scholarly journals Response Conversion for Improving Comparability of International Physical Activity Data

2012 ◽  
Vol 9 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Marijke Hopman-Rock ◽  
Elise Dusseldorp ◽  
Astrid Chorus ◽  
Gert Jacobusse ◽  
Alfred Ruetten ◽  
...  
Keyword(s):  
Physical Activity ◽  
Measurement Instrument ◽  
Principal Component ◽  
Cultural Bias ◽  
Face Validity ◽  
Key Comparisons ◽  
Accelerometer Data ◽  
Activity Data ◽  
Country Culture ◽  
Common Items

Background:Many questionnaires for measuring physical activity (PA) exist. This complicates the comparison of outcomes.Methods:In 8 European countries, PA was measured in random samples of 600 persons, using the IPAQ as a ‘bridge’ to historical sets of country-specific questions. We assume that a unidimensional scale of PA ability exists on which items and respondents can be placed, irrespective of country, culture, background factors, or measurement instrument. Response Conversion (RC) based on Item Response Theory (IRT) was used to estimate such a common PA scale, to compare PA levels between countries, and to create a conversion key. Comparisons were made with Eurobarometer (IPAQ) data.Results:Appropriateness of IRT was supported by the existence of a strong first dimension established by principal component analysis. The IRT analysis resulted in 1 common PA scale with a reasonable fit and face validity. However, evidence for cultural bias (Differential Item Functioning, DIF) was found in all IPAQ items. This result made actual comparison between countries difficult.Conclusions:Response Conversion can improve comparability in the field of PA. RC needs common items that are culturally unbiased. Wide-scale use of RC awaits measures that are more culturally invariant (such as international accelerometer data).

scholarly journals Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study

10.2196/18491 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18491
Author(s):  
Tracy E Crane ◽  
Meghan B Skiba ◽  
Austin Miller ◽  
David O Garcia ◽  
Cynthia A Thomson
Keyword(s):  
Physical Activity ◽  
Ovarian Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Accelerometer Data ◽  
Activity Tracking ◽  
Activity Data ◽  
Convenience Sample ◽  
Time Points ◽  
Activity Measurements

Background The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. Objective Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. Methods From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. Results We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. Conclusions The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

Missing value imputation for physical activity data measured by accelerometer

2016 ◽  
Vol 27 (2) ◽  
pp. 490-506 ◽  
Author(s):  
Jung Ae Lee ◽  
Jeff Gill
Keyword(s):  
Physical Activity ◽  
Count Data ◽  
R Package ◽  
Predictive Distribution ◽  
Epidemiological Studies ◽  
Accelerometer Data ◽  
Activity Data ◽  
Health And Nutrition ◽  
Log Normal ◽  
Over Dispersion

An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person’s activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis more challenging. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation is performed at the minute level and follows the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using 2003−2004 National Health and Nutrition Examination Survey data.

scholarly journals Physical activity patterns and clusters in 1001 patients with COPD

2017 ◽  
Vol 14 (3) ◽  
pp. 256-269 ◽  
Author(s):  
Rafael Mesquita ◽  
Gabriele Spina ◽  
Fabio Pitta ◽  
David Donaire-Gonzalez ◽  
Brenda M Deering ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Airflow Obstruction ◽  
Principal Component ◽  
Forced Expiratory Volume ◽  
Daily Physical Activity ◽  
Chronic Obstructive ◽  
Activity Data ◽  
Obstructive Pulmonary Disease ◽  
Activity Measures

We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV1], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV1, worse dyspnoea and higher ADO index compared to other clusters ( p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.

scholarly journals Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Tracy E Crane ◽  
Meghan B Skiba ◽  
Austin Miller ◽  
David O Garcia ◽  
Cynthia A Thomson
Keyword(s):  
Physical Activity ◽  
Ovarian Cancer ◽  
Data Collection ◽  
Cancer Survivors ◽  
Accelerometer Data ◽  
Activity Tracking ◽  
Activity Data ◽  
Convenience Sample ◽  
Time Points ◽  
Activity Measurements

BACKGROUND The collection of self-reported physical activity using validated questionnaires has known bias and measurement error. OBJECTIVE Accelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control. METHODS From July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns. RESULTS We evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (<i>P</i>&lt;.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer. CONCLUSIONS The high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

scholarly journals The development and initial validation of the Health and Reproductive Survey (HeRS)

2021 ◽  
Vol 17 ◽  
pp. 174550652110048
Author(s):  
Donna Duffy ◽  
Jennifer Yourkavitch ◽  
Georgie Bruinvels ◽  
Nicola J Rinaldi ◽  
Laurie Wideman
Keyword(s):  
Physical Activity ◽  
Principal Component Analysis ◽  
Content Validity ◽  
Principal Component ◽  
Component Analysis ◽  
Face Validity ◽  
Convenience Sample ◽  
Physically Active ◽  
Menstrual Function ◽  
The Face

Background: Due to the diversity in profiles associated with the female reproductive cycle and their potential physiological and psychological effects, monitoring the reproductive status of exercising females is important from a practical and research perspective. Moreover, as physical activity can influence menstrual function, the effects of physical activity energy expenditure on reproductive function should also be considered. Aim: The aim of this study was to develop and establish initial face and content validity of the Health and Reproductive Survey (HeRS) for physically active females, which is a retrospective assessment of menstrual function from menarche (first menstruation) to menopause (cessation of menstruation). Methods: Face validity was evaluated qualitatively, and the initial content validity was established through a principal component analysis. The face validity process was completed by 26 females aged 19–67 years and the content validity was established through a survey sent to a convenience sample of 392 females, of which 230 females (57.9% and aged 18–49 years) completed the survey. Results: The revisions made following the face validation improved the understanding, flow, and coherence of the survey. The principal component analysis indicated that, at a minimum, the survey measures these constructs: menstrual cessation and associated moderators, athletic participation and performance levels (as associated with menstruation change and the menstrual cycle), age and menstrual cessation, hormonal contraception (“birth control”), and menarche and associated moderators. Conclusion: The Health and Reproductive Survey (HeRS) is a partially validated tool that can be used by researchers to characterize the menstrual status of physically active females relative to their physical activity status.

scholarly journals The Weather Impact on Physical Activity of 6–12 Year Old Children: A Clustered Study of the Health Oriented Pedagogical Project (HOPP)

Sports ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Iana Kharlova ◽  
Wei Hai Deng ◽  
Jostein Mamen ◽  
Asgeir Mamen ◽  
Maren Valand Fredriksen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Intervention Program ◽  
Physical Activity Intervention ◽  
Vigorous Physical Activity ◽  
Weather Conditions ◽  
Day Length ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Activity Data ◽  
Weather Impact

It is commonly known that children do not engage in a sufficient amount of physical activity. Weather conditions and day length may influence physical activity of children. Little is known about the relationship between physical activity and seasons. The purpose of this study was to investigate the relationships between weather conditions and physical activity in 6–12 year old children based on hip-worn Actigraph wGT3X–BT accelerometer data. The study sample consisted of 2015 subjects aged 6–12 years from the Health Oriented Pedagogical Project (HOPP) study carried out in Horten municipality and Akershus county, Norway. Six days of sedentary and moderate-to-vigorous physical activity data was gathered in January–June and September–October, 2015, presented as daily averages. The accelerometer-monitored physical activity of children grouped within nine schools was matched with regional weather conditions and assessed with the means of linear mixed models. Increased day length was associated with decreased sedentary behavior. Warmer temperature and dry weather were associated with increased moderate-to-vigorous physical activity after adjusting for age and sex. One-hour increase in daylight resulted in a decrease of sedentary time by, on average, 2 min (95% CI = (−2.577, −0.798)). For every 5 °C increase in temperature (range: −0.95 and 15.51 °C) and dry weather, average moderate-to vigorous physical activity increased by 72 and 67 min (males and females, respectively) (p < 0.001). Days with precipitation had, on average, 10 fewer minutes of moderate-to-vigorous physical activity compared with days without precipitation (95% CI = (−16.704, −3.259)). Higher temperatures and dry weather led to higher physical activity levels, seeing larger increases among boys than girls. A school-based physical activity intervention program should be adjusted regarding local weather conditions in line with the present findings.

scholarly journals Comparability of The Netherlands Physical Activity Questionnaire with Accelerometer-Measured Physical Activity in Samoan Children: A Retrospective Analysis of Ola Tuputupua’e Data

2021 ◽  
Vol 18 (16) ◽  
pp. 8438
Author(s):  
Clara R. Warmath ◽  
Courtney C. Choy ◽  
Elizabeth A. Frame ◽  
Lauren B. Sherar ◽  
Rachel L. Duckham ◽  
...  
Keyword(s):  
Physical Activity ◽  
The Netherlands ◽  
Child Growth ◽  
Cardiometabolic Health ◽  
Activity Measurement ◽  
Accelerometer Data ◽  
Activity Data ◽  
Physical Activity Questionnaire ◽  
Marginal Means ◽  
Activity Questionnaire

Accurate measurement of physical activity is critical to understand its role in cardiometabolic health and obesity development in children and to monitor trends in behavior and evaluate interventions. An ongoing mixed-longitudinal study of child growth and development in Samoa is collecting physical activity data with both accelerometers and the Netherlands Physical Activity Questionnaire (NPAQ). The aims of our analyses were to (1) describe the response frequency and correlations of individual questions in the NPAQ, (2) develop modified NPAQ scores with selected questions and (3) examine the concordance of modified NPAQ scores with accelerometer outcomes among children aged 2–4 years. We developed two modified NPAQ scores with combinations of questions and assessed concordance of the modified scores with accelerometer data using estimated marginal means adjusted for monitor wear time. Although the evenly distributed tertiles of the modified 15-point NPAQ score showed promising trends of increasing minutes of accelerometer-assessed high-intensity physical activity with increasing tertile, the estimated marginal means were imprecise with high variance, demonstrating that NPAQ score could not accurately assess physical activity levels of preschool-aged children in Samoa. Considering that questionnaires are often considered more cost-effective tools for physical activity measurement than accelerometry, further research is necessary to develop a culturally and age-appropriate physical activity questionnaire in this population.

scholarly journals Multiple imputation to deal with missing objectively-measured physical activity data: findings from two cohorts

2021 ◽  
Vol 26 ◽  
pp. 1-8
Author(s):  
Rafaela Costa Martins ◽  
Bruna Gonçalves C. da Silva ◽  
Cauane Blumenberg ◽  
Luiza Isnardi Ricardo ◽  
Shana Ginar da Silva ◽  
...  
Keyword(s):  
Physical Activity ◽  
Multiple Imputation ◽  
Birth Cohort ◽  
Skin Color ◽  
Accelerometer Data ◽  
Activity Data ◽  
Mean Values ◽  
Almost All ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity

The objective of this article was to describe patterns of losses of information regarding accelerometer data and to assess the use of multiple imputation to generate physical activity estimates for individuals without accelerometry data. Two birth cohort studies from Pelotas (Brazil) with participants aged 22 and 11-years old assessed objectively measured physical activity differences between complete and imputed cases. Mean values of overall physical activity for complete cases (n1993 = 2,985 and n2004 = 3,348) and for complete cases plus imputed cases (n1993 = 760 and n2004 = 79) were described according to predictors. Male individuals, participants with black skin color, and less schooled individuals presented higher averages of overall physical activity than their counterparts. Almost all imputed estimates were comparable to the complete cases, and the highest difference found was 0.7 mg for the first quintile of socioeconomic status of the 1993 birth cohort. Multiple imputation is a positive technique to deal with missing data from objectively measured physical activity. It provides a set of relevant variables to be used in order to efficiently predict accelerometer data.

scholarly journals The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

2021 ◽  
Vol 10 (24) ◽  
pp. 5951
Author(s):  
Zan Gao ◽  
Wenxi Liu ◽  
Daniel J. McDonough ◽  
Nan Zeng ◽  
Jung Eun Lee
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
Behavioral Science ◽  
Accelerometer Data ◽  
Free Living ◽  
Activity Data ◽  
Future Studies ◽  
Acceleration Data ◽  
Challenges And Opportunities ◽  
The Times

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals’ energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

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