Associations between self-reported and objectively measured physical activity and overweight/obesity among adults in Kota Bharu and Penang, Malaysia

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

BACKGROUND

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

OBJECTIVE

This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

METHODS

Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

RESULTS

A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

CONCLUSIONS

This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

Physical Activity in Malaysia: Are We Doing Enough? Findings from the REDISCOVER Study

Physical activity (PA) in the form of structured or unstructured exercise is beneficial for health. This paper aims to study PA levels across four domains according to the International Physical Activity Questionnaire (IPAQ) and its associated factors. A total of 7479 Malaysian adult participants between 18 to 90 years old from the REDISCOVER study who completed the IPAQ were analyzed. PA was calculated as MET-min per week and were categorized according to insufficiently active, sufficiently active and very active. Multinomial regression was used to determine the association between sociodemographic, clinical factors and the level of PA. The mean age of the participants was 51.68 (±9.5 SD). The total reported physical activity in median (IQR) was 1584.0 (0-5637.3) MET-min per week. The highest total for PA was in the domestic domain which is 490 (0-2400) MET-min per week. Factors associated with sufficiently active or very active PA include Malay ethnicity, no formal education, elementary occupation, current smokers and high HDL. Whereas low income, male and normal BMI are less likely to participate in sufficiently active or very active PA. Intervention to encourage higher PA levels in all domains is important to achieve recommended PA targets.

Epidemiological characteristics of different patterns of obesity among adults in rural communities of south-east Nigeria: a population-based cross-sectional study

Background

Obesity is a complex disease characterised by excess fat accumulation and health risks. There is paucity of data on epidemiology of obesity patterns among adults in rural Nigeria. This study aimed to provide current data on the prevalence and predictors of three patterns of obesity (abdominal obesity alone, general obesity alone and a combination of both) among adults in three rural communities of Enugu State, South-east Nigeria to enhance specific obesity prevention and control programmes/interventions.

Methods

This population-based cross-sectional study involved 500 adults selected through a six-stage random sampling technique. Questionnaire was used to obtain data on socio-economic, dietary and lifestyle characteristics of the respondents. Weight, height and waist circumference were measured by standard procedures. Body mass index was used to assess general obesity while abdominal obesity was assessed through waist circumference. Each respondent was classified under only one of the three patterns: general obesity alone, abdominal obesity alone and combined obesity. Binary and multivariate logistic regression analyses were used to assess the predictors. Significance was set P<0.05.

Results

Prevalence of abdominal obesity alone was 6.0%. General obesity alone was found among 31.4% and 45.6% were affected by combined obesity. Being a female (AOR:0.35, 95% C.I.: 0.14, 0.88) and not skipping meals (AOR:0.24, 95% C.I.: 0.10, 0.55) were associated with less likelihood of abdominal obesity but ≥3 times daily meal intake increased the risk by 2.52 (AOR:2.52, 95% C.I.:1.10, 5.75). Predictors of general obesity alone were age 41-60 years (AOR:1.84, 95% C.I.:1.14, 2.97), being a female (AOR:7.65, 95% C.I.:4.77, 12.26) and having any form of formal education (AOR:2.55, 95% C.I.:1.10, 5.91). Combined obesity was less likely among 41-60 year-olds (AOR:0.36, 95% C.I.:0.23, 0.56) and females (AOR:0.21, 95% C.I.:0.13, 0.32). Never married (AOR:1.94, 95% C.I.:1.03, 3.67) and vigorous physical activities (AOR:1.81, 95% C.I.:1.08, 3.02) increased the risk of combined obesity by almost 2.

Conclusions

Prevalence of abdominal obesity alone, general obesity alone and combined obesity were high. They were functions of age, sex, never married, having any form of formal education, skipping meals, ≥3 daily meal intake and self-reported vigorous physical activity. Focused nutrition and health education are recommended strategies for prevention and control of obesity.

Comparison of the Physical Activity Measured by a Consumer Wearable Activity Tracker and That Measured by Self-Report: Cross-Sectional Analysis of the Health eHeart Study

Background

Commercially acquired wearable activity trackers such as the Fitbit provide objective, accurate measurements of physically active time and step counts, but it is unclear whether these measurements are more clinically meaningful than self-reported physical activity.

Objective

The aim of this study was to compare self-reported physical activity to Fitbit-measured step counts and then determine which is a stronger predictor of BMI by using data collected over the same period reflecting comparable physical activities.

Methods

We performed a cross-sectional analysis of data collected by the Health eHeart Study, a large mobile health study of cardiovascular health and disease. Adults who linked commercially acquired Fitbits used in free-living conditions with the Health eHeart Study and completed an International Physical Activity Questionnaire (IPAQ) between 2013 and 2019 were enrolled (N=1498). Fitbit step counts were used to quantify time by activity intensity in a manner comparable to the IPAQ classifications of total active time and time spent being sedentary, walking, or doing moderate activities or vigorous activities. Fitbit steps per day were computed as a measure of the overall activity for exploratory comparisons with IPAQ-measured overall activity (metabolic equivalent of task [MET]-h/wk). Measurements of physical activity were directly compared by Spearman rank correlation. Strengths of associations with BMI for Fitbit versus IPAQ measurements were compared using multivariable robust regression in the subset of participants with BMI and covariates measured.

Results

Correlations between synchronous paired measurements from Fitbits and the IPAQ ranged in strength from weak to moderate (0.09-0.48). In the subset with BMI and covariates measured (n=586), Fitbit-derived predictors were generally stronger predictors of BMI than self-reported predictors. For example, an additional hour of Fitbit-measured vigorous activity per week was associated with nearly a full point reduction in BMI (–0.84 kg/m², 95% CI –1.35 to –0.32) in adjusted analyses, whereas the association between self-reported vigorous activity measured by IPAQ and BMI was substantially smaller in magnitude (–0.17 kg/m², 95% CI –0.34 to –0.00; P<.001 versus Fitbit) and was dominated by the Fitbit-derived predictor when compared head-to-head in a single adjusted multivariable model. Similar patterns of associations with BMI, with Fitbit dominating self-report, were seen for moderate activity and total active time and in comparisons between overall Fitbit steps per day and IPAQ MET-h/wk on standardized scales.

Conclusions

Fitbit-measured physical activity was more strongly associated with BMI than self-reported physical activity, particularly for moderate activity, vigorous activity, and summary measures of total activity. Consumer-marketed wearable activity trackers such as the Fitbit may be useful for measuring health-relevant physical activity in clinical practice and research.

Device-Based Measures of Sedentary Time and Physical Activity Are Associated With Physical Fitness and Body Fat Content

Introduction/Purpose: Physical activity and sedentary time may associate with physical fitness and body composition. Yet, there exists some observational studies that have investigated the associations of device-based measures of sedentary time and physical activity (PA) with cardiorespiratory fitness (CRF) and body composition but associations with muscular fitness (MF) are less studied.

Methods: Objective sedentary time and physical activity was measured by a hip worn accelerometer from 415 young adult men (age: mean 26, standard deviation 7 years). Cardiorespiratory fitness (VO₂max) (CRF) was determined using a graded cycle ergometer test until exhaustion. Maximal force of lower extremities was measured isometrically and lower body power was assessed using standing long jump (MF). Body composition was determined with bioimpedance method. Single and compositional approach was used in regression analysis.

Results: Mean sedentary time was 707 (standard deviation 133) minutes per day (77 ± 8% of the wear time). Volumes of all PA intensities were positively associated with CRF and associations showed linearly increasing magnitudes with higher intensities in single regression models adjusted for age and smoking (p < 0.001). Similarly, PA intensities were positively associated with lower body MF, however, with weaker associations (p < 0.005). After further adjustment for resistance training, the associations remained significant. The associations of the relative distribution of time within sedentary behavior (SB), light intensity PA (LPA) and moderate-to-vigorous PA (MVPA) behaviors as a whole with using compositional analysis further revealed that within the composition MVPA and SB were positively associated with CRF and MF (p < 0.001), while LPA was not. In addition, within the composition, accumulated PA bouts lasting more than 3 min were consistently associated with CRF and MF, and with all body composition variables (p < 0.001), while sedentary time was associated with body fat percentage (p < 0.001).

Conclusion: Promoting physical activity and reducing sedentary time may have positive influence on physical fitness and body fat content, and thereby may offer positive health effects. Physical activity of higher intensities may offer greater benefits.