Using wearable activity monitors in metabolic and nutritional studies This talk will cover the scope of how activity monitors have been used in different types of applications such as controlled trials and natural histories 2 Adding wearable activity monitors to whole-room indirect calorimetry studies This talk will present the methodology of room calorimetry, and the components of daily EE that wearables can help to quantify (e g , sleep, resting, activity, Detecting hotspots for physical activity using accelerometry, GPS and GIS BACKGROUND AND AIM: Daily physical activity is not one behavior that takes place in one location; it consists of many different behaviors occurring in different locations To get a better understanding of the correlates and determinants of physical activity behavior, knowing in which context it occurs can add valuable additional information With the emerging of methods to combine accelerometer and global positioning system (GPS) The aim of this presentation is to explain how the process of identifying physical activity hotspots works, and demonstrate the method using examples from several studies conducted in Australia and Denmark METHODS: Data were collected among school-children in Denmark and preschool children in Australia using an accelerometer (ActiGraph GT3X or Axivity) and a GPS (Qstarz BT-Q1000X) for 7 days (5 week days, 2 weekend days) to determine their level of activity and movement patterns The GPS position was recorded every 15 seconds and their activity level was recorded and 100Hz and compiled into 15 second epochs Data were merged and processed using HABITUS, an online
{"title":"The 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement","authors":"Emily W Flanagan, N. Broskey, R. Regterschot, M. Hellemons, J. Aerts, Sarah Richardson, L. Allan, A. Yarnall, X. Janssen, A. Okely, Mohammad Sorowar Hossain, Katherine L. McKee, K. Pfeiffer, Amber Pearson, Andrea Moosreiner, S. Burkart, R. Dugger, Hannah Parker, R. Weaver, B. Armstrong, E. Adams, Paul Jacob, R. Marchand, Andrew Meyer, E. Hampp, Elaine Justice, K. Taylor, Kelly Luttazi, M. Verstraete, Ricardo Antunes","doi":"10.1123/jmpb.2021-0036","DOIUrl":"https://doi.org/10.1123/jmpb.2021-0036","url":null,"abstract":"The gold-standards for measuring energy expenditure (EE) under laboratory and free-living settings are whole-room indirect calorimeters and doubly labeled water (DLW), respectively These methods of measuring EE are generally used for quantifying differences in EE within individuals or across populations and can also be used as criterion measures to develop and validate wearable activity monitors for estimating EE Conversely, there can be added benefits of integrating wearable devices in EE studies using room calorimetry and DLW In EE studies aimed at measuring total EE, device-based measures add a dimension of context due to the fine temporal resolution and sensitivity to detect movement intensity which can be used to parse the individual contributors to total EE The focus of this workshop is to introduce the when, why, and how to integrate wearables to EE studies using room calorimeters and DLW For example, wearable monitors can be utilized during room calorimetry to better inform components of EE (resting, thermic effect of feeding, activity, etc ) Doubly labeled water studies give an average estimate of total daily energy expenditure over an assessment period Pairing wearable monitors with DLW, researchers can gain insight into day-to-day, weekday vs weekend, or inter-day variability in physical activity which may influence overall EE 1 Using wearable activity monitors in metabolic and nutritional studies This talk will cover the scope of how activity monitors have been used in different types of applications such as controlled trials and natural histories 2 Adding wearable activity monitors to whole-room indirect calorimetry studies This talk will present the methodology of room calorimetry, and the components of daily EE that wearables can help to quantify (e g , sleep, resting, activity, Detecting hotspots for physical activity using accelerometry, GPS and GIS BACKGROUND AND AIM: Daily physical activity is not one behavior that takes place in one location; it consists of many different behaviors occurring in different locations To get a better understanding of the correlates and determinants of physical activity behavior, knowing in which context it occurs can add valuable additional information With the emerging of methods to combine accelerometer and global positioning system (GPS) The aim of this presentation is to explain how the process of identifying physical activity hotspots works, and demonstrate the method using examples from several studies conducted in Australia and Denmark METHODS: Data were collected among school-children in Denmark and preschool children in Australia using an accelerometer (ActiGraph GT3X or Axivity) and a GPS (Qstarz BT-Q1000X) for 7 days (5 week days, 2 weekend days) to determine their level of activity and movement patterns The GPS position was recorded every 15 seconds and their activity level was recorded and 100Hz and compiled into 15 second epochs Data were merged and processed using HABITUS, an online","PeriodicalId":73572,"journal":{"name":"Journal for the measurement of physical behaviour","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78700112","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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