scholarly journals Clustering of Six Key Risk Behaviors for Chronic Disease among Adolescent Females

2020 ◽  
Vol 17 (19) ◽  
pp. 7211
Author(s):  
Lauren A. Gardner ◽  
Katrina E. Champion ◽  
Belinda Parmenter ◽  
Lucinda Grummitt ◽  
Cath Chapman ◽  
...  
Keyword(s):  
Behavior Change ◽  
Risk Behavior ◽  
Risk Behaviors ◽  
Screen Time ◽  
Latent Class ◽  
Vegetable Intake ◽  
Vigorous Physical Activity ◽  
Health Behavior Change ◽  
Adolescent Females ◽  
Positive Health

Chronic diseases are the leading cause of disability and mortality globally. In Australia, females are at heightened risk. This research explored the prevalence, patterns, and correlates of six key risk behaviors (physical inactivity, poor diet, recreational screen time, inadequate sleep, alcohol use, and smoking) among adolescent females and whether knowledge of health guidelines was associated with adherence. Adolescent females completed an anonymous online questionnaire (N = 687; Mage = 13.82). Logistic regression assessed the association between knowledge and adherence. A Latent Class Analysis (LCA) and three-step procedure identified risk behavior clusters and their correlates. Despite positive health self-ratings (77% good/very good), most participants reported insufficient moderate-to-vigorous physical activity (MVPA; 89%), vegetable intake (89%), and excessive screen time (63%). Knowledge of guidelines was associated with adherence for MVPA, vegetable intake, sleep, and alcohol abstinence. Three classes emerged: “moderate risk” (76%), “relatively active, healthy eaters” (19%), and “excessive screen users” (5%). These risk-behavior clusters were associated with perceived value of academic achievement and physical wellbeing. Adolescent females commonly perceive they are in good health, despite engaging in unhealthy behaviors. Public health interventions should utilize effective behavior change strategies, adopt a multiple health behavior change approach (MHBC), and be tailored to specific risk profiles and values among females.

Stages of health behavior change and mindsets: A latent class approach.

2013 ◽  
Vol 32 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Jana Richert ◽  
Natalie Schüz ◽  
Benjamin Schüz
Keyword(s):  
Health Behavior ◽  
Behavior Change ◽  
Latent Class ◽  
Health Behavior Change

scholarly journals Co-varying Patterns of Physical Activity and Sedentary Behaviors and Their Long-Term Maintenance Among Adolescents

2010 ◽  
Vol 7 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Jihong Liu ◽  
Jinseok Kim ◽  
Natalie Colabianchi ◽  
Andrew Ortaglia ◽  
Russell R. Pate
Keyword(s):  
Physical Activity ◽  
Young Adulthood ◽  
Screen Time ◽  
Latent Class ◽  
Vigorous Physical Activity ◽  
Sedentary Behaviors ◽  
Logistic Regression Models ◽  
Males And Females ◽  
Term Maintenance

Background:We examined the covarying patterns of physical activity and sedentary behaviors among adolescents and their long-term maintenance.Methods:Data came from the National Longitudinal Study of Adolescent Health (1995–2002). We used latent class analysis to identify distinct covarying patterns in adolescence. Logistic regression models were used to predict odds of meeting moderate-to-vigorous physical activity (MVPA) recommendations (≥5 bouts/week) and exceeding screen time guidelines (>2 hours/day) 6 years later based on their adolescent class profile.Results:Five classes for each gender were identified and labeled as low physical activity (PA)/low sedentary behaviors (SED), moderate (Mod) PA/high (HI) SED, Mod PA/low SED, HI PA/low SED, and HI PA (except skating/biking)/low SED. Compared with low PA/low SED, males and females in Mod PA/low SED, HI PA/low SED, and HI PA (except skating/biking)/low SED classes had increased odds of meeting MVPA recommendations in young adulthood. Mod PA/HI SED had higher odds of exceeding screen time guidelines in young adulthood (adjusted odds ratio [AOR] for females: 1.67, 95% CI: 1.00−2.81; AOR for males: 3.31, 95% CI: 1.80−6.09).Conclusions:Findings are useful to aid the development of multifactorial interventions that promote physical activity and reduce screen time among adolescents transitioning to adulthood.

Campus Recreation Use and Health Behaviors among College Students in Different Academic Disciplines

2017 ◽  
Vol 41 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Patrick C. Gathman ◽  
Nicole R. Grabowski ◽  
Julia Wallace Carr ◽  
Mikel K. Todd
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
Vegetable Intake ◽  
Academic Discipline ◽  
Vigorous Physical Activity ◽  
Perceived Health ◽  
Health Science ◽  
Campus Recreation ◽  
Positive Health ◽  
Health Related

Physical activity, campus recreation (CR) use, body mass index (BMI), and varied health indices were compared between academic discipline groups and sex. Participants ( n = 219) were classified as AD I (kinesiology and physical education majors), AD II (health science majors and nursing majors), and AD III (representative sample of non-health-related majors) to make between-group comparisons based on the amount of emphasis placed on physical activity and health-related content within different disciplines. Significant differences ( p < .05) were found between the academic discipline groups for International Physical Activity Questionnaire scores, CR minutes, CR days, CR time per day, vigorous physical activity (VPA), and perceived-health score; and between sex for BMI, VPA, sitting, fiber intake, and fruit and vegetable intake. The results indicate a positive relationship between the emphasis placed physical activity and health within an academic discipline and the degree to which students participate in physical activity, positive health behaviors, and perceived health.

scholarly journals Breakfast Habits and Associations with Fruit and Vegetable Intake, Physical Activity, Sedentary Time, and Screen Time among Swedish 13–14-Year-Old Girls and Boys

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4467
Author(s):  
Björg Helgadóttir ◽  
Hanna Baurén ◽  
Karin Kjellenberg ◽  
Örjan Ekblom ◽  
Gisela Nyberg
Keyword(s):  
Physical Activity ◽  
Screen Time ◽  
Fruits And Vegetables ◽  
Sedentary Time ◽  
Vegetable Intake ◽  
Vegetable Consumption ◽  
Vigorous Physical Activity ◽  
Fruit And Vegetable Intake ◽  
Fruit And Vegetable ◽  
Cross Sectional

This study explored whether breakfast habits were associated with intake of fruits and vegetables, minutes in moderate-to-vigorous physical activity (MVPA), minutes spent sedentary, and screen time among adolescents. Cross-sectional data were collected among 13–14-year-old boys and girls (n = 1139). Breakfast habits and screen time were determined via questionnaire, fruit and vegetable intake were determined through dietary recall, and physical activity and sedentary time were determined via accelerometers. Multilevel mixed models and general estimation equation models were applied. Almost 40% of participants skipped breakfast at least one day of the week. Participants with irregular breakfast habits on weekdays had lower fruit and vegetable consumption by 26.7 g (95% CI = −49.3, −5.9) while irregular breakfast habits during the whole week were associated with higher levels of screen time (OR = 1.5, 95% CI = 1.1, 2.1) compared to regular breakfast habits. Girls with irregular breakfast habits on weekdays had 7.7 min more sedentary time (95% CI = 0.8, 15.7) than girls with regular breakfast habits, while the opposite was found in boys (β = −13.3, 95% CI = −25.3, −2.6)). No significant associations were found for MVPA. Regular breakfast habits should be encouraged, as they might contribute to a higher intake of fruit and vegetables and are associated with lower levels of screen time, although further studies are necessary to establish causation.

Facilitating the Adoption of Digital Health Technologies by Older Adults to Support Their Health

2021 ◽  
pp. 164-179
Author(s):  
Maurita T. Harris ◽  
Wendy A. Rogers
Keyword(s):  
Older Adults ◽  
Health Behavior ◽  
Behavior Change ◽  
Technology Acceptance ◽  
Digital Health ◽  
Theoretical Models ◽  
Health Behavior Change ◽  
The United States ◽  
Positive Health ◽  
Health Technologies

With over 50% of older adults in the United States managing at least one chronic condition, it is crucial to understand how to promote their self-management of positive health behaviors. Health interventions through digital health technologies are becoming more commonplace. Theoretical models related to health behavior change and technology acceptance can guide the design of these healthcare tools and lead to adoption by older adults to support their health. This chapter provides an overview of health behavior change and technology acceptance models to inform the development of digital health technology for older adults. This chapter illustrates the application of these models by describing two design personas that represent human factors designers. This chapter discusses the lack of inclusion of technology adoption and other long-term concepts and the need for further exploration that could inform understanding of technology integration into everyday health activities.

scholarly journals Physical Disorders are Associated with Health Risk Behaviors in Chinese Adolescents: A Latent Class Analysis

2020 ◽  
Vol 17 (6) ◽  
pp. 2139
Author(s):  
Bingdong Song ◽  
Weirong Hu ◽  
Wanxia Hu ◽  
Rong Yang ◽  
Danlin Li ◽  
...  
Keyword(s):  
Risk Behaviors ◽  
Screen Time ◽  
Latent Class ◽  
Health Risk Behaviors ◽  
Chinese Adolescents ◽  
Suicidal Behaviors ◽  
Moderate Risk ◽  
Unintentional Injuries ◽  
Risk Class ◽  
Physical Disorders

It is known that health risk behaviors (HRBs) can lead to a variety of physical and mental health problems among adolescents, but few studies have paid attention to the relationship between latent classes of HRBs and adolescent diseases. The purpose of this study was to use latent class analysis (LCA) to clarify the potential subgroups of HRBs (smoking, drinking, screen time, non-suicidal self-injuries, suicidal behaviors, and unintentional injuries) and examine the association between the subgroups of HRBs and physical disorders (diarrhea, fever, cough, and vomiting) with multiple logistic regression analysis, in Chinese adolescents. Self-reported HRBs and physical disorders were used to evaluate 22,628 middle school students in six cities of China, from November 2015 to January 2016, based on a multistage stratified cluster sampling approach. The prevalence of diarrhea, fever, cough, and vomiting was 23.5%, 15.9%, 50.6%, and 10.7%, respectively. We identified four latent classes of HRBs by LCA, including low-risk class, moderate-risk class 1 (smoking, drinking, and screen time), moderate-risk class 2 (non-suicidal self-injuries and suicidal behaviors, unintentional injuries), and high-risk class (smoking, drinking, screen time, non-suicidal self-injuries, suicidal behaviors, and unintentional injuries), which were 64.0%, 4.5%, 28.8% and 2.7% of participants, respectively. Compared to the low-risk class, all other classes showed higher risk for these physical disorders (P < 0.01 for each). In particular, the high-risk class had the highest risk (diarrhea (odds ratio (OR) = 2.628, 95% confidence interval (CI) 2.219 to 3.113), fever (OR = 3.103, 95% CI 2.591 to 3.717), cough (OR = 2.142, 95% CI 1.805 to 2.541), and vomiting (OR = 3.738, 95% CI 3.081 to 4.536). In conclusion, these results indicated that heterogeneity exists in HRBs, and subgroups of HRBs were correlated to the occurrence of common physical disorders in Chinese adolescents. Therefore, multiple HRBs rather than single factors should be considered for the prevention of common physical disorders in schools.

Associations between multiple positive health behaviours and cardiometabolic risk using three alternative measures of physical activity: NHANES 2005–2006

2020 ◽  
Author(s):  
Stina Oftedal ◽  
Elroy J Aguiar ◽  
Mitch J. Duncan
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Health Behaviors ◽  
Screen Time ◽  
Cardiometabolic Risk ◽  
Vigorous Physical Activity ◽  
Single Point ◽  
Index Score ◽  
Linear Regression Models ◽  
Positive Health

Purpose: The study aimed to investigate the association between clustered cardiometabolic risk (CCMR) and health-behavior indices comprising three different measures of physical activity, screen time, diet and sleep in NHANES 2005-2006. Methods: CCMR was calculated by standardizing and summarizing measures of blood pressure, fasting glucose, triglycerides, insulin, high-density lipoprotein and waist circumference to create a Z-score. Three health behavior indices were constructed with a single point allocated to each of the following lower risk behaviors: muscle strengthening activity, healthy eating score, sleep disorder/disruption, sleep duration, screen time and physical activity (self-reported moderate-to-vigorous physical activity [MVPA] (Index Score-SR), accelerometer-measured MVPA (Index Score-MVPA) or accelerometer-measured steps Index Score-Steps). Linear regression models explored associations between index scores and CCMR. Results: In the sample (n=1537, 52% male, aged 45.5 [SE:0.9] years), reporting 0-5 vs. 6 health behaviors using Index Score-SR and Index Score-MVPA, and 0-4 vs. 6 health behaviors using Index Score-Steps, were associated with a significantly higher CCMR. The beta (β [95%CI]) for zero vs. six behaviors were: Index Score-SR (2.86 [2.02, 3.69], Index Score-MVPA (2.41 [1.49, 3.33] and Index Score-Steps (2.41 [1.68, 3.15]). Conclusion: Irrespective of the measure of physical activity, engaging in fewer positive health behaviors was associated with greater CCMR. Novelty bullets • Physical activity, screen time, diet and sleep may exert synergistic/cumulative effects on clustered cardiometabolic risk. • A greater number of positive health behaviors was associated with a lower clustered cardiometabolic risk factor score. • The reduction in cardiometabolic risk was similar irrespective of which physical activity measure was used.

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