Year: 2024
Type: article
Abstract: Walking has emerged as the predominant physical activity (PA) among adults, proving to be an effective strategy for enhancing PA levels, particularly in sedentary populations.Its association with reduced risks of obesity, cardiovascular diseases, cancer, stroke, and other chronic ailments underscores its significance in promoting overall health.Despite its proven benefits, as China undergoes rapid modernization, the nation grapples with escalating rates of obesity and chronic diseases, necessitating a comprehensive understanding of walking behaviors among its populace.This study aimed to address this knowledge gap by analyzing accelerometer data collected from Chinese individuals throughout 2018, supplemented with demographic information.The original data with year-round information (N = 100,000) was obtained from the database of Shenzhen Lifesense Medical Electronics Company and 5,000 participants were randomly selected.Children and adolescents were excluded from the data as their walking behaviors were not quite the same as those of adults and were less reliable.Wear time criterion of at least 10 hours per day for a minimum of 4 days, including at least one weekend day was applied.As a result, data of 4,959 individuals remained and was included in this study.A total of 3,191 participants were males and 1,768 were females, with an average age of 45.64 yr.(SD = 12.49 yr.).Findings revealed that the average daily step counts for the Chinese population in 2018 based on the year-round data was 13,641 ± 3,611, with males averaging 13,743 ± 3,664 steps per day and females 13,455 ± 3,505 steps per day.While assessing adherence to the widely recommended 10,000 steps per day recommendation, it was demonstrated that 86.13% of participants met this benchmark.The risk of cardiovascular and metabolic diseases measured by waist-to-height ratio (WHtR) was not associated with the average number of steps walked per day, and there was no significant difference in the number of steps walked between people at high risk of cardiovascular and metabolic diseases and healthy people (t = 1.45, p < .05,Cohen's d = 0.02).Gender disparities in average daily step counts, the daily (i.e., the diurnal patterns in PA), weekly (workday vs. weekend), monthly (beginning vs. middle vs. end of the month) and seasonal active groups were not evident.Although results from statistical test indicated that males walked more than females in general (t = 2.69, p < .05,Cohen's d = 0.08), and they were more active in the morning (t = 5.46, p < 0.05) and on workdays (t = 2.45, p < 0.05) while less active in the evening (t = -2.42,p < 0.05) iii and on weekends (t = -3.24,p < 0.05) compared to females, evaluation on effect size (ES) suggested that the observed statistical significance was most likely attributable to the substantial sample size.On the other hand, daily and weekly active groups exhibited distinct patterns across age groups.Older individuals tended to engage in more PA during the morning hours while reducing their activity levels in the evening, 𝝌 2 (21, 4,958) = 401.46,p = 0.00., Cohen's 𝜔 = 5.70.As age groups increased from 20-29 yr. to 80-89 yr., the percentage of morning active group increased from 11.45% to 66.67% and the percentage of evening active group decreased from 5.31% to 0, while the percentage of workday active group increased from 59.18% to 72.22% and the percentage of weekend active group decreased from 40.82% to 27.78%.Accumulated steps in the morning and on workdays and weekends were associated with higher step counts (r = 0.70, 0.995, 0.968, respectively).No temporal variations in walking behavior were observed in monthly and seasonal active periods.These findings highlight the potential of accelerometer data in understanding walking patterns and offering personalized PA recommendations.By utilizing accelerometer data and data mining techniques, future research can revolutionize PA analysis, leading to more tailored interventions and improved public health policies and urban planning strategies to promote PA and mitigate the burden of chronic diseases. CHAPTER 1: INTRODUCTIONThis chapter provides an introduction about the definition of walking behavior and the health benefits of walking.The assessment tools to identify and diagnose the walking behavior were discussed.The current physical activity (PA) status and walking behavior of Chinese population were also introduced.Then this chapter discussed the main objective of the whole study and specific aims to realize it. Definition of Walking BehaviorWalking can be described as any trip made by foot (Dickinson & Lumsdon, 2010).Ambulatory activity (most apparently walking, including any other bipedal locomotion) is usually considered part of a daily routine and is essential to activities performed during transportation, occupation, leisure time and domestic work for most able-bodied individuals.It is not limited by age and health condition and unlike exercise in gym, walking is free of cost and does not require additional equipment.Walking behavior can be defined by its intensity, motivation/purpose, and measurements.Steps and cadence and patterns were often used to capture the characteristics of walking behavior.Steps.The fundamental unit of human locomotor movement that underlies all forms and purposes of ambulation is a step, which can be objectively captured and summarized as steps/day using most contemporary wearable technologies.Surveillance of population PA through steps per day provides the basic information for the planning, implementation, and evaluation of public health practice.Cadence.The total daily accumulated step is a volume indicator of PA (Schuna et al., 2013), with no consideration for the variable intensities of effort associated with execution.Current wearable technologies have enabled the time-stamped tracking of step accumulation patterns over shorter time frames.It is shown that cadence (steps/min) has been strongly linked to objectively measured speed (r = 0.97) and intensity (r = 0.94) under controlled laboratory conditions (e.g., treadmill speeds from 1.8 to 12.1km/hour; Tudor-Locke et al., 2011).Patterns.Walking patterns refer to the habitual or characteristic ways in which individuals engage in walking behavior over time.These patterns encompass various aspects of
Author: Yan, Hai
Language: English
Topic: Physical Activity and Health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7220760660