Clustering of multiple energy balance related behaviors is associated with body fat composition indicators in adolescents: Results from the HELENA and ELANA studies

Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: a systematic review

BACKGROUND

Physical activity (PA), sedentary behaviour (SB) and diet play an important role in the physical and mental health of young people. Understanding how these behaviours cluster, and the impact of clusters on health is important for the development of public health interventions. This review examines the prevalence of clusters of PA, sedentary time, and dietary behaviours, and how clusters relate to physical and mental health indicators among children, adolescents and young adults.

METHODS

Electronic (PubMed, Web of Science and Scopus) and manual searches were conducted for articles that were (i) observational studies including children, adolescents and/or young adults aged 5-24 years, (ii) examined the 'patterning', 'clustering', or 'co-existence' of each of PA, dietary behaviour and SB, and (iii) published in English up to and including July 2022. In addition to information on clustering, data on physical and mental health outcomes were extracted where reported. Included studies were assessed using the Cochrane risk of bias for observational studies. A narrative synthesis was conducted due to high heterogeneity. This review was registered with PROSPERO (CRD42021230976).

RESULTS

Forty-nine cross-sectional studies and four prospective cohort studies from eighteen countries reporting data from 778,415 individual participants were included. A broad range of clusters (n = 172) were found (healthy, unhealthy, and mixed). Mixed clusters were common (n = 98), and clusters of high diet quality, low PA and high SB were more prevalent in girls, while mixed clusters of high PA, high SB and low diet quality were more prevalent in boys. Unhealthy clusters comprising low moderate to vigorous PA, low consumption of fruits and vegetables, and high screen time were prevalent, particularly in those from lower socioeconomic status families. Compared to those with healthy behavioural clusters, those with unhealthy and mixed clusters had a higher adiposity, higher risk of cardiovascular disease, poorer mental health scores, and lower cardiorespiratory fitness.

CONCLUSIONS

PA, SB and diet cluster in healthy, unhealthy and mixed patterns in young people that differ across sociodemographic characteristics. Unhealthy clusters are associated with poorer health outcomes. Intervention strategies targeting un-clustering multiple unhealthy behaviours should be developed and evaluated for their impact on health outcomes.

Association between Recreational Screen Time and Sleep Quality among Adolescents during the Third Wave of the COVID-19 Pandemic in Canada

The study objective was to verify whether recreational screen time was associated with sleep quality among adolescents during the third wave of the COVID-19 pandemic in Canada. Data collection took place in four high schools in the region of Chaudière-Appalaches (Quebec, Canada) from the end of April to mid-May 2021. Recreational screen time and sleep quality were measured using the French versions of validated questionnaires specifically designed for adolescents. A total of 258 adolescents (14−18 years; 66.3% girls) answered the online survey. Adolescent boys had a higher total mean recreational screen time (454.3 ± 197.5 vs. 300.5 ± 129.3 min/day, p < 0.0001) and a higher total mean sleep quality score (4.2 ± 0.9 vs. 3.9 ± 0.8, p = 0.0364) compared to girls. Recreational screen time (β = −0.0012, p = 0.0005) and frequency of concurrent screen use (sometimes: β = −0.3141, p = 0.0269; often: β = −0.4147, p = 0.0048; almost always or always: β = −0.6155, p = 0.0002) were negatively associated with sleep quality while being a boy (β = 0.4276, p = 0.0004) was positively associated with sleep quality and age (p = 0.6321) was not. This model explained 16% of the variance in adolescents’ sleep quality. Public health interventions during and after the COVID-19 pandemic should target recreational screen time, concurrent screen use and especially girls to possibly improve sleep quality and promote adolescents’ physical and mental health.

Clustering of Physical Activity, Diet and Sedentary Behavior among Youth from Low-, Middle-, and High-Income Countries: A Scoping Review

Background: The interaction between physical activity (PA), diet, and sedentary behavior (SB) plays an important role on health-related outcomes. This scoping review (Prospero CRD42018094826) aims to identify and appraise clusters of PA, diet, and SB among youth (0–19 years) according to country income. Methods: Five databases were searched. Fifty-seven articles met the inclusion criteria. Results: Fifty-five cluster types were identified, with greater variety in high-income than lower income countries. The most prevalent profiles were “High SB and consumption of sugar, salt, and beverages (SSB)” (n = 17) and “High PA” (n = 13–5), both of which presented in all income countries. The healthiest profile, “High PA and fruit and vegetables (F&V); Low SB and SSB” (n = 12), was present in upper-middle and high-income countries, while the unhealthiest “Low PA and F&V; High SB and SSB” (n = 6) was present only in high-income countries. Conclusions: High SB and unhealthy diet (SSB) were more prevalent in clusters, mainly in high-income countries. The results support the need for multi-component actions targeting more than one behavior at the same time.

Poor Health Related Quality of Life and Unhealthy Lifestyle Habits in Weight-Loss Treatment-Seeking Youth

Obesity is associated with unhealthy lifestyle behaviors and poor Health Related Quality of Life (HRQOL). The cumulative effect of lifestyle behaviors on HRQOL has been demonstrated in chronically ill adolescents, but not in adolescents with obesity. The present study aimed to assess the association between HRQOL and adherence to the Mediterranean Diet (MD) and/or low levels of physical activity (PA) in a large sample of outpatient adolescents with overweight or obesity seeking weight loss treatment. Four-hundred-twenty participants were enrolled from 10 Italian outpatient clinics. The demographics and anthropometric features, KIDMED scores, and exercise levels of the participants were collected, together with parental features. The HRQOL was assessed by the Pediatric Quality of Life Inventory (PedsQL™), Adolescents Version 4.0. PedsQL total score and functioning subscales were lower in adolescents who reported one or two unhealthy habits. Compared with the high/intermediate groups, the risk of low HRQOL was twice as high for each unit increase in BMI SDS, while the percentage was reduced by 12.2% for every unit increase in the KIDMED score and by 32.3% for each hour increase of exercise. The clustering of these two unhealthy behaviors conferred a 120% higher risk of low HRQOL. Similarly, adolescents displaying better diet quality and/or a physically more active lifestyle have better physical and psychological functioning. Further studies are needed to disclose whether these characteristics may be predictive of better adherence to weight loss treatment.

Aim2Be mHealth intervention for children with overweight and obesity: study protocol for a randomized controlled trial

Background

The prevalence of overweight and obesity remains high in Canada, and the current standard for the treatment of childhood obesity is in-person, family-based, multidisciplinary interventions that target lifestyle behaviors (e.g., diet, physical activity, and sedentary behaviors). These programs are costly to operate, have limited success, and report recruitment and retention challenges. With recent advances in technology, mobile health or mHealth has been presented as a viable alternative to in-person interventions for behavior change, especially with teens.

Purpose

The primary aim of this study is to test the efficacy of Aim2Be, a gamified app based on behavior change theory with health coaching to improve weight outcomes (i.e., decrease in standardized body mass index (zBMI)) and lifestyle behaviors (i.e., improve dietary quality, increase fruit and vegetable intake, reduce sugar-sweetened beverage intake, increase physical activity, and reduce screen time) among children 10- to 17-years old with overweight or obesity versus their peers randomized into a waitlist control condition. The secondary aims of this study are to 1) test whether supplementing the Aim2Be program with health coaching increases adherence and 2) examine the mediators and moderators of adherence to the Aim2Be intervention.

Methods

We will employ a randomized controlled trial design and recruit 200 child and parent dyads to participate in the study (2019–2020). Participants will be recruited from Canadian pediatric weight management clinics and through online advertisements. Child participants must be between the ages of 10 and 17 years, have overweight or obesity, be able to read English at least at a grade 5 level, and have a mobile phone or home computer with internet access. Following baseline data collection, participants will be randomized into intervention and waitlist control groups. Intervention participants will receive access to Aim2Be, with access to health coaching. After having their data collected for 3 months, the control group will gain access to Aim2Be, with no access to health coaching. Participants will control their frequency and duration of app usage to promote autonomy.

Discussion

Findings from this study will determine the efficacy of using Aim2Be in improving child weight outcomes and lifestyle behaviors and guide future mHealth interventions for pediatric weight management.

Trial registration

ClinicalTrials.gov, NCT03651284. Registered 29 August 2018.

Clusters of health behaviours and their relation to body mass index among adolescents in Northern Finland

AIMS

To identify clusters of health behaviours among Finnish adolescents and examine whether these clusters are associated with body mass index (BMI).

METHODS

The data, drawn from the prospective population-based Northern Finland Birth Cohort 1986, comprised a total of 7182 adolescents who replied to a follow-up postal questionnaire at the age 15 to 16. The adolescents were asked about multiple health behaviours, such as physical activity, screen time, cigarette smoking, alcohol use and diet. Adolescents who answered the questions concerning all of these variables were included in the study, yielding a final sample of 4305 subjects. K-means cluster analysis was performed to identify group of adolescents with similar health behaviours. Chi-squared tests were performed to analyse the association between the clusters and BMI.

RESULTS

Two clusters - Healthy Lifestyle and Unhealthy Lifestyle - were identified for both genders. The values of all the studied variables differed significantly between the clusters identified for both boys and girls. No significant relationship between the identified clusters and BMI was found for either boys or girls.

CONCLUSIONS

It is important to consider multiple lifestyle-related behaviours and trying to identify groups that present probability for developing lifestyle diseases.

Activity-related behavior typologies in youth: a systematic review

BACKGROUND

Clusters of adolescents differentiated by patterns of physical activity and sedentary behavior (activity-related typologies) are common. Understanding both the characteristics of adolescents and modifiable correlates of these typologies, can help to develop interventions for those most at risk. This systematic review aimed to synthesize the socio-demographic characteristics and modifiable correlates of activity-related behavioral typologies among adolescents.

METHODS

A systematic search of seven electronic databases was conducted to identify quantitative studies using person-oriented statistical approaches to identify activity-related behavioral typologies among 12-18 year-olds. This systematic review was registered in Prospero (registration number: CRD42016046879).

RESULTS

Thirty-six studies met the inclusion criteria and were classified according to three sub-themes based on behaviors included in the typologies (1. physical activity and sedentary behavior only; 2. physical activity, sedentary behavior and risk-related behaviors; 3. physical activity, sedentary behavior and diet). Studies were mostly cross-sectional and relied on self-report measures. Methods were considerably heterogeneous, however results revealed some consistency in typologies within specific groups. For example, typologies characterized by unhealthy behavior patterns (e.g., characterized by physical inactivity, high sedentary behavior and poor diet or high risk-related behaviors) comprised more older adolescents. With the exception of socio-demographics (age, sex, body mass index and socio-economic status), very few correlates have been studied to date (mostly school-related behavioral factors and intrapersonal influences), with evidence largely from typologies comprised of physical activity, sedentary behavior and diet.

CONCLUSIONS

More research is needed to assess a range of modifiable correlates associated with activity-related behavior typologies among adolescents. This will allow for more targeted interventions, to achieve long-lasting, positive behavior change in adolescent populations.

Physical Activity, Screen Time, and Dietary Intake in Families: A Cluster-Analysis With Mother-Father-Child Triads

Background: The co-occurrence of multiple health behaviors such as physical activity, diet, and sedentary behavior affects individuals' health. Co-occurence of different health behaviors has been shown in a large number of studies. This study extended this perspective by addressing the co-occurrence of multiple health behaviors in multiple persons. The objective was to examine familial health behavioral patterns by (1) identifying clusters of families with similar behavior patterns and (2) characterizing the clusters by analyzing their correlates.

Methods: Cross-sectional data were collected from 198 families (mother, father, and child). Mothers, fathers, and children completed questionnaires assessing health related behaviors (physical activity, consumption of “healthy” and “unhealthy” foods, and screen time), the perception of Family Health Climate (regarding physical activity and nutrition) and demographics. Twelve variables (four health behaviors of three family members) were included in a cluster analysis conducted with Ward's Method and K-means analysis. Chi-square tests and analyses of variance were performed to characterize the family clusters regarding their demographics and their perception of Family Health Climate.

Results: Three clusters of families with specific behavioral patterns were identified: “healthy behavior families” with levels of physical activity and consumption of healthful foods above average and levels of media use and consumption of sweets below average; “unhealthy behavior families” with low levels of consumption of healthful foods and high levels of screen time; “divergent behavior families” with unhealthier behavioral patterns in parents and healthier screen time and eating behaviors combined with low physical activity levels in children. Family Health Climate differed between family clusters with most positive ratings in “healthy behavior families” and least positive ratings in “unhealthy behavior families.” “Divergent behavior families” rated the nutrition climate nearly as high as “healthy behavior families” while they rated the physical activity climate nearly as low as the “unhealthy behavior families.”

Conclusions: The study shows that co-occurrence of multiple health behaviors occurs on the family level. Therefore, focusing the family as a whole instead of individuals and targeting aspects related to the Family Health Climate in interventions could result in benefits for both children and adults and enhance effectivity of intervention programs.

Fluid intake patterns of children and adolescents: results of six Liq.In7 national cross-sectional surveys

PURPOSE

This study aimed to identify and characterize patterns of fluid intake in children and adolescents from six countries: Argentina, Brazil, China, Indonesia, Mexico and Uruguay.

METHODS

Data on fluid intake volume and type amongst children (4-9 years; N = 1400) and adolescents (10-17 years; N = 1781) were collected using the validated 7-day fluid-specific record (Liq.In7 record). To identify relatively distinct clusters of subjects based on eight fluid types (water, milk and its derivatives, hot beverages, sugar-sweetened beverages (SSB), 100% fruit juices, artificial/non-nutritive sweetened beverages, alcoholic beverages, other beverages), a cluster analysis (partitioning around k-medoids algorithm) was used. Clusters were then characterized according to their socio-demographics and lifestyle indicators.

RESULTS

The six interpretable clusters identified were: low drinkers-SSB (n 523), low drinkers-water and milk (n 615), medium mixed drinkers (n 914), high drinkers-SSB (n 513), high drinkers-water (n 352) and very high drinkers-water (n 264). Country of residence was the dominant characteristic, followed by socioeconomic level, in all six patterns.

CONCLUSIONS

This analysis showed that consumption of water and SSB were the primary drivers of the clusters. In addition to country, socio-demographic and lifestyle factors played a role in determining the characteristics of each cluster. This information highlights the need to target interventions in particular populations aimed at changing fluid intake behavior and improving health in children and adolescents.

Bone mineral density in patients with chronic heart failure: a meta-analysis

Objective

This study aimed to verify the existing relationship between bone mineral density (BMD) and chronic heart failure (CHF) by meta-analysis.

Methods

Databases, including PubMed, Web of Science, and Chinese National Knowledge Infrastructure, published in English or Chinese up to February 28, 2017, were searched for studies on the association between CHF and BMD. Two independent reviewers collected the relevant articles. The standard mean deviation (SMD) and 95% confidence interval were calculated for BMD with fixed- and random-effect models. Subgroup and sensitivity analyses were also conducted.

Results

A total of six studies (552 CHF and 243 non-CHF patients) were included. The results indicated that the patients with CHF had a lower total BMD compared with the non-CHF patients. Similar effects were also observed for femoral neck, arm, leg, and trunk BMD. However, no difference was observed in the lumbar spine BMD. The SMD of total BMD in New York Heart Association classes I or II (NYHA I or II) patients was −0.62, while that in NYHA III or IV patients was −0.87, and the SMD of femoral bone mineral density in NYHA I or II patients was −0.47, while that in NYHA III or IV patients was −1.07. Moreover, vitamin D and parathyroid hormone (PTH) were also found to be associated with CHF.

Conclusion

Patients with CHF had a lower total BMD and femoral neck, arm, leg, or trochanter BMD than patients with non-CHF. Vitamin D reduced, whereas PTH increased, with the severity of CHF. The clinical significance of the present findings remains uncertain and should be confirmed by future studies.