The Influence Of Applying Different Non-wear Criteria On Wear-time Distributions Across Childhood And Youth

Introduction The field of physical activity monitoring has developed rapidly over the last decade. It is becoming more common to apply accelerometer-based physical activity monitors in large population based samples. The researchers are faced with large variety of choices regarding settings including wear and non-wear criteria. As no consensus has been reached regarding non-wear criteria researchers have little information to qualify their choices.

Purpose The purpose of the study is to describe the influence of different non-wear criteria on daily wear-time across a large age heterogeneous sample of Danish children, adolescents and adults.

Methods The analysis will be based on population-based samples with 4500 individuals and 8600 observations from the Danish arm of the European Youth Heart Study (EYHS) (N=1600, age 9 to 27 years) with 2-3 waves (1997-2009) of follow-up, The Copenhagen School Child Intervention Study (COSCIS) (N=700, age 6 to 14 years) with 3 waves of follow-up (2001-2010), The Childhood Health, Activity, and Motor Performance School Study Denmark (The CHAMPS-study DK) (N=1200, age 6-12 years) with two waves of follow-up (2009 and 2010), The Odense Preschool Study (TOPS) (N=450, age 5 years), and The Public After-School Care study (N=550, age 6-8 years). All physical activity measurements were measured using the Actigraph AM7164, GTM1, GT3X or GT3X+.

Analysis Plan In step one, all data files will be screened for compliance to the night-time removal criteria using the customized software Propero. Data files containing activity between 11 pm through 5 am will be screened manually (independently by two researchers) and the night-time duration estimated. If consensus cannot be reached on the night-time duration, the file is excluded from the analysis. In cases were consensus has been reached, strings of zeros will be imputed. Secondly, wear-time (without non-wear criteria applied) by gender and age are described and used as a reference for further analysis. Third, we will remove 10, 30, 60 and 90 minutes of consecutive zeros and describe the absolute number of minutes removed and describe the influence on the age- and gender specific distributions of wear-time. Finally, we will describe the probability of exclusion by different minimum wear-time criteria (60, 80 and 100% of a predefined minimum day) and non-wear criteria (10, 30, 60 and 90 minutes of consecutive zeros) across gender and age.

Conclusions The study will provide researchers using Actigraph activity monitors with observations that will enable them to make informed decisions when applying non-wear criteria.