Background: Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong Kong remains unclear. Objective: This study aimed to develop a local AI chatbot and compare the effectiveness of the AI chatbot with a conventional nurse hotline in reducing the level of anxiety and depression among individuals in Hong Kong. Methods: This study was a pilot randomized controlled trial conducted from October 2022 to March 2023, involving 124 participants allocated randomly (1:1 ratio) into the AI chatbot and nurse hotline groups. Among these, 62 participants in the AI chatbot group and 41 in the nurse hotline group completed both the pre- and postquestionnaires, including the GAD-7 (Generalized Anxiety Disorder Scale-7), PHQ-9 (Patient Health Questionnaire-9), and satisfaction questionnaire. Comparisons were conducted using independent and paired sample t tests (2-tailed) and the χ² test to analyze changes in anxiety and depression levels. Results: Compared to the mean baseline score of 5.13 (SD 4.623), the mean postdepression score in the chatbot group was 3.68 (SD 4.397), which was significantly lower (P=.008). Similarly, a reduced anxiety score was also observed after the chatbot test (pre vs post: mean 4.74, SD 4.742 vs mean 3.4, SD 3.748; P=.005), respectively. No significant differences were found in the pre-post scores for either depression (P=.38) or anxiety (P=.19). No statistically significant difference was observed in service satisfaction between the two platforms (P=.32). Conclusions: The AI chatbot was comparable to the traditional nurse hotline in alleviating participants’ anxiety and depression after responding to inquiries. Moreover, the AI chatbot has shown potential in alleviating short-term anxiety and depression compared to the nurse hotline. While the AI chatbot presents a promising solution for offering accessible strategies to the public, more extensive randomized controlled studies are necessary to further validate its effectiveness.

Despite concerns about the negative effects of social distancing and prolonged school closures on children’s lifestyle and physical activity (PA) during the COVID-19 pandemic, robust evidence is lacking on the impact of the pandemic-related school closures and social distancing on children’s wellbeing and daily life. This study aimed to examine changes in the PA levels, sleep patterns, and screen time of school-aged children during the different phases of the COVID-19 outbreak in Hong Kong using a repeated cross-sectional design. School students (grades 1 to 12) were asked to report their daily electronic device usage and to fill in a sleep diary, recording their daily sleep onset and wake-up time. They were equipped with a PA monitor, Actigraph wGT3X-BT, to obtain objective data on their PA levels and sleep patterns. Students were recruited before the pandemic (September 2019–January 2020; n = 577), during school closures (March 2020–April 2020; n = 146), and after schools partially reopened (October 2020–July 2021; n = 227). Our results indicated lower PA levels, longer sleep duration, and longer screen time among participants recruited during school closures than those recruited before the COVID-19 outbreak. Primary school students were found to sleep on average for an extra hour during school closures. The later sleep onset and increased screen time documented during school closures persisted when schools partially reopened. Our findings illustrate the significant impact of social distancing policies during the COVID-19 pandemic on the sleep pattern, screen time, and PA level in school-aged children in Hong Kong. Professionals should urgently reinforce the importance of improving physically activity, good sleep hygiene, and regulated use of electronic devices for parents and school-aged children during this unprecedented time.

Background: The SARS-CoV-2 Omicron BA.2 subvariant replaced BA.1 globally in early 2022, and caused an unprecedented tsunami of cases in Hong Kong, resulting in the collapse of elimination strategy. Vaccine effectiveness (VE) of BNT162b2 and CoronaVac against BA.2 is unclear. Methods: We utilize an ecological design incorporating population-level vaccine coverage statistics and territory-wide case-level SARS-CoV-2 infection surveillance data, and investigate the VE against infection during the Omicron BA.2 wave between January 1 to April 19, 2022, in Hong Kong for children and adolescents. Results: We estimate VE to be 33.0% for 1 dose of BNT162b2 in children aged 5–11 and 40.8% for 2 doses of CoronaVac in children aged 3–11. We also estimate 54.9% VE for 2 doses of BNT162b2, and 55.0% VE for 2 doses of CoronaVac in adolescents aged 12–18. Conclusions: Our findings support partly preserved VE against infection by variants of concerns for children and adolescents in settings with extremely low levels of prior SARS-CoV-2 circulation.

There is lacking a population-based study on the fitness level of Hong Kong schoolchildren, and it seems that increasing childhood obesity prevalence has shifted the classification of healthy fitness, with ‘underfit’ as normal. This cross-sectional territory study aimed to develop an age- and sex-specific physical fitness reference using a representative sample of children aged 6–17 and to determine the associations with body mass index in schoolchildren. The study analyzed Hong Kong School Physical Fitness Award Scheme data covering grade 1 to grade 12 students’ physical fitness and anthropometric measurements from 2017 to 2018. This reference was established without the impact due to COVID-19. Four aspects of physical fitness tests were measured using a standardized protocol, including (i) upper limb muscle strength, (ii) one-minute sit-up, (iii) sit-and-reach, and (iv) endurance run tests. The generalized additive model for location, scale, and shape was used to construct the reference charts. A Mann–Whitney U test was used to compare the mean differences in age, weight, and height, and a Pearson’s chi-square test was used to examine the distributions of sex groups. A Kruskal–Wallis test was used to compare the group differences in BMI status, followed by the Dunn test for pairwise comparisons. A 5% level of significance was regarded as statistically significant. Data of 119,693 students before the COVID-19 pandemic were included in the analysis. The association between physical fitness level and BMI status varied depending on the test used, and there were significant differences in fitness test scores among BMI groups. The mean test scores of the obese group were lower in most of the tests for both boys and girls, except for handgrip strength. The underweight group outperformed the obese group in push-ups, one-minute sit-ups, and endurance run tests, but not in handgrip strength. In conclusion, a sex- and age-specific physical fitness reference value for Hong Kong Chinese children aged 6 to 17 years old is established, and this study demonstrated a nonlinear relationship between BMI status and physical fitness. The reference will help to identify children with poor physical fitness to offer support and guidance on exercise training. It also serves as a baseline for assessing the impact of the COVID-19 pandemic on Hong Kong students’ physical fitness.

Despite concerns about the negative effects of social distancing and prolonged school closures on children’s lifestyle and physical activity (PA) during the COVID-19 pandemic, robust evidence is lacking on the impact of the COVID-19 pandemic on children’s wellbeing and daily life. This study aimed to examine changes in the PA levels, sleep patterns and screen time of school-aged children during the different phases of COVID-19 outbreak in Hong Kong using a repeated cross-sectional design. School students (Grades 1 to 12) were asked to report their daily electronic device usage and to fill in a sleep dairy recording their daily sleep and wake-up time. They were equipped with a PA monitor, Actigraph wGT3X-BT, to obtain objective data on their PA levels and sleep patterns. Students were recruited before the pandemic (Sep 2019 – Jan 2020; n=577), during school closures (Mar 2020 – Apr 2020; n=146), and after schools partially reopened (Oct 2020 – Jul 2021; n=227). Our results indicated lower PA levels, longer sleep duration, and longer screen time among participants recruited during school closures than those recruited before the COVID-19 outbreak. Primary school students were found to sleep on average for an extra hour during school closures. Our findings illustrate the impact of social distancing policies during the COVID-19 pandemic on the sleep pattern, screen time, and PA level in school-aged children in Hong Kong. Professionals should reinforce the importance of maintaining a physically active lifestyle, good sleep hygiene, and healthy use of electronic devices to parents and school-aged children during the COVID-19 Pandemic.

Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures.