Dataset Information

Use of latent class analysis as a method of assessing the physical activity level, sedentary behavior and nutritional habit in the adolescents' lifestyle: A scoping review.

ABSTRACT:

Background

Currently, adolescents' lifestyle is commonly characterized by physical inactivity, sedentary behavior, and inappropriate eating habits in general. A person-oriented approach as Latent Class Analysis (LCA) can offer more insight than a variable-centered approach when investigating lifestyle practices, habits, and behaviors of adolescent population.

Objective

The aim of the present study was to assess which variables are mostly used to represent the physical activity level, sedentary behavior SB) and nutritional habit in the adolescents' lifestyle in studies that used the LCA.

Design

Scoping review.

Methods

The study was a performed in accordance with the proposed criteria for systematic reviews and meta-analyses-Preferred Reporting Items for Systematic Reviews and Meta-Analyses and registered in PROSPERO (CRD42018108444). The original articles were searched in MEDLINE/PubMed, Scopus, ScienceDirect, Web of Science, PsycINFO, and SPORTdiscus. The Quality Assessment Tool analyzed the risk of bias of the included studies.

Results

30 original articles were selected. The physical activity level (28 studies), SB and nutritional habits (18 studies) were the most common variable used to evaluate the adolescent's lifestyle by LCA model. Specifically, physical inactivity and high SB were the manifest variables with higher frequency in the negative latent classes (LCs) in adolescent girls. On the other hand, physical exercises and sports were activities more commonly labeled as positive LCs.

Conclusions

The LCA models of the most of selected studies showed that physical inactivity, high SB were the most common in the LCs with negative characteristics of the adolescents' lifestyle. Better understanding the results of analyzes of clusters of multivariate behaviors such as the LCA can help to create more effective strategies that can make the lifestyle of adolescents healthier.

SUBMITTER: Miranda VPN

PROVIDER: S-EPMC8376087 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Use of latent class analysis as a method of assessing the physical activity level, sedentary behavior and nutritional habit in the adolescents' lifestyle: A scoping review.

Miranda Valter Paulo Neves VPN Coimbra Danilo Reis DR Bastos Ronaldo Rocha RR Miranda Júnior Márcio Vidigal MV Amorim Paulo Roberto Dos Santos PRDS

PloS one 20210819 8

<h4>Background</h4>Currently, adolescents' lifestyle is commonly characterized by physical inactivity, sedentary behavior, and inappropriate eating habits in general. A person-oriented approach as Latent Class Analysis (LCA) can offer more insight than a variable-centered approach when investigating lifestyle practices, habits, and behaviors of adolescent population.<h4>Objective</h4>The aim of the present study was to assess which variables are mostly used to represent the physical activity lev ...[more]

PMID: 34411143

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Project description:BackgroundLatent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.MethodsComprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.ResultsOf 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.ConclusionsThis review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

Project description:Background: Unhealthy dietary and lifestyle behaviors are associated with a higher prevalence of non-communicable chronic diseases and higher mortality in adults. However, there remains some uncertainty about the magnitude of the associations between lifestyle behaviors and cardiovascular factors in adolescents. Methods: We conducted a school-based cross-sectional study of 895 Chinese adolescents aged 15-19 years. They participated in a questionnaire survey, physical examination, and blood sample collection. Latent class analysis (LCA) was used to identify heterogeneous subgroups of lifestyle behaviors. A set of 12 latent class indicators, which reflected lifestyle behaviors including dietary habits, physical activity, sleep duration, screen time, and pressure perception, were included in the analysis. Logistic regression analysis was performed to determine whether the derived classes were related to a cardiometabolic risk. Results: In total, 13.7 and 5.6% of the participants were overweight and obese, respectively, and 8.4 and 14.1% reported having pre-hypertension and hypertension, respectively. A two-class model provided the best fit with a healthy lifestyle pattern (65.8%) and a sub-healthy lifestyle pattern (34.2%). There were more female participants with a healthy lifestyle (56.2 vs. 43.8%), whereas there were more males with a sub-healthy lifestyle (45.4 vs. 54.6%), (all P = 0.002). Increased risk of cardiometabolic abnormality (BMI categories, blood pressure and lipids) was not significant across lifestyle patterns, except for waist circumference (70.5 vs 69.1 cm, P = 0.044). There was no significant difference in physical activity and intake of fruit and vegetable between the two patterns. Conclusion: Primary prevention based on lifestyle modification should target patterns of behaviors at high risk in adolescents. Due to the complex effect of lifestyle clusters on cardiometabolic risks, well-designed and prospective studies in adolescents are needed in the future.

Project description:BackgroundDigital technology and media use is integral to adolescents' lives and has been associated with both positive and negative health consequences. Previous studies have largely focused on understanding technology behaviors and outcomes within adolescent populations, which can promote assumptions about adolescent technology use as homogeneous. Furthermore, many studies on adolescent technology use have focused on risks and negative outcomes. To better understand adolescent digital technology use, we need new approaches that can assess distinct profiles within study populations and take a balanced approach to understanding the risks and benefits of digital technology use.ObjectiveThe purpose of this study was to identify profiles of adolescent technology use within a large study population focusing on four evidence-based constructs: technology ownership and use, parental involvement, health outcomes, and well-being indicators.MethodsAdolescent-parent dyads were recruited for a cross-sectional web-based survey using the Qualtrics (Qualtrics International, Inc) platform and panels. Technology use measures included ownership of devices, social media use frequency, and the Adolescents' Digital Technology Interactions and Importance scale. Parent involvement measures included household media rules, technology-related parenting practices, parent social media use frequency, and the parent-child relationship. Health outcome measures included physical activity, sleep, problematic internet use, and mental health assessments. Well-being indicators included mental wellness, communication, and empathy. We used latent class analysis (LCA) to identify distinct profile groups across the aforementioned 4 critical constructs.ResultsAmong the 3981 adolescent-parent dyads recruited, adolescent participants had a mean age of 15.0 (SD 1.43) years; a total of 46.3% (1842/3981) were female, 67.8% (2701/3981) were White, and 75% (2986/3981) lived in a household with an income above the poverty line. The LCA identified 2 discrete classes. Class 1 was made up of 62.8% (2501/3981) of the participants. Class 1 participants were more likely than Class 2 participants to report family-owned devices, have lower technology importance scores, have household technology rules often centered on content, have positive parent relationships and lower parent social media use, and report better health outcomes and well-being indicators.ConclusionsFindings from this national cross-sectional survey using LCA led to 2 distinct profile groups of adolescent media use and their association with technology use and parent involvement as well as health and well-being outcomes. The two classes included a larger Class 1 (Family-Engaged Adolescents) and a smaller Class 2 (At-Risk Adolescents). The findings of this study can inform interventions to reinforce positive technology use and family support.

Project description:BackgroundLifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status.MethodsThe current cross-sectional study consists of 525 adults aged 18-64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits.ResultsMean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3rd class was characterized by a unhealthy dietary pattern, low level of physical activity and low probability of smoking and further analysis found that there were significant differences in body mass index (BMI), Waist-to-hip ratio (WHR), FBS, Hemoglobin (Hb), education levels and anxiety status between classes (P <0.05).ConclusionThis study attempts to classify Iranian adults by their own health behavior. Healthcare professionals should be aware of associations between different lifestyle risk factors and health promotion strategies should further focus on multiple behaviors at the same time. In our country, more studies about the adult population are needed to support the observed findings of our study and therefore allow for a certain generalization of the observations.

Project description:Background: The negative effects of sedentary behavior (SB) on public health have been extensively documented. A large number of studies have demonstrated that high prevalence of SB is a critical factor of all-cause mortality. Globally, the frequency of SB research has continued to rise, but little is known about SB in the Chinese population. Therefore, this review was conducted to scope the research situation and to fill the gaps related to the effects of SB in the Chinese population. Methods: Using a scoping review based on York methodology, a comprehensive search of published journal articles and grey literature was carried out through 12 databases. The literature research was conducted by two authors in July 2019, and included journal articles that targeted on the Chinese population were published between 1999 and 2019. The two authors screened the records independently and included those research topics related to SB in the Chinese population. Results: The number of included studies increased from 1 to 29 per year during the analyzed period, during which, a remarkable climb happened from 8 in 2013 to 19 in July 2019. Out of the 1303 screened studies, a total of 162 studies (81 English and 81 Chinese journal articles) met the inclusion criteria in this review. Most of the included studies (66.0%) reported the overall estimated prevalence of SB, in which, 43.2% of studies reported the average time of SB, and 40.0% of studies reported the cutoff point of SB. Besides this, 54.9% and 23.5% of studies focused on the outcomes and correlates/determinants of SB, and the proportions of studies based on testing the validation of measurement tools and on interventions were 3.7% and 4.9%, respectively. Nearly all of the reviewed articles used data from cross-sectional studies (75.9%) and longitudinal studies (13.6%), while intervention trials are less developed. The majority of the studies (64.8%) used self-reported surveys, and only 3.7% studies used device-based measurement tools. Furthermore, 35.8% of the included studies were focused on children and adolescents, while only a few studies investigated infants/toddlers and older adults. Both female and male were examined in most studies, and non-clinical populations were investigated in the context of SB in a relatively large number of studies. Conclusions: The number of research articles on SB in the Chinese population published per year has increased year by year, indicating a growing interest in this research area. More studies using population subgroup samples are needed, particularly among infants/toddlers, older adults, and clinical populations. To provide stronger evidence of the determinants and outcomes of SB, longitudinal studies using device-based measures of SB are required.

Dataset Information

Use of latent class analysis as a method of assessing the physical activity level, sedentary behavior and nutritional habit in the adolescents' lifestyle: A scoping review.

Background

Objective

Design

Methods

Results

Conclusions

Publications

Use of latent class analysis as a method of assessing the physical activity level, sedentary behavior and nutritional habit in the adolescents' lifestyle: A scoping review.

Similar Datasets

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