Adolescents With Severe Obesity Had an Increased Risk of Reporting Symptoms That Indicated They Had Major Depression

ABSTRACT

Aim

This study aimed to compare the severity of depressive symptoms of Hong Kong adolescents with and without obesity and to explore the correlates of depressive symptoms using Patient Health Questionnaire‐9 (PHQ‐9).

Methods

Participants were recruited from the Paediatric Weight Control Clinic of Queen Mary Hospital or mainstream secondary schools in Hong Kong during the period of June 2023 and December 2023. This was a cross‐sectional study comparing depression risk among adolescents with and without obesity. Subgroup analysis was conducted to examine the risk factors.

Results

A total of 612 adolescents were included for analysis. No significant difference in PHQ‐9 scores ≥ 10 was found between normal weight and obesity groups after socio‐economic status (SES) adjustment. A positive correlation between BMI z‐score and PHQ‐9 was observed in adolescents with obesity, especially beyond the 99.6th percentile BMI. BMI ≥ 99.6% was identified as a significant risk factor for depression, without collinearity.

Conclusion

Using the BMI ≥ 95th percentile to define obesity, no link was found between obesity and depression risk in Hong Kong adolescents. However, our study showed that adolescents with severe obesity (BMI ≥ 99.6th percentile) have a significantly higher risk of depressive symptoms. We recommend routine depression screening for adolescents with severe obesity.

Abbreviations

BMI

body mass index

OR

odd ratio

PHQ‐9

Patient Health Questionnaire‐9

SES

socio‐economic status

WAT

white adipose tissue

Summary.

• This study assessed depression risk in 612 Hong Kong adolescents with and without obesity, using Patient Health Questionnaire‐9 (PHQ‐9).

• No significant difference was found between groups after Socioeconomic status (SES) adjustment. However, severe obesity (Body Mass Index—BMI ≥ 99.6th percentile) was linked to a higher risk of depressive symptoms, with a positive correlation between BMI z‐score and PHQ‐9.

• Routine depression screening is recommended for adolescents with severe obesity.

1. Aim

Childhood obesity is a serious global health concern. In Hong Kong, from 2010 to 2019, the rate of secondary school students with obesity has increased from 4% to 21% [1]. Obesity may negatively impact the mental health of adolescents. Multiple studies have shown conflicting findings on the relationship between obesity and depression. While some studies have shown that obesity was positively associated with depression (odd ratio, OR 1.3–1.9) in both adult and paediatric populations [2, 3, 4, 5, 6, 7], other studies have either concluded no significant differences [8, 9, 10, 11, 12] or even an inverse relationship between obesity and depression [13, 14, 15, 16, 17, 18]. In 2023, the American Academy of Paediatrics issued guidelines advocating the use of the Patient Health Questionnaire‐9 (PHQ‐9) to assess the psychosocial functioning of adolescents with obesity [19].

Over the past decade, there have been no data available on depression in adolescents with obesity in Hong Kong. Numerous studies have suggested that the rate of mental health problems in adolescents has increased since the COVID‐19 pandemic [20, 21, 22, 23]. Therefore, there is a need to assess whether adolescents with obesity are at a higher risk. The aim of this study was to evaluate the severity of depressive symptoms in Hong Kong adolescents with and without obesity and explore the risk factors that correlate with depressive symptoms in adolescents with obesity.

2. Method

2.1. Study Design

This was a cross‐sectional study comparing adolescents with obesity to those of normal weight. Participants were recruited from either the Paediatric Weight Control Clinic at Queen Mary Hospital or from mainstream secondary schools. All clinic participants had obesity, while school participants included both adolescents with normal weight and those with obesity. All participants were enrolled in mainstream schools. Unless formally assessed and diagnosed with intellectual disabilities, students were presumed to have intelligence within the normal range.

The primary objective was to compare the risk of major depression (indicated by a PHQ‐9 score of ≥ 10) between adolescents with obesity and without obesity in Hong Kong. A score of ≥ 10 on the PHQ‐9 has been associated with the highest sensitivity (88%) and specificity (85%) for major depression [24, 25]. It was also the most adopted cutoff in other centres [26]. The secondary objective was to investigate the associated risk factors, such as obesity‐related complications, Body Mass Index (BMI), severity of obesity, age, gender, and socio‐economic status.

Cases were defined as adolescents with obesity (BMI ≥ 95th percentile), and controls were defined as adolescents with normal weight (BMI > 5th and < 85th percentile), based on the Hong Kong 1993 BMI‐for‐age chart. To enhance the generalisability of the study, adolescents with obesity from both clinical and community settings were combined into a single group, capturing the full spectrum of obesity severity.

Due to significant age and gender differences between the groups with and without obesity, a randomised 1:1 matching was performed. Normal‐weight adolescents were matched to adolescents with obesity based on age and gender distribution using computer‐generated randomisation (Figure 1). Subgroup analyses (Table 1) were conducted to further explore potential risk factors and group‐specific characteristics.

FIGURE 1.

Study design.

TABLE 1.

Descriptive subgroup analysis of adolescent with severe obesity, non‐severe obesity and normal weight.

	Severe obesity (N = 24)			Non‐severe obesity (N = 282)
	Mean/N	SD/%	Range	Mean/N	SD/%	Range	p
Sex
F	22	91.7%		71	25.2%		< 0.001
M	2	8.3%		211	74.8%
Setting
Clinic	19	79.2%		165	58.5%		0.047
Community	5	20.8%		117	41.5%
Age	14.04	1.81	11–17	13.53	1.81	11–17	0.182
BMI	36.70	4.36	32.30–47.40	28.86	3.43	22.00–38.46	< 0.001
BMI z‐score	2.87	0.16	2.68–3.38	2.09	0.28	1.65–2.67	< 0.001
SES (range 1–18)	14.00	5.37	1–18	13.35	5.02	1–18	0.548
PHQ‐9	10.04	7.12	0–24	4.43	4.48	0–27	< 0.001
PHQ‐9 group
< 10	11	45.8%		247	87.6%		< 0.001
≥ 10	13	54.2%		35	12.4%

	Severe obesity (N = 24)			Normal (N = 306)			p
	Mean/N	SD/%	Range	Mean/N	SD/%	Range
Sex
F	22	91.7%		93	30.4%		< 0.001
M	2	8.3%		213	69.6%
Setting
Clinic	19	79.2%		0	0.0%		< 0.001
Community	5	20.8%		306	100.0%
Age	14.04	1.81	11–17	13.57	1.81	11–17	0.227
BMI	36.70	4.36	32.30–47.40	18.55	2.04	32.30–47.40	< 0.001
BMI z‐score	2.87	0.16	2.68–3.38	0.01	0.70	2.68–3.38	< 0.001
SES (range 1–18)	14.00	5.37	1–18	12.13	6.13	1–18	0.116
PHQ‐9	10.04	7.12	0–24	5.73	5.75	0–24	0.008
PHQ‐9 group
< 10	11	45.8%		239	78.1%		0.001
≥ 10	13	54.2%		67	21.9%

2.2. Participants and Data Collection

As part of our routine clinical workflow, patients with obesity aged ≥ 11 years who attended the Paediatric Weight Control Clinic were invited to fill out the PHQ‐9 questionnaire to screen for depressive symptoms. Patient data between June 2023 and December 2023 were collected from electronic patient records, including demographic information, medical history, BMI and obesity‐related complications such as hypertension—defined as blood pressure above the 95th percentile for age and gender based on the past three visits, hyperlipidemia, impaired glucose tolerance, diabetes mellitus, fatty liver disease and hyperuricemia.

Secondary school students were recruited from 1st September 2023 to 31st May 2024. The recruitment covered 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 secondary schools from each district. The schools were approached sequentially, starting with the first school and moving on to the next if the invitation was declined. The PHQ‐9 questionnaire was completed by the students. Body height and weight of students in all schools were measured barefoot and in light clothing by trained research staff following a standardised protocol provided by the panel committee. Height was measured using standardised stadiometers to the nearest 0.1 cm, and weight was measured with calibrated weighing scales to the nearest 100 g.

2.3. Inclusion and Exclusion Criteria

The study included adolescents aged 11 to 17 years old. Participants were recruited from weight control clinics with obesity and from schools, including students with either normal weight or obesity. All participants were able to understand either the English or Chinese version of the PHQ‐9 questionnaire. Individuals with known intellectual disabilities were excluded. Only patients with simple obesity were included; those with obesity due to syndromic, secondary endocrinological conditions or weight gain from psychiatric medications, such as antipsychotics, were excluded.

2.4. Statistical Method

Based on a 2018 meta‐analysis in Mainland China [5], which reported an odds ratio of 1.877 from 3548 children and adolescents with obesity and 20 445 controls, a sample size of 88 is needed (80% power, 5% alpha) to assess the increased risk of depressive symptoms in children and adolescents with obesity.

Descriptive statistics were used to investigate the demographic characteristics of the participants, including age, gender, BMI, PHQ‐9 score and socio‐economic status (SES). SES was determined by ranking the median household income of each patient's home district, with a higher ranking indicating a better socio‐economic status. Categorical variables were analysed using the Pearson chi‐squared test, while continuous variables were examined using independent t‐tests. No outliers were identified in this study.

Individuals were classified as being at high risk for major depression if their PHQ‐9 score was 10 or higher. Logistic regression analysis was performed to explore the association between the risk of major depression and obesity status while adjusting for SES. The Pearson chi‐square test and Fisher's z‐transformation were employed to evaluate differences in correlations between PHQ‐9 scores and BMI z‐scores across various groups.

Using the highest percentile line in the Hong Kong 2020 BMI chart (BMI z‐score = 2.68, equivalent to the 99.6th percentile), participants with obesity were divided into two subgroups: those with BMI ≥ 99.6% were classified as having severe obesity, while those with BMI below 99.6% (< 99.6%) were classified as non‐severe obesity (Table 1).

Subgroup analyses (Table 1) were conducted to examine the different characteristics among adolescents in these two subgroups and the normal weight group. Univariable and multiple logistic regression analyses were employed to explore the risk factors associated with an increased risk of major depression in adolescents with obesity. All statistical analyses were performed using R software, version 4.3.0 (The R Program for Statistical Computing, Lucent Technologies, New Jersey, USA).

This study has been approved by the institutional Review Board of the Hospital Authority of Hong Kong, which complies with the Declaration of Helsinki. Parental written consent was obtained before the study commenced.

3. Result

The study recruited a total of 840 adolescents, including 306 adolescents with obesity from the clinic and local schools. The 534 adolescents of normal weight, recruited from local schools, were randomly matched 1:1 by age and gender to the 306 adolescents with obesity (Table 2). Consequently, after matching, 612 adolescents were included in the analysis. Both groups had a mean age of 13.57 (±1.81) years, with 69.6% male participants. The obesity group had a significantly higher mean BMI z‐score of 2.15 (±0.34) compared to 0.01 (±0.70) in the normal weight group (p < 0.001). The mean SES scores were 13.41 (±5.04) for the obesity group and 12.13 (±6.13) for the normal weight group (p = 0.005), indicating higher SES among adolescents with obesity. Nearly all patients in the clinic had at least one obesity‐related complication, except for four individuals. Participants recruited from the weight control clinic were free from major non‐obesity‐related medical conditions such as chronic renal disorders, malignancies, severe autoimmune diseases, syndromic disorders, congenital diseases and Type 1 diabetes. No community participants were attending the weight control clinic at Queen Mary Hospital. No participant from the community was attending the weight control clinic in Queen Mary Hospital.

TABLE 2.

Demographic and characteristics of patients of adolescents with and without obesity following a 1:1 age and gender matching.

	Obese (N = 306)						Normal (N = 306)
	Clinic (N = 184)			Community (N = 122)
	Mean/N	SD/%	Range	Mean/N	SD/%	Range	Mean/N	SD/%	Range	p
Sex
F	56	30.4%		37	30.3%		93	30.4%		1
M	128	69.6%		85	69.7%		213	69.6%
Age	13.82	1.93	11–17	13.19	1.53	11–17	13.57	1.81	11–17	0.011
BMI	30.26	4.01	23.10–47.40	28.28	3.93	22.00–41.74	18.55	2.04	14.27–23.84	< 0.001
BMI z‐score	2.23	0.33	1.65–3.38	2.03	0.33	1.65–3.17	0.01	0.70	−1.55 to 1.02	< 0.001
SES (range 1–18)	14.34	3.90	1–18	11.99	6.14	1–18	12.13	6.13	1–18	< 0.001
PHQ‐9	4.30	4.63	0–24	5.72	5.33	0–27	5.73	5.75	0–24	0.011
PHQ‐9 group
< 10	163	88.6%		95	77.9%		239	78.1%		0.009
≥ 10	21	11.4%		27	22.1%		67	21.9%

The logistic regression showed that the proportion of adolescents with PHQ‐9 scores ≥ 10 was significantly higher in the normal weight group without adjustment (Table 3). However, after adjusting for SES, there was no significant difference between the groups (OR 0.94 for obesity, p = 0.051). A positive correlation between BMI z‐score and PHQ‐9 score was observed in adolescents with obesity (r = 0.177, p = 0.002), but no correlation was found in adolescents without obesity (r = −0.088, p = 0.123). A chi‐squared test indicated that this correlation was significantly more positive among adolescents with obesity (p = 0.001).

TABLE 3.

Descriptive analysis of all adolescents with obesity and normal weight.

	All obese (N = 306)			Normal (N = 306)
	Mean/N	SD/%	Range	Mean/N	SD/%	Range	p
Sex
F	93	30.4%		93	30.4%		1.000
M	213	69.6%		213	69.6%
Age	13.57	1.81	11–17	13.57	1.81	11–17	1.000
BMI	29.47	4.09	22.00–47.40	18.55	2.04	14.27–23.84	< 0.001
BMI z‐score	2.15	0.34	1.65–3.38	0.01	0.70	−1.55 to 1.02	< 0.001
SES (range 1–18)	13.41	5.04	1–18	12.13	6.13	1–18	0.005
PHQ‐9	4.87	4.96	0–27	5.73	5.75	0–25	0.049
PHQ‐9 group
< 10	258	84.3%		239	78.1%		0.049
≥ 10	48	15.7%		67	21.9%

The segmented regression analysis (Figure 2) indicated no significant trend between PHQ‐9 score and BMI z‐score when BMI < 2.68 (99.6th centile), but an increasing trend was observed when BMI z‐score ≥ 2.68 (99.6th centile) (slope before breakpoint = 1.11 vs. after breakpoint = 12.89, p = 0.008).

FIGURE 2.

Segmented regression analysis of PHQ‐9 score with BMI z‐score.

The subgroup analysis (Table 1) indicated that the severe obesity group had the highest proportion of females, clinic‐based adolescents and the highest percentage of adolescents with a PHQ‐9 score of ≥ 10. According to the logistic regression results (Table 4), after adjusting for sex and gender, severe obesity was associated with higher odds ratios compared to the normal population (OR 1.27) and non‐severe obesity (OR 1.46).

TABLE 4.

Relationships between PHQ‐9 and subgroups adjusted by sex and age.

	PHQ‐9 < 10	%	PHQ‐9 > 10	%	Odd ratio	Cl	p
Normal	239	77.9%	67	21.8%	Reference
Severe obese	11	45.8%	13	54.2%	1.27	1.051–1.523	0.013

	PHQ‐9 < 10	%	PHQ‐9 > 10	%	Odd ratio	Cl	p
Non‐severe obesity	247	87.6%	35	12.4%	Reference
Severe obesity	11	45.8%	13	54.2%	1.46	1.245–1.710	< 0.001

The univariable logistic regression analysis (Table 5) showed that severe obesity (OR 1.518, p < 0.001) and female gender (OR 1.156, p = 0.001) were significant risk factors for having a PHQ‐9 score ≥ 10 among individuals with obesity. Age, SES and the total number of complications did not significantly increase the risk of depression.

TABLE 5.

Univariate and multivariate logistic analysis of risk factors for PHQ‐9 ≥ 10 in known adolescents with obesity.

Risk factors	Odds ratio	Cl	t	p
Univariable logistics regression to explore risk factors for PHQ‐9 ≥ 10 in known obese subjects
Group
Non‐severe obesity	Reference
Severe obesity	1.518	1.313–1.756	5.658	< 0.001
Sex
Female	Reference
Male	0.865	0.792–0.944	−3.262	0.001
Age	1.004	0.981–1.027	0.322	0.748
SES	0.995	0.987–1.003	−1.294	0.197
Total complications	0.983	0.955–1.013	−1.120	0.264
Multiple logistics regression to examine risk factors for PHQ‐9 ≥ 10 in known obese subjects
Group
Non‐severe obesity	Reference
Severe obesity	1.459	1.247–1.708	4.724	< 0.001
Sex
Female	Reference
Male	0.942	0.859–1.033	−1.273	0.204

In the multiple logistic regression analysis (Table 5) assessing the combined effects of the significant risk factors, only the severe obesity group remained significant (OR 1.459, p < 0.001). No collinearity was detected between the risk factors based on the variance inflation factor.

4. Discussion

The PHQ‐9 questionnaire used in this study does not provide a diagnosis of major depression but serves as a screening tool for identifying individuals at risk, facilitating prompt referral and recognition. Using the widely adopted cutoff of PHQ‐9 ≥ 10, we could determine the point at which the risk of major depression becomes significant, with higher scores indicating a greater likelihood of depression.

Our study found a higher prevalence of obesity among boys, aligning with data from the Hong Kong Centre for Health Protection, which reported approximately 8% higher overweight and obesity detection rates in boys compared to girls. After adjusting for SES, there was no statistically significant difference in depression risk between the obesity and normal weight groups. Variations across studies may stem from complex factors such as race, sex, socioeconomic status, obesity severity and gene–environment interactions that influence the depression‐obesity relationship. Additionally, differences in screening tools—such as the PHQ‐9, Centre for Epidemiologic Studies Depression Scale, Geriatric Depression Scale and Beck's Depression Inventory—contributed to inconsistent findings, as each has different cutoff points, sensitivities, specificities and validity for paediatric populations. Previous paediatric research indicates that the choice of screening instrument significantly affects the estimated prevalence of depressive symptoms [5].

A 2020 study involving over 10 000 students from Hong Kong aged 11–17 years reported a mean PHQ‐9 score of 6.65 (SD 4.95) [27]. In comparison, the mean PHQ‐9 for our sample with obesity was 4.87 (SD 4.96), slightly lower than the general population. The reason for this discrepancy was unclear but may be due to differences in demographic backgrounds and BMI, which were not assessed in the 2020 study.

For studies that deduced a positive correlation, some postulated obesity and depression have a bi‐directional association [3, 4]. Depressed adolescents may develop eating disorders leading to weight fluctuations, lack of motivation resulting in sedentary lifestyles and sleep disturbances that can contribute to obesity. Conversely, adolescents with obesity often face stigmatisation, poor body image and low self‐esteem, increasing their vulnerability to depression [8]. Inflammation has also been linked to depression, as it can cause the brain to perceive ongoing stress similarly to an active illness [28]. Both animal and human studies have shown that white adipose tissue (WAT) in individuals with obesity produces increased levels of inflammatory molecules such as TNF‐alpha and interleukin‐6, with infiltrating macrophages serving as a key source of pro‐inflammatory cytokines [29].

Regarding the inverse relationship reported by some studies [13, 14, 15, 16, 17, 18], it was first described by Crisp et al. [16], who observed a significant positive association between obesity and lower levels of depression in middle‐aged men. They suggested that periodic overeating might serve as a protective or compensatory mechanism against anxiety and depression [16]. When this comfort mechanism is disrupted—such as through weight loss—the incidence of depression may subsequently increase [16].

Upon examining the correlation between PHQ‐9 scores and BMI in adolescents with obesity, we observed that higher BMI is associated with an increased risk of depression, a correlation that was not present in adolescents without obesity. Segmented regression analysis (Figure 2) indicated that beyond the highest percentile line from the 2020 BMI chart, the link between BMI and the risk of major depression significantly strengthens. This suggests that the risk of depression may initially resemble that of the normal population, but as obesity severity reaches a certain level, it becomes notably significant. With the adoption of the latest Hong Kong Growth Chart 2020 [30] and its suggested definition of obesity (BMI > 98% instead of ≥ 95%), the association between obesity and increased risk of depression is likely to become more evident. Our findings revealed that adolescents with severe obesity (BMI ≥ 99.6%) had the highest risk of major depression compared to both adolescents with obesity (BMI < 99.6%) and normal‐weight peers, after adjusting for demographic factors. The logistic regression analysis (Table 5) identified severe obesity and female gender as significant risk factors for major depression; however, when assessing their combined effects, only severe obesity remained significant without collinearity. Although there is no international consensus on the definition of paediatric severe obesity, in Hong Kong, the 99.6th percentile is used as an extreme threshold to identify markedly abnormal growth [31]. Factors such as age, SES and the total number of obesity‐related complications were not significantly associated with depression risk in our cohort. Our results align with a multi‐centre study from Germany involving children and young adults aged 6–30 years, which found that severe obesity was more likely to be associated with depression [32]. Several other studies have described a U‐shaped relationship between BMI and depression risk, indicating that both underweight and severe obesity are linked to higher incidences of depression and other mental health disorders [33, 34, 35, 36]. Although our study did not specifically examine underweight individuals, our findings supported this U‐shaped association, suggesting that extremes of BMI confer increased vulnerability to depression. The absence of significant difference in our primary outcome may be attributed to the small number of patients with severe obesity, which was only 24 in our sample.

This serves as a pilot study that lays the foundation for a potentially broader, cross‐regional study aimed at assessing the mental health of adolescents with obesity in Hong Kong. PHQ‐9 is a simple, validated questionnaire that consists of only nine questions [24]. In our centre, the use of the PHQ‐9 questionnaire is limited to the subspecialized weight control clinic. However, there are also adolescents with obesity following up in the general paediatrics clinic or community‐based clinics. It is essential for practitioners to recognise the importance of regular depression screening for all adolescents with obesity, especially when their BMI deteriorates or exceeds the threshold of ≥ 99.6%.

While our study did not explore the causal relationship between high BMI and PHQ‐9 scores, previous studies [2, 3] suggested a bi‐directional relationship between these factors, underscoring the importance of effective BMI control and diligent monitoring. This approach can help prevent the development of both medical and mental complications.

5. Limitations

Our study has several limitations. Firstly, the PHQ‐9 questionnaire utilised in this research serves as a screening tool rather than a diagnostic instrument for depression. While PHQ‐9 demonstrates strong sensitivity and specificity in identifying individuals at risk for depression, a formal diagnosis should be confirmed through further clinical assessment.

Secondly, the sample size of adolescents with severe obesity in this study was relatively small; this might limit the generalizability of the findings. A larger sample would provide a more robust understanding of the relationship between obesity and depression.

Thirdly, it is important to note that this study was conducted before the publication of the Hong Kong Growth Study 2020, which introduced updated definitions of obesity. Consequently, the study employed a different definition of obesity that may not align with the latest suggested criteria.

Fourthly, our study design involved mixing clinical‐based patients with community‐based adolescents; this might compromise the homogeneity of data collection. However, severe obesity is not common. From Galler et al., it required more than 100 000 sample size to assess the effect of extreme obesity on mental health. Hence, by including the data from the clinic, it offers the full spectrum of severity of obesity, thus maintaining the power of the comparison.

Fifthly, the more severe obesity data was collected from a single centre. Further study involving other centres with obesity clinics will promote the generalisability.

Lastly, the body measurements in the community and clinic settings were conducted by two different groups of people: research staff and outpatient healthcare providers. This difference in observers could potentially introduce the risk of observer bias. However, it is worth noting that all observers underwent prior training to ensure standardised measurement techniques were followed. This helps minimise the risk of bias associated with observer variability.

To address these limitations, future cross‐cluster research is warranted using a similar study model but with an expanded sample size of adolescents with severe obesity. In addition, future investigations should adopt the newly recommended definition of obesity from the Hong Kong Growth Study 2020 to enhance the validity and applicability of the findings. Collaboration with Clinical Psychology professionals would also be beneficial, as their expertise can contribute to confirming the diagnosis of major depression. By recognising and addressing these limitations, future research endeavours can advance our understanding of the intricate relationship between obesity and mental health, leading to more accurate assessments and contributing to improved clinical practices.

6. Conclusion

Using the BMI ≥ 95th percentile definition of obesity, there is no relationship between obesity and depression risk in adolescents. However, our study demonstrated that in adolescents, severe obesity (BMI ≥ 99.6th percentile) was associated with significantly higher risk of depressive symptoms. Routine depression screening for adolescents with severe obesity should be advocated.

Author Contributions

Geoffrey Chek‐Fei Yu: methodology, data curation, formal analysis, writing – original draft, conceptualization, writing – review and editing. Ka‐Man Yip: data curation, formal analysis, writing – review and editing. Wilfred Hing‐Sang Wong: validation, formal analysis, supervision, data curation, writing – review and editing. Queenie Wing‐Shan See: conceptualization, methodology, validation, supervision, writing – original draft, writing – review and editing, data curation.

Conflicts of Interest

The authors declare no conflicts of interest.