scholarly journals Measurement of Physical Activity and Sedentary Behavior by Accelerometry Among a Nationwide Sample from the KiGGS and MoMo Study: Study Protocol (Preprint)

2019 ◽  
Author(s):  
Alexander Burchartz ◽  
Kristin Manz ◽  
Bastian Anedda ◽  
Claudia Niessner ◽  
Doris Oriwol ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Children And Adolescents ◽  
Study Protocol ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Activity Data ◽  
National Cohort ◽  
Technical Details ◽  
The Right

BACKGROUND Currently, no nationwide objective physical activity data exists for children and adolescents living in Germany. The German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the Motorik-Modul study (MoMo) is a national cohort study that has incorporated accelerometers in its most recent data collection wave (wave 2, since 2014). This wave 2 marks the first nationwide collection of objective data on the physical activity of children and adolescents living in Germany. OBJECTIVE The purpose of this protocol is to describe the methods used in the KiGGS and MoMo study to capture the intensity, frequency, and duration of physical activity with accelerometers. METHODS Participants (N=11,003, aged 6 to 31 years) were instructed to wear an ActiGraph GT3X+ or wGT3X-BT accelerometer laterally on the right hip. Accelerometers were worn on consecutive days during waking hours, including at least 4 valid weekdays and 1 weekend day (wear time >8 hours) in the evaluation. A nonwear time protocol was also implemented. RESULTS Data collection was completed by October 2017. Data harmonization took place in 2018. The first accelerometer results from this wave were published in 2019, and detailed analyses are ready to be submitted in 2020. CONCLUSIONS This study protocol provides an overview of technical details and basic choices when using accelerometers in large-scale epidemiological studies. At the same time, the restrictions imposed by the specified filters and the evaluation routines must be taken into account. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/14370

scholarly journals Measurement of Physical Activity and Sedentary Behavior by Accelerometry Among a Nationwide Sample from the KiGGS and MoMo Study: Study Protocol

10.2196/14370 ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. e14370 ◽  
Author(s):  
Alexander Burchartz ◽  
Kristin Manz ◽  
Bastian Anedda ◽  
Claudia Niessner ◽  
Doris Oriwol ◽  
...  
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Children And Adolescents ◽  
Study Protocol ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Activity Data ◽  
National Cohort ◽  
Technical Details ◽  
The Right

Background Currently, no nationwide objective physical activity data exists for children and adolescents living in Germany. The German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the Motorik-Modul study (MoMo) is a national cohort study that has incorporated accelerometers in its most recent data collection wave (wave 2, since 2014). This wave 2 marks the first nationwide collection of objective data on the physical activity of children and adolescents living in Germany. Objective The purpose of this protocol is to describe the methods used in the KiGGS and MoMo study to capture the intensity, frequency, and duration of physical activity with accelerometers. Methods Participants (N=11,003, aged 6 to 31 years) were instructed to wear an ActiGraph GT3X+ or wGT3X-BT accelerometer laterally on the right hip. Accelerometers were worn on consecutive days during waking hours, including at least 4 valid weekdays and 1 weekend day (wear time >8 hours) in the evaluation. A nonwear time protocol was also implemented. Results Data collection was completed by October 2017. Data harmonization took place in 2018. The first accelerometer results from this wave were published in 2019, and detailed analyses are ready to be submitted in 2020. Conclusions This study protocol provides an overview of technical details and basic choices when using accelerometers in large-scale epidemiological studies. At the same time, the restrictions imposed by the specified filters and the evaluation routines must be taken into account. International Registered Report Identifier (IRRID) DERR1-10.2196/14370

scholarly journals A roadmap: Social distancing physical activity data collection to protect those with intellectual disabilities

2021 ◽  
pp. 174462952110096
Author(s):  
Whitley J Stone ◽  
Kayla M Baker
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Intellectual Disabilities ◽  
Research Methods ◽  
Virus Transmission ◽  
Future Research ◽  
Activity Data ◽  
Physical Activity Data ◽  
Social Distancing ◽  
Novel Coronavirus

The novel coronavirus may impact exercise habits of those with intellectual disabilities. Due to the mandated discontinuation of face-to-face research, investigators must adapt projects to protect all involved while collecting objective physical activity metrics. This brief report outlines a modification process of research methods to adhere to social distancing mandates present during COVID-19. Actions taken included electronic consent and assent forms, an electronic survey, and mailing an accelerometer with included instructions. The amended research methods were implemented without risk for virus transmission or undue burden on the research team, participant, or caregiver. Recruitment was likely impacted by the coronavirus-mediated quarantine, plausibly resulting in bias. Objective physical activity data collection can be sufficiently modified to protect those with intellectual disabilities and investigators. Future research designs may require greater participant incentives and the creation of in-home participation.

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.

scholarly journals Challenges to school-based physical activity data collection: Reflections from English primary and secondary schools

2020 ◽  
pp. 001789692095909
Author(s):  
Sarah Taylor ◽  
Michael Owen
Keyword(s):  
Physical Activity ◽  
Data Collection ◽  
Early Career ◽  
Resource Utilisation ◽  
Activity Data ◽  
Postgraduate Students ◽  
Good Communication ◽  
School Based ◽  
Efficient Data ◽  
Practical Guidance

Background: Schools are ideal environments in which to conduct child and adolescent physical activity (PA) research. Despite this, PA-specific practical guidance for school-based research is lacking, which may present unique challenges to researchers. Based on reflections from our own experiences, this paper seeks to provide practical guidance on how best to approach school-based PA data collection. Discussion: This paper focuses on the practicalities of quantitative and qualitative data collection in English primary (4–11 years) and secondary (11–16 years) schools. Recruitment and consent are discussed, and practical guidance provided with respect to engagement with parent/carer(s) and ethical considerations. The importance of good communication with schools, together with its importance in facilitating efficient data collection (through planning, data collection and resource utilisation), is described. Finally, the importance of giving back to the school and participants once a research project has been completed is stressed. Summary: Improved understanding of data collection procedures for school-based PA research is key to helping research become more systematic and efficient. Findings in this paper will be particularly useful to undergraduate and postgraduate students and early career researchers.

An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

2021 ◽  
pp. 715-740
Author(s):  
Pooja Parameshwarappa ◽  
Zhiyuan Chen ◽  
Gunes Koru
Keyword(s):  
Physical Activity ◽  
Large Scale ◽  
Health Management ◽  
Health Care Research ◽  
Sequential Data ◽  
Computationally Efficient ◽  
Activity Data ◽  
Cross Sectional ◽  
Physical Activity Data ◽  
Privacy Risks

Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This article presents an effective anonymization approach, multi-level clustering-based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.

scholarly journals Legume consumption and its association with fasting glucose, insulin resistance and type 2 diabetes in the Indian Migration Study

2016 ◽  
Vol 19 (16) ◽  
pp. 3017-3026 ◽  
Author(s):  
Preet K Dhillon ◽  
Liza Bowen ◽  
Sanjay Kinra ◽  
Ankalmadugu Venkatsubbareddy Bharathi ◽  
Sutapa Agrawal ◽  
...  
Keyword(s):  
Physical Activity ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Large Scale ◽  
Fasting Glucose ◽  
Epidemiological Studies ◽  
Model Assessment ◽  
Dietary Intakes ◽  
Migration Status

AbstractObjectiveLegume consumption is associated with lower fasting glucose (FG) and insulin levels in nutrition trials and lower CVD mortality in large-scale epidemiological studies. In India, legumes are widely consumed in various preparations, yet no epidemiological study has evaluated the association of legumes with FG levels, insulin resistance and diabetes risk. The present study aimed to fill this gap.DesignFasting blood samples, in-person interviews to obtain information on demographic/socio-economic factors, physical activity, alcohol and tobacco use, and anthropometric measurements were collected. Dietary intakes were assessed by an interviewer-administered, validated, semi-quantitative FFQ.SettingLucknow, Nagpur, Hyderabad and Bangalore, India.SubjectsMen and women (n 6367) aged 15–76 years – urban residents, urban migrants and their rural siblings.ResultsIn multivariate random-effects models adjusted for age, BMI, total energy intake, macronutrients, physical activity and rural/migration status, daily legume consumption was not associated with FG (P-for-trend=0·78), insulin resistance (homeostasis model assessment score; P-for-trend=0·73) or the prevalence of type 2 diabetes mellitus (P-for-trend=0·41). Stratified analyses by vegetarian diet and migration status did not change the findings. Inverse associations between legumes and FG emerged for participants with lower BMI and higher carbohydrate, protein, fat and sugar intakes.ConclusionsAlthough legumes are essential in traditional Indian diets, as well as in prudent and Mediterranean diets in the West, we did not find an association between legumes and markers of glycaemic control, insulin resistance or diabetes, except for subgroups based on BMI and macronutrient intake. The ubiquitous presence and complexity of legume preparations in Indian diets may contribute to these findings.

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.

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