scholarly journals Productivity and Health: Physical Activity as a Measure of Effort

2020 ◽  
Author(s):  
Oladele Akogun ◽  
Andrew Dillon ◽  
Jed Friedman ◽  
Ashesh Prasann ◽  
Pieter Serneels
Keyword(s):  
Physical Activity ◽  
Fixed Time ◽  
Agricultural Workers ◽  
Activity Measurement ◽  
Accelerometer Data ◽  
Piece Rate ◽  
Time Period ◽  
Individual Productivity ◽  
Activity Measures ◽  
The Impact

Abstract This paper examines the relationship between physical activity and individual productivity among agricultural workers paid on a piece-rate basis. In the context studied, physical activity has a clear correspondence with worker effort. Agricultural workers’ physical activity is directly observed from accelerometer data and is robustly associated with their daily productivity. In addition the impact of a health intervention, which provides malaria testing and treatment, on physical activity and productivity, indicates that the increased daily productivity of workers who are offered this program is explained by worker effort reallocation from low-intensity to high-intensity work within a fixed time period. This demonstrates, in settings when individual productivity is observed, that physical activity measures can help disentangle productivity effects due to effort. When productivity is unobserved, physical activity measures may proxy for individual productivity in physically demanding tasks. The challenges and limitations of physical activity measurement using accelerometers is discussed including their potential use for alternative contexts and the importance of field and data analysis protocols.

Impact of the implantable cardioverter defibrillator on confidence to undertake physical activity in inherited heart disease: A cross-sectional study

2017 ◽  
Vol 16 (8) ◽  
pp. 742-752 ◽  
Author(s):  
Joanna Sweeting ◽  
Kylie Ball ◽  
Julie McGaughran ◽  
John Atherton ◽  
Christopher Semsarian ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Disease ◽  
Implantable Cardioverter Defibrillator ◽  
Vigorous Physical Activity ◽  
Survey Study ◽  
Accelerometer Data ◽  
Cross Sectional ◽  
Physical Activity Guidelines ◽  
Cardioverter Defibrillator ◽  
The Impact

Background: Physical activity is associated with improved quality of life. Patients with an implantable cardioverter defibrillator (ICD) face unique clinical and psychological challenges. Factors such as fear of ICD shock may negatively impact on physical activity, while a sense of protection gained from the ICD may instil confidence to be active. Aim: We aimed to examine the impact of an ICD on physical activity levels and factors associated with amount of activity. Methods: Two cross-sectional studies were conducted. Accelerometer data (seven-day) was collected in March–November 2015 for 63 consecutively recruited hypertrophic cardiomyopathy patients, with or without an ICD, aged ⩾18 years. A survey study was conducted in July–August 2016 of 155 individuals aged ⩾18 years with an inherited heart disease and an ICD in situ. Results: Based on the International Physical Activity Questionnaire, mean leisure time physical activity was 239 ± 300 min/week with 51% meeting physical activity guidelines. Accelerometry showed that mean moderate–vigorous physical activity was the same for patients with and without an ICD (254 ± 139 min/week versus 300 ± 150 min/week, p=0.23). Nearly half of survey participants ( n=73) said their device made them more confident to exercise. Being anxious about ICD shocks was the only factor associated with not meeting physical activity guidelines. Conclusions: Patients with inherited heart disease adjust differently to their ICD device, and for many it has no impact on physical activity. Discussion regarding the appropriate level of physical activity and potential barriers will ensure best possible outcomes in this unique patient group.

scholarly journals Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution?

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Kristin Meseck ◽  
Marta M. Jankowska ◽  
Jasper Schipperijn ◽  
Loki Natarajan ◽  
Suneeta Godbole ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Linear Mixed Model ◽  
Vigorous Physical Activity ◽  
Gps Data ◽  
Positioning System ◽  
Accelerometer Data ◽  
Generalised Linear Mixed Model ◽  
Significant Difference ◽  
The Impact

The main purpose of the present study was to assess the impact of global positioning system (GPS) signal lapse on physical activity analyses, discover any existing associations between missing GPS data and environmental and demographics attributes, and to determine whether imputation is an accurate and viable method for correcting GPS data loss. Accelerometer and GPS data of 782 participants from 8 studies were pooled to represent a range of lifestyles and interactions with the built environment. Periods of GPS signal lapse were identified and extracted. Generalised linear mixed models were run with the number of lapses and the length of lapses as outcomes. The signal lapses were imputed using a simple ruleset, and imputation was validated against person-worn camera imagery. A final generalised linear mixed model was used to identify the difference between the amount of GPS minutes pre- and post-imputation for the activity categories of sedentary, light, and moderate-to-vigorous physical activity. Over 17% of the dataset was comprised of GPS data lapses. No strong associations were found between increasing lapse length and number of lapses and the demographic and built environment variables. A significant difference was found between the pre- and postimputation minutes for each activity category. No demographic or environmental bias was found for length or number of lapses, but imputation of GPS data may make a significant difference for inclusion of physical activity data that occurred during a lapse. Imputing GPS data lapses is a viable technique for returning spatial context to accelerometer data and improving the completeness of the dataset.

Classification and Processing of 24-Hour Wrist Accelerometer Data

2018 ◽  
Vol 1 (2) ◽  
pp. 51-59 ◽  
Author(s):  
Anna Pulakka ◽  
Eric J. Shiroma ◽  
Tamara B. Harris ◽  
Jaana Pentti ◽  
Jussi Vahtera ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Time ◽  
Total Activity ◽  
Sleep Time ◽  
Accelerometer Data ◽  
Classification Methods ◽  
Wake Time ◽  
Wear Time ◽  
Aging Study ◽  
Activity Measures

Background: An important step in accelerometer data analysis is the classification of continuous, 24-hour data into sleep, wake, and non-wear time. We compared classification times and physical activity metrics across different data processing and classification methods.Methods: Participants (n = 576) from the Finnish Retirement and Aging Study (FIREA) wore an accelerometer on their non-dominant wrist for seven days and nights and filled in daily logs with sleep and waking times. Accelerometer data were first classified as sleep or wake time by log, and Tudor-Locke, Tracy, and ActiGraph algorithms. Then, wake periods were classified as wear or non-wear by log, Choi algorithm, and wear sensor. We compared time classification (sleep, wake, and wake wear time) as well as physical activity measures (total activity volume and sedentary time) across these classification methods.Results:M(SD) nightly sleep time was 467 (49) minutes by log and 419 (88), 522 (86), and 453 (74) minutes by Tudor-Locke, Tracy, and ActiGraph algorithms, respectively. Wake wear time did not differ substantially when comparing Choi algorithm and the log. The wear sensor did not work properly in about 29% of the participants. Daily sedentary time varied by 8–81 minutes after excluding sleep by different methods and by 1–18 minutes after excluding non-wear time by different methods. Total activity volume did not substantially differ across the methods.Conclusion: The differences in wear and sedentary time were larger than differences in total activity volume. Methods for defining sleep periods had larger impact on outcomes than methods for defining wear time.

scholarly journals Assessing the Impact of Lifestyle Interventions on Diabetes Prevention in China: A Modeling Approach

2019 ◽  
Vol 16 (10) ◽  
pp. 1677 ◽  
Author(s):  
Linna Luo ◽  
Bowen Pang ◽  
Jian Chen ◽  
Yan Li ◽  
Xiaolei Xie
Keyword(s):  
Physical Activity ◽  
Peer Influence ◽  
Diabetes Prevention ◽  
The United States ◽  
Population Based ◽  
Agent Based ◽  
Individual Health ◽  
Time Period ◽  
Lower Body Weight ◽  
The Impact

China’s diabetes epidemic is getting worse. People with diabetes in China usually have a lower body weight and a different lifestyle profile compared to their counterparts in the United States (US). More and more evidence show that certain lifestyles can possibly be spread from person to person, leading some to propose considering social influence when establishing preventive policies. This study developed an innovative agent-based model of the diabetes epidemic for the Chinese population. Based on the risk factors and related complications of diabetes, the model captured individual health progression, quantitatively described the peer influence of certain lifestyles, and projected population health outcomes over a specific time period. We simulated several hypothetical interventions (i.e., improving diet, controlling smoking, improving physical activity) and assessed their impact on diabetes rates. We validated the model by comparing simulation results with external datasets. Our results showed that improving physical activity could result in the most significant decrease in diabetes prevalence compared to improving diet and controlling smoking. Our model can be used to inform policymakers on how the diabetes epidemic develops and help them compare different diabetes prevention programs in practice.

scholarly journals Validity of the Global Physical Activity Questionnaire (GPAQ) in Adult Latinas

2012 ◽  
Vol 9 (5) ◽  
pp. 698-705 ◽  
Author(s):  
Tracy Hoos ◽  
Nancy Espinoza ◽  
Simon Marshall ◽  
Elva M. Arredondo
Keyword(s):  
Physical Activity ◽  
Self Report ◽  
Accelerometer Data ◽  
Post Intervention ◽  
Activity Intensity ◽  
Time Work ◽  
Global Physical Activity Questionnaire ◽  
Work Commute ◽  
The Impact ◽  
Multilevel Intervention

Background:Valid and reliable self-report measures of physical activity (PA) are needed to evaluate the impact of interventions aimed at increasing the levels of PA. However, few valid measures for assessing PA in Latino populations exist.Objective:The purpose of this study is to determine whether the GPAQ is a valid measure of PA among Latinas and to examine its sensitivity to intervention change. Intervention attendance was also examined.Methods:Baseline and postintervention data were collected from 72 Latinas (mean age = 43.01; SD = 9.05) who participated in Caminando con Fe/Walking with Faith, a multilevel intervention promoting PA among church-going Latinas. Participants completed the GPAQ and were asked to wear the accelerometer for 7 consecutive days at baseline and again 6 months later. Accelerometer data were aggregated into 5 levels of activity intensity (sedentary, light, moderate, moderate-vigorous, and vigorous) and correlated to self-reported mean minutes of PA across several domains (leisure time, work, commute and household chores).Results:There were significant correlations at postintervention between self-reported minutes per week of vigorous LTPA and accelerometer measured vigorous PA (r = .404, P < .001) as well as significant correlations of sensitivity to intervention change (post intervention minus baseline) between self-reported vigorous LTPA and accelerometer-measured vigorous PA (r = .383, P < .003) and self-reported total vigorous PA and accelerometer measured vigorous PA (r = .363, P < .003).Conclusions:The findings from this study suggest that the GPAQ may be useful for evaluating the effectiveness of programs aimed at increasing vigorous levels of PA among Latinas.

scholarly journals PSXIII-14 The impact of estrus intensity on variables associated with fertility in Bos taurus-influenced beef cows

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 362-363
Author(s):  
Reinaldo F Cooke ◽  
Nicola Oosthuizen ◽  
Kelsey Schubach ◽  
Alice Brandão ◽  
Ramiro Oliveira Filho ◽  
...  
Keyword(s):  
Physical Activity ◽  
Bos Taurus ◽  
Fixed Time ◽  
Beef Cows ◽  
Estrus Synchronization ◽  
Estrus Detection ◽  
Follicle Size ◽  
The Impact ◽  
Ovarian Dynamics ◽  
Synchronization Protocol

Abstract To evaluate the impact of estrus expression and intensity on parameters associated with reproductive performance, 219 lactating, multiparous Bos taurus-influenced beef cows were enrolled in this study. Cows were exposed to an estrus synchronization protocol, where they received a 100-µg injection of GnRH and a CIDR insert on d -10, a 25-mg injection of PGF2a at CIDR removal on d -3, and an injection of GnRH 60–66 h following CIDR removal at fixed-time AI (TAI; d 0). Cows were fitted with a pedometer behind their right shoulder on d -10, and an estrus detection patch was applied to their tail-head on d -3. Estrus expression was defined as removal of &gt; 50% of the rub-off coating from the patch on d 0. Net physical activity during estrus was calculated by subtracting daily activity during the non-estrus period (d -10 to -3) from activity during the expected estrus period (d -3 to 0). Cows were classified into 3 groups: cows that did not express estrus (NOESTR; n = 119), cows that expressed estrus with net physical activity greater than the median (HIESTR; n = 50), and the remaining cows (LWESTR; n = 50). Ultrasonography was performed on d -3, 0, and 7 to determine the presence and size of ovarian structures. Pregnancy diagnosis was performed 29 d after TAI. Net physical activity was greater in HIESTR compared to both LWESTR and NOESTR (P &lt; 0.01). Dominant follicle size was greater in HIESTR compared to both LWESTR and NOESTR (P &lt; 0.01). Furthermore, HIESTR had greater corpus luteum volume on d 7 than LWESTR and NOESTR (P &lt; 0.01). Pregnancy rates to AI (PR/AI) were greater in HIESTR and LWESTR compared to NOESTR (P &lt; 0.01). In conclusion, cows that expressed estrus at a greater intensity had improved ovarian dynamics. Additionally, cows that exhibited estrus had greater PR/AI compared to non-estrual cows.

scholarly journals Associations Between Parent Self-Reported and Accelerometer-Measured Physical Activity and Sedentary Time in Children: Ecological Momentary Assessment Study

10.2196/15458 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15458
Author(s):  
Junia N de Brito ◽  
Katie A Loth ◽  
Allan Tate ◽  
Jerica M Berge
Keyword(s):  
Physical Activity ◽  
Ecological Momentary Assessment ◽  
Sedentary Time ◽  
Objective Assessment ◽  
Self Report ◽  
Accelerometer Data ◽  
Interaction Terms ◽  
Time Period ◽  
Ecological Momentary ◽  
Momentary Assessment

Background Retrospective self-report questionnaires are the most common method for assessing physical activity (PA) and sedentary behavior (SB) in children when the use of objective assessment methods (eg, accelerometry) is cost prohibitive. However, self-report measures have limitations (eg, recall bias). The use of real-time, mobile ecological momentary assessment (EMA) has been proposed to address these shortcomings. The study findings will provide useful information for researchers interested in using EMA surveys for measuring PA and SB in children, particularly when reported by a parent or caregiver. Objective This study aimed to examine the associations between the parent’s EMA report of their child’s PA and SB and accelerometer-measured sedentary time (ST), light-intensity PA (LPA), and moderate-to-vigorous–intensity PA (MVPA) and to examine if these associations differed by day of week, sex, and season. Methods A total of 140 parent-child dyads (mean child age 6.4 years, SD 0.8; n=66 girls; n=21 African American; n=24 American Indian; n=25 Hispanic/Latino; n=24 Hmong; n=22 Somali; and n=24 white) participated in this study. During an 8-day period, parents reported child PA and SB via multiple daily signal contingent EMA surveys, and children wore a hip-mounted accelerometer to objectively measure ST, LPA, and MVPA. Accelerometer data was matched to the time period occurring before parent EMA-report of child PA and SB. Generalized estimating equations with interaction-term analyses were performed to determine whether the relationship between parent-EMA report of child PA and SB and accelerometer-measured ST and LPA and MVPA outcomes differed by day of the week, sex and season. Results The parent’s EMA report of their child’s PA and SB was strongly associated with accelerometer-measured ST, LPA, and MVPA. The parent’s EMA report of their child’s PA was stronger during the weekend than on weekdays for accelerometer-measured ST (P≤.001) and LPA (P<.001). For the parent’s EMA report of their child’s SB, strong associations were observed with accelerometer-measured ST (P<.001), LPA (P=.005), and MVPA (P=.008). The findings related to sex-interaction terms indicated that the association between the parent-reported child’s PA via EMA and the accelerometer-measured MVPA was stronger for boys than girls (P=.02). The association between the parent’s EMA report of their child’s PA and SB and accelerometer-measured ST and PA was similar across seasons in this sample (all P values >.31). Conclusions When the use of accelerometry-based methods is not feasible and in contexts where the parent is able to spend more proximate time observing the child’s PA and SB, the parent’s EMA report might be a superior method for measuring PA and SB in young children relative to self-report, given the EMA’s strong associations with accelerometer-measured PA and ST.

scholarly journals Impact of Reduced Sampling Rate on Accelerometer-Based Physical Activity Monitoring and Machine Learning Activity Classification

2021 ◽  
pp. 1-13
Author(s):  
Scott Small ◽  
Sara Khalid ◽  
Paula Dhiman ◽  
Shing Chan ◽  
Dan Jackson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Sampling Rate ◽  
Learning Activity ◽  
Activity Measurement ◽  
Battery Life ◽  
Accelerometer Data ◽  
Wear Time ◽  
Physical Activity Monitoring

Purpose: Lowering the sampling rate of accelerometers in physical activity research can dramatically increase study monitoring periods through longer battery life; however, the effect of reduced sampling rate on activity metric validity is poorly documented. We therefore aimed to assess the effect of reduced sampling rate on measuring physical activity both overall and by specific behavior types. Methods: Healthy adults wore sets of two Axivity AX3 accelerometers on the dominant wrist and hip for 24 hr. At each location one accelerometer recorded at 25 Hz and the other at 100 Hz. Overall acceleration magnitude, time in moderate to vigorous activity, and behavioral activities were calculated and processed using both linear and nearest neighbor resampling. Correlation between acceleration magnitude and activity classifications at both sampling rates was calculated and linear regression was performed. Results: Of the 54 total participants, 45 contributed >20 hr of hip wear time and 51 contributed >20 hr of wrist wear time. Strong correlation was observed between 25- and 100-Hz sampling rates in overall activity measurement (r = .97–.99), yet consistently lower activity was observed in data collected at 25 Hz (3.1%–13.9%). Reduced sleep and light activity and increased sedentary time was classified in 25-Hz data by machine learning models. Discrepancies were greater when linear interpolation resampling was used in postprocessing. Conclusions: The 25- and 100-Hz accelerometer data are highly correlated with predictable differences, which can be accounted for in interstudy comparisons. Sampling rate and resampling methods should be consistently reported in physical activity studies, carefully considered in study design, and tailored to the outcome of interest.

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