Association Between Longitudinal Trajectories of Lifestyle Pattern and BMI in Early Childhood

A lifestyle screening tool for young children in the community: needs and wishes of parents and youth healthcare professionals

BACKGROUND

Youth healthcare has an important role in promoting a healthy lifestyle in young children in order to prevent lifestyle-related health problems. To aid youth healthcare in this task, a new lifestyle screening tool will be developed. The aim of this study was to explore how youth healthcare professionals (YHCP) could best support parents in improving their children's lifestyle using a new lifestyle screening tool for young children.

METHODS

We conducted four and seven focus groups among parents (N = 25) and YHCP (N = 25), respectively. Two main topics were addressed: the experiences with current practice of youth healthcare regarding lifestyle in young children, and the requirements for the lifestyle screening tool to be developed. The focus groups were recorded, transcribed verbatim and analysed using an inductive approach.

RESULTS

Both parents and YHCP indicated that young children's lifestyles are often discussed during youth healthcare appointments. While parents felt that this discussion could be more in-depth, YHCP mainly needed clues to continue the discussion. According to parents and YHCP, a new lifestyle screening tool for young children should be easy to use, take little time and provide courses of action. Moreover, it should be attractive to complete and align with the family concerned.

CONCLUSIONS

According to parents and YHCP, a new lifestyle screening tool for young children could be useful to discuss specific lifestyle topics in more detail and to provide targeted advice.

Trajectories of lifestyle patterns from 2 to 8 years of age and cardiometabolic risk in children: the GUSTO study

BACKGROUND

Tracking combinations of lifestyle behaviours during childhood ("lifestyle pattern trajectories") can identify subgroups of children that might benefit from lifestyle interventions aiming to improve health outcomes later in life. However, studies on the critical transition period from early to middle childhood are limited. We aimed to describe lifestyle patterns trajectories in children from 2 to 8 years of age and evaluated their associations with cardiometabolic risk markers at age 8 years in a multi-ethnic Asian cohort.

METHODS

Twelve lifestyle behaviours related to child's diet, physical activity, screen use, and sleep were ascertained using questionnaires at ages 2, 5, and 8 years. Age-specific lifestyle patterns were derived using principal component analysis and trajectories were determined using group-based multi-trajectory modelling. Child cardiometabolic risk markers were assessed at age 8 years, and associations with trajectories examined using multiple regression, adjusted for confounders.

RESULTS

Among 546 children, two lifestyle patterns "healthy" and "unhealthy" were observed at ages 2, 5, and 8 years separately. Three trajectory groups from 2 to 8 years were identified: consistently healthy (11%), consistently unhealthy (18%), and mixed pattern (71%). Children in the consistently unhealthy group (vs. mixed pattern) had increased odds of pre-hypertension (OR = 2.96 [95% CI 1.18-7.41]) and higher levels of diastolic blood pressure (β = 1.91 [0.27-3.55] mmHg), homeostasis model assessment of insulin resistance (β = 0.43 [0.13-0.74]), triglycerides (β = 0.11 [0.00-0.22] mmol/L), and metabolic syndrome score (β = 0.85 [0.20-1.49]), but not with BMI z-score or any anthropometric measurements. The consistently healthy group showed no differences in cardiometabolic outcomes compared to the mixed pattern group.

CONCLUSION

Three distinct lifestyle pattern trajectories were identified from early to middle childhood. Children in the consistently unhealthy lifestyle group did not have a raised BMI but was associated with several elevated cardiometabolic risk markers. These findings suggest the potential benefits of initiating holistic lifestyle interventions to improve children's health and well-being from an early age.

TRIAL REGISTRATION

Trial registration number: NCT01174875. Name of registry: ClinicalTrials.gov. URL of registry: https://classic.

CLINICALTRIALS

gov/ct2/show/NCT01174875 . Date of registration: August 4, 2010. Date of enrolment of the first participant to the trial: June 2009.

Development and evaluation study of FLY-Kids: a new lifestyle screening tool for young children

Evaluating, discussing, and advising on young children's lifestyles may contribute to timely modification of unhealthy behaviour and prevention of adverse health consequences. We aimed to develop and evaluate a new lifestyle screening tool for children aged 1-3 years. The lifestyle screening tool "FLY-Kids" was developed using data from lifestyle behaviour patterns of Dutch toddlers, age-specific lifestyle recommendations, target group analyses, and a Delphi process. Through 10 items, FLY-Kids generates a dashboard with an overview of the child's lifestyle that can be used as conversation aid. FLY-Kids was completed by parents of children aged 1-3 years attending a regular youth healthcare appointment. Youth healthcare professionals (YHCP) then used the FLY-Kids dashboard to discuss lifestyle with the parents and provided tailored advice. Parents as well as YHCP evaluated the tool after use. Descriptive and correlation statistics were used to determine the usability, feasibility, and preliminary effect of FLY-Kids. Parents (N = 201) scored an average of 3.2 (out of 9, SD 1.6) unfavourable lifestyle behaviours in their children, while 3.0% complied with all recommendations. Most unfavourable behaviours were reported in unhealthy food intake and electronic screen time behaviour. Parents and YHCP regarded FLY-Kids as usable and feasible. The number of items identified by FLY-Kids as requiring attention was associated with the number of items discussed during the appointment (r = 0.47, p < 0.001).     Conclusion: FLY-Kids can be used to identify unhealthy lifestyle behaviour in young children and guide the conversation about lifestyle in preventive healthcare settings. End-users rated FLY-Kids as helpful and user-friendly. What is Known: • A healthy lifestyle is important for optimal growth, development and overall health of young children (1-3 years). • Evaluating, discussing and advising on young children's lifestyles may contribute to timely modification of unhealthy behaviour and prevention of adverse health consequences. What is New: • The new lifestyle screening tool FLY-Kids generates a dashboard with an overview of young children's lifestyle that can be used as conversation aid between parents and youth healthcare professionals. • As parents and youth healthcare professionals rated FLY-Kids as helpful and user-friendly, and the number of items identified by FLY-Kids as requiring attention was associated with the number of items discussed during the appointment, FLY-Kids can be considered guiding the lifestyle discussion in preventive healthcare settings.

Evaluation and interpretation of latent class modelling strategies to characterise dietary trajectories across early life: a longitudinal study from the Southampton Women's Survey

There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother-child dyads from the longitudinal Southampton Women's Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6-7 and 8-9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman's = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards.

Describing the longitudinal breakfast quality index trajectories in early childhood: results from Melbourne InFANT program

BACKGROUND

Breakfast quality in early childhood remains understudied. This study describes the changes in breakfast quality index (BQI) (i.e. trajectory) in early childhood and assesses its associations with obesity outcomes.

METHODS

Data from children who participated in the Melbourne InFANT Program were used (n = 328). The Melbourne InFANT Program was a 15-month early obesity prevention intervention conducted from 2008 to 2013. Dietary intakes at ages 1.5, 3.5 and 5.0 years were assessed using three parent-proxy reported 24 h recalls. A revised nine-item BQI tool developed based on Australian dietary recommendations for young children was used to calculate BQI scores. Group-based trajectory modelling identified BQI trajectory groups. Multivariable linear and logistic regression examined the associations between identified BQI trajectory groups and obesity outcomes at age 5 years.

RESULTS

Mean BQI at ages 1.5, 3.5 and 5.0 years was 4.8, 4.8, 2.7 points, respectively. Two BQI trajectory groups were identified, and both showed a decline in BQI. The mean BQI of most children (74%) decreased from 5.0 to 4.0 points from ages 1.5 to 5.0 years (referred as "High BQI" group). The remaining children (26%) had a mean BQI of 4.8 and 1.2 points at age 1.5 and 5.0 years, respectively (referred as "Low BQI" group). The "Low BQI" group appeared to show higher risk of overweight (OR:1.30, 95% CI: 0.60, 2.81, P = 0.66) at age 5 years than the "High BQI" group. No difference in body mass index (BMI) z-score was found between the two groups.

CONCLUSIONS

Two BQI trajectory groups were identified. Both groups showed a decline in breakfast quality from ages 1.5 to 5.0 years. Our study highlights the need for early health promotion interventions and strategies to improve and maintain breakfast quality across early childhood.

Dietary trajectories through the life course: opportunities and challenges

Studying the dynamic patterns of dietary changes or stability (otherwise known as dietary trajectories) across the life course can provide important information about when and in whom to intervene with nutritional interventions. This article reviews evidence from longitudinal studies that describe dietary trajectories through the different life stages, covering early life, adolescence to young adulthood and from mid to late adulthood. Current findings suggest that the establishment of diet patterns likely occurs before 3 years of age and allude to other potential ‘windows of change’ in the life course such as the period of 7–9 years of age and during the period of adolescence and early adulthood. Examining diets using various diet parameters appears to be valuable in elucidating different aspects of the diet that can be changed to potentially alter trajectories. In adults, examining long-term diet trends at a population level can reveal shifts in eating patterns as countries undergo epidemiological and nutrition transitions and elucidate the longer-term impact of adherence to particular diets on the development of chronic diseases. While challenges such as the availability of adequate diet data points, consistency in the dietary assessment tools used and the limitations of statistical methods for trajectory modelling remain, integrating diet data with other lifestyle behaviours, high-dimensional biomarkers and genetics data into pattern analyses and examining them from a longitudinal approach, open up potential opportunities to gain deeper insights into diet–disease relationships and support the development of more holistic lifestyle disease prevention recommendations stratified for population groups.