Participants were recruited from the five main regions in Hong Kong including Hong Kong Island, West Kowloon, East Kowloon, West New Territories, and East New Territories. Stratified random sampling was employed to randomly select primary and secondary schools from each district. This sampling method was previously used in the local prevalence study, which was published in a peer-reviewed journal.9 Parental written consents were obtained prior to the study. Each student and their parent received a package containing questionnaires and a physical activity (PA) monitor (Actigraph wGT3X-BT, Pensacola, Florida, USA). The wearable device has been widely utilized in previous peer-reviewed publications for monitoring PA and sleep in children.9,10

The questionnaires were completed by one of the parents and the students to collect demographic and PedsQL data. Additionally, students were instructed to keep a sleep diary to record their sleep times and wake-up times daily.

The PedsQL tool is a validated flexible method used to assess HRQoL in both healthy children and adolescents, as well as those with short-term and long-term health conditions.11

The non-wear time and sleep period were classified based on a previous study.12 The physical activity level was calculated using the sum of vector magnitude (VM) in 60 s epochs which were processed using ActiLife software version 6.13.4. Cut-offs of PA and sedentary behavior according to Chandler et al. were used to quantify the time spent in sedentary behavior (VM < 3672 per min), light PA (3672 ≤ VM per min < 9816), moderate PA (9816 ≤ VM per min < 23,628), and moderate-to-vigorous physical activity (MVPA) (VM per min ≥ 23,628).13

The statistical analyses were carried out using R software, version 4.0.3 (R Program for Statistical Computing). BMI was computed by dividing weight in kilograms (kg) by the square of height in meters (m²). Participants' BMI was categorized into underweight, healthy weight, overweight, and obese groups based on the International Obesity Task Force age- and sex-specific standards.14 The underweight group was defined using international cut-off points that pass through a BMI of 17 at age 18, which aligns with the World Health Organization's standard of thinness. On the other hand, the overweight and obese groups were categorized using cut-off points passing through BMI 25 and 30 at age 18, respectively. The children, except for those in the healthy weight group, were classified as being in the non-healthy weight group.

Descriptive analysis was performed to investigate the demographic characteristics of the students, such as age, sex, anthropometric measurements, variables used for index construction, and PedsQL outcomes. Potential outliers were examined and eliminated if the values deviated by three or more standard deviations from the mean of the group, taking into account age and sex.

HRQoL is a multidimensional concept that encompasses physical, emotional, social, and cognitive well-being. Understanding the factors that contribute to HRQoL in young individuals is crucial for promoting their overall health and well-being. Possible factors of HRQoL were described in a model of a previous study.15,16 The construction of the YouthFit Index included these variables which were separated into three groups: protective factors, risk factors, and demographics. Protective factors included average sleep duration and MVPA time per day on weekdays and weekends. Risk factors included average sedentary time and bedtime per day on weekdays and weekends. Demographic variables included age and BMI status.

Multiple factor analysis (MFA) of mixed data was then used to identify the principal components (PCs) of these variables. MFA is a multivariate analysis method capable of handling both continuous and categorical variables that can be grouped into several groups.17 The weights assigned to variables within the same group were equal, giving equal importance to each group. According to Chavent et al. (2014),18 the PCs are defined as the following:

Since the PCs calculated can vary from positive to negative and may not be suitable for direct use as an index, min-max normalization and a revised scaling technique were applied. This transformation standardized and rescaled the PCs to values between 0 and 10. Higher scores indicated better performance. The formula used was:

The YouthFit Index was calculated by summing the weighted PCs. The weight assigned to each PC was based on the proportion of variance explained by that PC. The formula for calculating the index is:

The YouthFit Index can also be represented as an equation of the original dataset, utilizing equations (i), (ii), and (iii) as shown below:

The total PedsQL scores were classified into risk and normal groups based on the clinically meaningful cut-off established by Huang et al.19 Participants who fell into the risk group were potentially identified as having special healthcare needs or chronic conditions.

The YouthFIt Index was subsequently divided into two groups using Receiver Operating Characteristic (ROC) curve analysis in relation to the PedsQL total score. Logistic regression models were utilized to assess the odds ratios (OR) with 95 % confidence intervals (CI) for risks associated with the YouthFIt Index categories with and without adjustment of sex.

A total of 1867 students were recruited for the study during the past 4 years, including 831 primary students and 1036 secondary students. Table 1 provides a summary of the student's characteristics. Among the students, 59.6 % were female, and the average age was 12.4 years. Approximately 76.0 % of the students had a healthy weight. The average time spent in MVPA per day was similar between weekdays and weekends, with 0.50 h and 0.51 h, respectively. However, students had a longer average sedentary time per day on weekdays (11.2 h) compared to weekends (10.3 h). It was observed that students tended to go to bed later during weekends (00:02) compared to weekdays (23:26), but they had a longer sleep duration on weekends (9.6 h) compared to weekdays (7.2 h). The average PedsQL score (78.0 ± 13.3) and the scores for the four scales - physical functioning (82.0 ± 14.3), emotional functioning (70.7 ± 20.3), social functioning (83.1 ± 16.6), and school functioning (75.8 ± 16.4) - closely aligned with the norm recently published in Hong Kong.2

Table 2 shows the variables used in MFA. The variables were grouped into 3 dimensions: 1) Protective factors including weekday and weekend average MVPA time (WkPA and WdPA) and sleep duration (WkSL and WdSL); 2) Risk factors including weekday and weekend average bedtime (WkBD and WdBD) and sedentary time (WkSD and WdSD); 3) Demographic including age and BMI status. Supplemental Appendices 1 and 2 illustrate the percentage of variance explained by and the squared loadings of variables to each PC. All commonalities of each PC exceeded the minimum cut-off value of 0.25.20

Table 3 presents the coefficients of expression (i). Based on expressions (iii) and (iv), the final formula for calculating the YouthFit Index is as follows:

The ROC curve analysis indicated that a cut-off value of 5 is optimal for differentiating between the risk group and the normal group based on the PedsQL total score. Table 4 displays the odds ratios of PedsQL scores falling below the cut-off based on the YouthFit Index group. The findings indicate that having YouthFit Index scores greater than 5 is associated with a significantly lower risk of identifying with special health care needs. Compared to the group with YouthFit Index scores below or equal to 5, the group with scores above the cut-off demonstrated significant reductions in the risk of PedsQL total score by 43 % (CI 31 %–53 %), physical functioning by 44 % (CI 32 %–54 %), emotional functioning by 36 % (CI 23 %–46 %), social functioning by 49 % (CI 38 %–58 %), and school functioning by 27 % (CI 11 %–41 %).

The YouthFit Index offers several potential implications and utilities for promoting the health and well-being of children. Firstly, the YouthFit Index is a simple score ranging from 0 to 10 and the cut-off is age-independent, it is easy to be understood by the public and even among children. It serves as a tool for assessment of the overall health of children. With advances in technology, the YouthFit Index can be easily implemented on webpages or mobile apps, making it accessible to a wider audience. Parents, educators, and healthcare professionals can assess children's HRQoL and make necessary adjustments to their lifestyle habits by inputting factors such as physical activity, sleep patterns, sedentary time, age, and weight status. This allows them to track changes in their children's HRQoL over time, making it easier to identify potential issues and take proactive steps to address them using a single index. Secondly, the YouthFit Index can be employed in longitudinal studies to observe children's HRQoL changes over time and serves as a crucial element in designing and evaluating interventions targeting their HRQoL improvement. By offering a comprehensive measure of multiple lifestyle factors' impact on HRQoL, researchers and practitioners can better gauge the efficacy of various strategies in fostering healthy habits and enhancing overall well-being. This insight assists in developing evidence-based programs and policies targeting young people's health and well-being. This can help refine existing programs or create new, targeted tailored interventions to boost children's HRQoL.

One of the main strengths of the present study is the development of a comprehensive index that incorporates multiple lifestyle factors relevant to children's HRQoL. The YouthFit Index incorporated multiple lifestyle factors (physical activity, sedentary behavior, and BMI status) and their impact on children's health-related quality of life. It provides a more holistic view of children's well-being compared to existing indices that focus on single aspects of health or lifestyle behaviors. Another strength of the study is this study is the utilization of objective data obtained from accelerometers worn by the participants, which offers a more reliable assessment compared to self-reported questionnaires. This objective data collection method enhances the reliability and accuracy of the measurements, reducing potential biases and record fatigue associated with parental assessment. Furthermore, the diverse sample of Hong Kong Chinese children between the ages of 6 and 17 adds generalizability to the findings and ensures that the YouthFit Index is applicable to a broad range of children within this population.

However, there are limitations to consider, such as the cross-sectional design, which does not allow for the assessment of causal relationships between lifestyle factors and HRQoL. Future studies employing a longitudinal design could provide valuable insights into the direction and strength of these associations over time. Additionally, the YouthFit Index was developed and validated in a specific population of Hong Kong Chinese children. Although this enhances its applicability to this demographic, it may limit its generalizability to children from other cultural backgrounds or geographical locations. Further research is needed to assess the validity and utility of the YouthFit Index in different populations.

In conclusion, the YouthFit Index is a user-friendly tool for assessing the HRQoL of Hong Kong Chinese children between the ages of 6 and 17. It can help raise awareness about the importance of a healthy quality of life in Hong Kong children. Its application in various settings, from individual assessments to large-scale population studies, may help inform the development of targeted interventions and policies that promote health and well-being among young people, ultimately leading to a healthier society and a more efficient healthcare system.

This study develops a YouthFit Index which is a comprehensive tool for assessing children's health-related quality of life. It can be used for individual assessments or large-scale population studies, aiding the development of interventions and policies that promote well-being among young people and improve healthcare efficiency.