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Article

An Instrumental Analysis of the Triad Association Between Sugar-Sweetened Beverages, Screen Time, and Dental Caries in Adolescents

by
Hui Xue
1,†,
Xin Wang
2,†,
Linyuan Lai
1,
Ying Li
1,
Feng Huang
1,
Xiaoyan Ni
1,
Yong Tian
1,
Meng Li
1,
Lijun Fan
1,
Jie Yang
2,* and
Wei Du
1,*
1
School of Public Health, Southeast University, 87 Dingjiaqiao, Nanjing 210009, China
2
Department of Child and Adolescent Health Promotion, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Future 2024, 2(4), 149-163; https://doi.org/10.3390/future2040012
Submission received: 12 July 2024 / Revised: 27 September 2024 / Accepted: 10 October 2024 / Published: 18 October 2024
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Abstract

:

Highlights

  • What are the main findings?
    • More screen time on mobile devices was associated with worse dental caries experiences in adolescents.
    • Sugar-sweetened beverage intake might be an independent influential factor for dental caries instead of the mediator between screen time on mobile devices and dental caries.
  • What are the implications of the main findings?
    • Our findings highlighted the necessity to alleviate the burden of dental caries in adolescents and called for concerted actions, including the establishment of appropriate intervention strategies with better targeting of excess time on mobile devices to prevent dental caries and promote oral health in adolescents.
    • Implementing recommendations on reducing screen time and promoting healthier dietary patterns, including control of sugar-sweetened beverage consumption, could be beneficial in alleviating the burden of dental caries in adolescents.

Abstract

Background: Previous studies reported screen time in association with unhealthy dietary behaviors, such as excessive intake of sugar-sweetened beverages leading to dental caries. Thus, we aimed to explore the association between screen time on mobile devices and dental caries experience in adolescents, as well as whether sugar-sweetened beverages would mediate the association. Methods: We analyzed 24,374 junior and senior high school students of age 12 to 17 years from the 2021 “Surveillance for common diseases and health risk factors among students” Project in Jiangsu Province of China. Dental caries experience was determined by the designated dentists. Screen time and consumption of sugar-sweetened beverages were self-reported and treated as a priori risk. We employed an instrumental variable (IV) approach for the current cross-sectional setting. We used the lasso technique to shortlist covariates from a range of confounding factors. Binary logistic regression or ordered logistic regression was performed where appropriate to explore the associations of screen time on mobile devices with dental caries and sugar-sweetened beverages. Results: The prevalence rate of dental caries was 38.4% in the study population. In comparison with <2 h/d screen time on mobile devices, extended screen time (≥2 h/d) was associated with higher dental caries risk (coefficient: 1.27, 95%; confidence interval: 0.80–1.75). Mediation analysis suggested that sugar-sweetened beverage intake might be an independent risk factor for dental caries, instead of the mediator between screen time on mobile devices and dental caries. None of the interaction terms under investigation was statistically significant. Conclusions: Exposure to mobile device screens and sugar-sweetened beverages was associated with dental caries in adolescents. These findings highlighted the importance of increasing awareness of potential risks owing to mobile device usage and sugar-sweetened beverages and the necessity to develop appropriate intervention strategies for school-aged adolescents.

1. Introduction

Teenagers are enthusiastic fans of mobile devices, such as smartphones and tablets. In China, for instance, the number of smartphone users accounted for 84.3% of students in junior high schools and 79.7% in senior high schools in 2021 [1], rising from 87.6% in 2014 to 93.1% in 2019 [2,3]. Even higher penetration of mobile devices in adolescents was observed in developed countries, for example, the United States (95%) and the United Kingdom (93–96%) [4]. Recently, the emergence of distance learning has gained popularity in the education sector by promoting the use of mobile devices off-campus in school-aged children and adolescents to connect with their teachers, peers, and the rest of the world, which would perhaps result in an increase in their screen time [5]. In spite of the educational purpose for children and adolescents spending time on mobile devices, there are growing concerns about screen time on mobile devices in association with physical health and dietary behaviors among young people, which, however, remains unclear to date [6,7].
Excessive use of mobile devices may change youth lifestyles in many ways, having adverse impacts on their health, such as deterioration of eyesight, poor mental health, and sleep disorders [7]. Moreover, growing evidence has pointed towards the potential association between screen time and dental caries [6,8], allowing for dental caries to affect 10% of the population around the world and remain a key driver to the global burden of oral diseases [9]. Considering screen time in children and adolescents increased over time in China [2,3], we hypothesized that extended screen time on mobile devices would increase the risk of dental caries in the Chinese setting. From a life course perspective, adverse outcomes in relation to dental caries included malnutrition [10], poor cognitive and social–emotional development [11] in children, and demonstrated a shift of burden towards adulthood with deteriorated oral health, high healthcare costs, and poor quality of life [12], which would require much close attention to early prevention and control of this common infectious disease during childhood and adolescence.
Increased screen time on mobile devices has been reported in association with unhealthy dietary behaviors, for example, higher consumption of potentially cariogenic foods such as sugar-sweetened beverages and sweet foods in children and adolescents [6]. Further studies reported that unhealthy dietary behaviors, such as excessive intake of sugar-sweetened beverages and sweet foods, were critical contributors to dental caries [13]. Should these aforementioned links exist for dietary behaviors to play a mediating role in the association between screen time and dental caries, unhealthy dietary behaviors could be targeted for intervention strategies to reduce dental caries in children and adolescents. However, this potential mediation role has not been explored in the literature. Therefore, we hypothesized that the extended screen time would increase the risk of dental caries through higher consumption of sugar-sweetened beverages.
In this study, we aimed to determine whether screen time on mobile devices might be associated with dental caries using a large-scale provincial-wide sample of adolescents in Jiangsu province of China. We also aimed to explore whether dietary behaviors would mediate this association.

2. Methods

2.1. Study Design and Participants

Data for this study were obtained from the 2021 “Surveillance for Common Diseases and Health Risk Factors among Students” Project, which employed a stratified random sampling frame to select participants in Jiangsu Province and collected data from September to November 2021. From the project population, a total of 25,376 school-aged children from 104 junior and senior high schools were initially sampled in the current study. Strict quality control measures have been implemented throughout this project. For example, to mitigate the risk of reporting bias, clear and concise questionnaires were employed to minimize misinterpretation, and participants were encouraged to provide honest responses with the assurance to maintain the anonymity and confidentiality of their responses. Details of the project were described elsewhere [14], with the reliability, validity, and responsiveness of the survey instruments reported elsewhere [15]. Participants with missing (n = 148) and unclassifiable values (n = 161) in screen time on mobile devices (addition of screen time on mobile devices and sleep time exceeding over 24 h) or aged less than 12 years or greater than and equal to 18 years (n = 693) were sequentially excluded, resulting in the final sample of 24,374 students of age 12 to 17 years for analysis. We used an instrumental variable (IV) approach to estimate causal inference, allowing for potential reverse causality in the current cross-sectional setting. Ethics approval was obtained from the Institutional Ethics Committee for Clinical Research of Zhongda Hospital Affiliated with Southeast University (No. 2023ZDSYLL456-P01), and all participants provided their informed consent for participation.

2.2. Measurements

2.2.1. Dental Caries Experiences

Experience of dental caries was measured using the decayed, missing, and filled teeth (DMFT) index, i.e., the sum score of one’s decayed, missing, and filled permanent teeth, which follows the commonly accepted World Health Organization criteria [16]. Trained dental professionals performed the oral examination and recorded the DMFT score for each study participant. A DMFT index greater than zero was regarded as having dental caries experiences.

2.2.2. Screen Time on Mobile Devices

Screen time on mobile devices was self-reported by asking the participants about the length of time that they spent on mobile devices (including mobile phones, handheld game consoles, laptops, and tablets) in the past week during the survey (Supplementary Questionnaire Materials). According to the Guidelines for Physical Activity in Chinese Children and Adolescents [17], children and adolescents should have no more than 2 h of sedentary screen time per day. Therefore, respondents who used less than 2 h per day were categorized as short-hour users, and those who used at least 2 h per day were categorized as long-hour users.

2.2.3. Consumption of Sugar-Sweetened Beverages

Consumption of sugar-sweetened beverages (SSBs) was evaluated in terms of self-reported intake of sugar-sweetened beverages, including fruit juice, coke, iced tea, and other flavored soft drinks, with two options to choose from, i.e., not every day or every day (Supplementary Questionnaire Materials). Respondents who consumed less than once a day were categorized as low-frequency consumers, whereas the others were categorized as high-frequency consumers.

2.2.4. Instrumental Variable

Given that the number of co-occupants was highly correlated with the use of mobile devices and unlikely to affect dental caries [18], we consider the number of co-occupants as a potential continuous instrumental variable (IV). Co-occupants were defined as family members living together. The number of co-occupants was self-reported by participants.

2.2.5. Model Covariates

According to the previous literature [6,7,8,13], we considered screen time and sugar-sweetened beverages as a priori risk in association with dental caries in children and adolescents. Using the Andersen behavioral model, we classified the other self-reported characteristics, which may indicate the causal links to dental caries, into three broad categories. Predisposing factors included age group as 12–15 years or 16–17 years; sex, as boy or girl; geographic region (south, middle, or north of Jiangsu province); and maternal education attainment as primary school and below, junior and senior high school, and more advanced attainment. Enabling factors included tooth brushing habits twice a day as to whether or not participants brushed their teeth two times per day and parental intervention as to whether or not restricting children from using mobile devices. Health-need factors included obesity according to body-mass-index as yes or no.

2.3. Statistical Analysis

We used Stata 16.0 software (Stata Corp LLC, College Station, TX, USA) to carry out all analyses. A p-value less than 0.05 was set as statistically significant. We reported descriptive statistics in terms of numbers and proportions for categorical variables. We used the chi-square test for categorical variables to compare the differences in variable distribution in the study population between different screen times on mobile devices. We used the lasso technique for variable selection [19]. We then selected the variables identified by the lasso technique as covariates in the models, as detailed in Section 2.2.5. Adjusting for age group, sex, geographic region, and other covariates, we used the binary logistic regression models to evaluate the association between screen time on mobile devices and dental caries in terms of the odds ratio (OR) and 95% confidence interval (CI). Allowing for potential clustering, instead of using the sample weight approach, we employed robust estimation methods where appropriate, including the specification of the vce option, to offer improved stability and efficiency in estimating parameters from complex datasets.
Assuming the causal pathway where screen time on mobile devices would impact sugar-sweetened beverage consumption and the latter in turn would impact dental caries, we examined the role of dietary behaviors to mediate the association between screen time on mobile devices and dental caries in adolescents using the Baron and Kenny approach [20]. First, we used the ordinal logistic regression model to estimate the OR to confirm the association between screen time on mobile devices and sugar-sweetened beverages consumption, and then compared the logistic full model (adjusting for sugar-sweetened beverages consumption) with the reduce model (omitting such adjustment) to further evaluate and calculate the proportionate change in marginal effect sizes of screen time on mobile devices in association with dental caries [21]. In addition, we investigated the potential interaction between screen time and sugar-sweetened beverage consumption, and we also investigated the potential two-way interaction between age, sex, and geography region. We introduced two indices, i.e., the relative excess risk due to the interaction (RERI) and the attributable proportion (AP) due to the interaction, and calculated their 95%CI. Containing zero for either RERI or AP 95%CI would indicate the absence of additive interaction. We also evaluated the multiplicative interaction and further compared the models with and without the interaction term using a likelihood ratio test.
Allowing for reverse causality due in part to the current nature of cross-sectional data, we used the state-of-the-art IV approach to treat endogeneity [22]. Given that the number of co-occupants was highly correlated with the use of mobile devices and unlikely to affect dental caries [18], we used an extended regression model (ERM) having the number of co-occupants as the IV variables to make valid inferences in the current study context, which served in an appropriate way for ordinal endogenous variables [23].
We considered several candidate instrumental variables. To confirm whether the instrumental variable was valid, we conducted analyses to verify that our instrument was a solid exogenous instrumental variable for the exposure (screen time on mobile devices); consistent with the exclusion restriction assumption of instrumental variables (in other words, no confounder significantly associated with an instrument); and having no statistically significant effect on the outcome (dental caries) while remaining independent of the exposure. We then used marginal means to generate absolute risk estimates of dental caries in association with screen time on mobile devices. However, the association of the instrument with age, sex, or geographic region was excluded from consideration, allowing for that they were unlikely to change easily. Moreover, current research reported that dental caries might result from dysbiotic changes in the oral environment, and such an association was mediated by personal dietary behaviors [13]. Therefore, we mainly focused on the association of behaviors with the instrument and considered the potential mediation effects of sugar-sweetened beverage intake.
Sensitivity analyses were performed in an identical modeling manner under the three new categories of the exposure and outcome variable. First, the variable of 2-level screen time on mobile devices was replaced by the 3-level screen time on mobile devices (i.e., <1 h/d, [1,2)/d, and ≥2 h/d) to evaluate the impact of potential misclassification. Second, the outcome variable DMFT index was recalculated with the exclusion of missing and filled teeth. Furthermore, we also repeated the modeling approach for the mediation analysis. The following directed acyclic graph represents the hypothesized causal relationships in the current study setting (Figure 1).

3. Results

Of all 24,374 study participants, more than half of the students (n = 15,007; 61.7%) were caries-free in their permanent teeth, and the rest (n = 9340; 38.3%) had dental caries experiences (i.e., DMFT > 0). The mean age of participants was 15.1 years, with a standard deviation of 1.7 years (Table 1). A total of 11,716 adolescents (48.1%) were girls, and the rest (51.9%) were boys. Except for obesity and sex, the distribution of the other variables varied across different categories of screen time on mobile devices (Table 1).

3.1. Screen Time in Relation to Dental Caries Experiences

To explore the association between screen time on mobile devices and dental caries, we conducted a binary logistic regression analysis. The results indicated that compared with using mobile devices <2 h/d, spending more screen time on mobile devices (≥2 h/d) related to worse dental caries experiences in the study population (unadjusted OR: 1.32, 95%CI: 1.24–1.40, p < 0.001; adjusted OR: 1.23, 95%CI: 1.17–1.33, p < 0.001, Table 2). We also observed a higher risk of experiencing dental caries among 16–17-year-olds compared to the 12–15-year-olds (adjusted OR: 1.27, 95%CI: 1.20–1.35, p < 0.001). In addition, girls exhibited a higher risk compared to boys (adjusted OR: 1.68, 95%CI: 1.59–1.77, p < 0.001), and adolescents from the central region of Jiangsu had a higher risk of dental caries compared to those from the southern region (adjusted OR: 1.38, 95%CI: 1.28–1.47, p < 0.001, Table 2). However, no significant interactions were observed in association with age, sex, and geographic region subgroups (p > 0.05).

3.2. Causal Validation of IV Approach

To address the potential endogeneity of screen time on mobile devices, we utilized the IV approach for estimation. The results indicated that the model fitting errors in screen time on mobile devices and dental caries experiences were statistically significantly correlated (Table 3: the correlation coefficient between the errors in screen time on mobile devices and dental caries experiences is −0.72 in Model 1 and −0.67 in Model 2), indicating the existence of endogenous bias and the need of the IV approach. The ERM coefficients for the number of co-occupants were positive and statistically significant (p < 0.001), suggesting a robust correlation between the instrumental variable and the screen time on mobile devices and hence excluding the possibility of a weak instrumental variable (Table 3). Second, the instrument variable was not significantly associated with dental caries (p > 0.05, Supplementary Table S1). Third, except for sex, age, and geographic region, none of the covariates was statistically associated with the instrumental variable (all p > 0.05, Supplementary Table S2). Moreover, the instrumental variable was not statistically significantly associated with the Pearson residuals in the main equation (p > 0.05, Supplementary Table S1). Therefore, according to the results of all available candidate instrument variables, the number of co-occupants can be considered exogenous and valid for the current causal evaluation.
Using the IV approach, longer screen time on mobile devices maintained its effect on dental caries experiences with a potential dose–response marginal increase in the risk of dental caries from 0.373 (screen time <2 h/d) to 0.421 (≥2 h/d), although not statistically significant (Figure 2). In the absence of the weight approach, the substantial difference in screen time represented the potential source of variability in the study population.

3.3. Relationship Between Screen Time on Mobile Devices, Sugar-Sweetened Beverages, and Dental Caries

To investigate the mediating effect of sugar-sweetened beverage consumption between screen time on mobile devices and dental caries, we conducted the similar modeling approach for mediation analysis. More screen time on mobile devices was found to be associated with increased intake of sugar-sweetened beverages in the logistic regression model (adjusted OR: 1.93, 95%CI: 1.75–2.12, p < 0.001, Supplementary Table S7). However, in the extended model, there was no statistically significant association between screen time on mobile and sugar-sweetened beverages consumption (adjusted β coefficient: 0.63, 95%CI: −0.09–1.33, p > 0.05, Supplementary Table S9).
When adjusting for sugar-sweetened beverages in the logistic regression models, in comparison with no consumption of sugar-sweetened beverages, higher consumption of sugar-sweetened beverages showed an elevated risk of dental caries experiences (Table 4). The association between sugar-sweetened beverages and dental caries followed a similar pattern in the extended regression model (Table 4).
According to the calculation of marginal effect change in screen time on mobile devices, sugar-sweetened beverages explained 0.9% of the dental caries experiences in association with longer screen time on mobile devices (≥2 h/d). After the additional adjustment for the covariates, the consumption of sugar-sweetened beverages remained, explaining 0.7% of the dental caries experiences in association with longer screen time (≥2 h/d). Consistently, consumption of sugar-sweetened beverages played a negligible role in mediating the marginal effect of screen time on dental caries in the extended regression model (explaining 0.7%, Table 4).
To further investigate the potential interaction between screen time and sugar-sweetened beverage consumption, we calculated the RERI and the AP due to interaction and compared the models with and without the interaction term. Our findings indicated that both RERI and AP were not statistically significant, suggesting the absence of an additive interaction. The multiplicative effect was also not significant, and a simplified model was preferred based on the likelihood ratio test (p = 0.1807, Supplementary Table S10).

3.4. Sensitivity Analysis

Supplementary Tables S1–S12 showed the results of the sensitivity analyses. The association between screen time and dental caries was consistently statistically significant (p < 0.05) across different screen time or dental caries classifications. These sensitivity analyses demonstrated the robust effect of mobile device screen time on dental caries. We also estimated the marginal effects of screen time on dental caries in the sensitivity analysis (Supplementary Figures S1–S3), as well as the association among screen time on mobile devices, sugar-sweetened beverages, and dental caries (Supplementary Tables S11–S13). There was little material change, indicating little impact of potential misclassification bias arising from the analysis of self-report data.

4. Discussion

To the best of our knowledge, the present study was the first to explore the association of screen time on mobile devices with dental caries experiences among adolescents in China and was also novel in its investigation into the potential mediating role of sugar-sweetened beverage consumption, thereby providing robust evidence for the development of targeted risk mitigation strategies closely related to the everyday behavioral routines and habits in teenagers. We observed that nearly half of the recruited adolescents (38.4%) aged 12 to 17 years in Jiangsu province of China experienced dental caries, similar to what was reported in the US (27.1%), Europe (34.3%), Asia (32.8%), and Australia (29.2%) [24], highlighting the urgent need to promote children’s oral health around the world to alleviate the burden of caries-related adversities during adolescence and their later adulthood.
Our study demonstrated that more screen time on mobile devices was associated with worse dental caries experiences in adolescents, which was consistent with previous studies addressing prolonged time spent on TV, electronic devices, and other mass media [6,8]. This finding added to the literature the unwanted effects of excess use of mobile devices in children and adolescents in spite of the benefits mobile devices have brought into their everyday lives [5]. For example, with respect to the current focus on dental caries experience, on the one hand, more time spent on mobile devices was associated with reduced salivary flow and poor tooth brushing behaviors [13], both of which could increase the risk of dental caries. On the other hand, the use of mobile devices could help children and adolescents to learn by accessing electronic educational resources to increase their health literacy [25]. Nowadays, children spend more time on mobile devices than reading books and doing exercises [26]. Controversy can arise as to whether we should restrict the use of mobile devices in children and adolescents, which would obviously create plenty of room for dispute in this digital era. Future studies may focus on how benefits relate to harms and quantify person-centered wellness, such as esthetic satisfaction and confidence from caries-free teeth. In the absence of such evidence, parents, teachers, pediatricians, and dentists are expected to make a concerted effort in promoting device-free times as a precautionary principle.
We observed that more screen time on mobile devices was associated with increased consumption of sugar-sweetened beverages in logistic regression models, yet not associated with increased consumption of sugar-sweetened beverages in extended regression models. These findings differ from previous studies [6] that suggested more screen exposure or time was associated with unhealthy dietary behaviors. According to previous investigations on the association of mobile device use with human behavioral change, unobserved conscientious factors in the current study setting, such as impulsiveness in adolescence, distraction on the development of satiety, and intensified urge to consume energy-dense, high-calorie foods [27,28] might result in such behavioral tendency. Unfortunately, we were unable to illustrate these possible neural mechanisms underlying our observations due to a lack of relevant biological data. Considering that such an adaptive neurobiological process seems to be a key driver to performing self-control tasks, which, however, children and adolescents are developing, it would be prudent to avoid blaming children and adolescents for lack of self-control in their excess use of mobile devices. Instead, healthy coping skills in response to external stimulants, for example, behavior change techniques, digital competence promotion, and digital interventions should be encouraged during this crucial phase of physical and cognitive development [5,25].
Unsurprisingly, we found that increased consumption of sweet-sweetened beverages in adolescents contributed towards worse dental caries experience, which was consistent with previous findings [13]. To effectively mitigate the risk of dental caries in children and adolescents in the future, for example, schools and community healthcare facilities are encouraged to work collaboratively promoting targeted education campaigns and skill development sessions in regard to healthy dietary choices among children and parents as well. Activities could include learning the importance of balanced diets, the consequences of excessive sugar intake, and practical ways to reduce the consumption of sugar-sweetened beverages. Such a collaborative task force may also consider interventions to restrict competitive beverage sales targeting children and adolescents.
The underlying behavioral mechanisms in association with dental caries were complex and rarely addressed in the literature, presenting a precious opportunity for in-depth investigation [13]. We observed that the function of sugar-sweetened beverages on dental caries was independent, similar to the impact of screen time on dental caries, instead of playing a mediator role between screen time on mobile devices and dental caries among teenagers. These findings would imply the demand for appropriately designed and implemented interventions towards restricting excess screen time on mobile devices as well as cultivating healthy dietary habits with an emphasis on reduced intake of sugar-sweetened beverages, both of which are inevitable components in the everyday life of adolescents nowadays.
In spite of the major strengths of our study to use the large-scale, population-based, and representative sample of adolescents and the instrumental variable approach with robust causality implications, there were several limitations. First, the nature of cross-sectional design would restrict the power to confirm the cause–effect relationship. Although the instrumental variable approach demonstrated advantages in controlling for unobserved confounding and reverse causality, its effect estimation facility was less powerful than the conventional adjustment modeling approach [29], and therefore, results should be interpreted with care. In addition, the current study selected the IV approach for the purpose of making appropriate causal inferences at the cost of lacking the power to evaluate the association between dental caries and a range of independent risk factors. Although the lasso technique would be advantageous to mitigate the risk of collinearity in conventional modeling procedures, simplify the process of feature selection, and avoid overfitting the model, such efficiency in the result interpretation might not account for the complexity in the real world. Second, our study utilized the number of co-occupants as the instrumental variable to evaluate the association between screen time and dental caries. Ideally, family dynamics, such as interactions among family members, lifestyle habits, and the number of electronic devices in the household, would be considered as another potentially appropriate instrumental variable. However, such information was not collected in the study, and therefore, we were unable to delve deeply into the complexity of intra-family factors and their specific mechanisms affecting the risk of dental caries. Future research may further elucidate the multidimensional impact of family dynamics on the risk of dental caries. Third, the use of additive models in the calculation of marginal effects did not quantify the exact effects of any potential mediators, indicating the existence of possible indirect effects in the mediation analysis [21]. Fourth, we can identify neither the exact distribution of different amounts of screen time in a day spent on mobile devices nor the exact distribution of different dietary intake and determine their contribution towards the risk of dental caries; therefore, further studies are warranted to investigate the underlying mechanisms, perhaps dose–response relationship, to fill the knowledge gap as well as promote appropriate behavioral intervention strategies. Fifth, the present study was carried out in Jiangsu province, a relatively affluent region, limiting the generalization of results of other less-developed settings. Potential biases and variations in data quality across different regions might have an impact on the result interpretation. For example, underreporting of screen time and sugar-sweetened beverages might bias the risk estimates towards the null. To ensure data consistency and reliability, strict quality control measures were implemented, including standardized staff training across the province during the data collection phase, comprehensive logic detection to mitigate the risk of poor-quality data, and meticulous database checks and call-backs for missing and outlier data fields. These measures would potentially enhance the data interpretability and ensure the robustness of the models. In addition to the potential interpretability issues, recall bias might arise from the self-reported nature of analytic data in the current study. Nevertheless, we carried out sensitivity analyses using different classifications and found little material change in the primary analysis. Hence, our results were somewhat robust, highlighting new opportunities for the prevention and control of dental caries in adolescents. Future research should consider the establishment of dental caries prevention and control strategies based on the collaboration between school communities, families, dentists, physicians, and public health practitioners. Continuing efforts are warranted towards implementing multifactorial lifestyle interventions on reducing prolonged screen time and consumption of sugar-sweetened beverages in school-aged children and adolescents.

5. Conclusions

Excess screen time on mobile devices was associated with the risk of dental caries in adolescents. In addition to its independent role in contributing towards the elevated risk of dental caries, consumption of sugar-sweetened beverages played a negligible role in mediating the association between screen time on mobile devices and dental caries. These findings highlighted the necessity to alleviate the burden of dental caries in adolescents and called for concerted actions, including the establishment of appropriate intervention strategies with better targeting of excess time on mobile devices to prevent dental caries and promote oral health in adolescents.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/future2040012/s1, Table S1. Associations of the number of co-occupants with dental caries and pearson residual; Table S2. Associations of the number of co-occupants with covariates; Table S3. Associations between screen time on mobile devices and dental caries; Table S4. Extended regression model to address any potential concerns about endogeneity (with mobile devices categories change to 3-level); Table S5. Extended regression model to address any potential concerns about endogeneity (with dental caries calculation change: exclude missing and filled teeth); Table S6. Extended regression model to address any potential concerns about endogeneity (with both screen time on mobile devices categories and dental caries calculation change); Table S7. Associations between screen time on mobile devices and SSBs intake; Table S8. The association of the number of co-occupants with SSBs intake and pearson residual; Table S9. Associations between screen time on mobile devices and dental caries; Table S10. Joint analysis of dental caries in relation to different combinations of screen time on mobile devices and sugar-sweetened beverages consumption; Table S11. Mediating proportion of sugar-sweetened beverages intake on the association between mobile device screen time and dental caries (with screen time on mobile devices in 3-level); Table S12. Mediating proportion of sugar-sweetened beverages intake on the association between mobile device screen time and dental caries (with dental caries calculation change: exclude missing and filled teeth); Table S13. Mediating proportion of sugar-sweetened beverages intake on the association between mobile device screen time and dental caries (with both screen time on mobile devices categories and dental caries calculation change); Figure S1. Marginal effects of screen time on mobile devices in extended regression model (with screen time on mobile devices in 3-level); Figure S2. Marginal effects of screen time on mobile devices in extended regression model (with dental caries calculation change: exclude missing and filled teeth); Figure S3. Marginal effects of screen time on mobile devices in extended regression model (with both screen time on mobile devices categories and dental caries calculation change); Supplementary Questionnaire Materials. Detailed questionnaire items for variables (screen time on mobile device and sugar-sweetened beverages consumption); Supplementary STROBE Materials. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies.

Author Contributions

H.X., Writing—review and editing, Writing—original draft, Methodology, Formal analysis, Data curation, and Conceptualization; X.W., Writing—review and editing, Writing—original draft, Methodology, Formal analysis, Data curation, and Conceptualization; L.L., Writing—review and editing, Software, Methodology, and Investigation; Y.L., Writing—review and editing, Software, Methodology, and Investigation; F.H., Writing—review and editing, Project administration, Investigation, and Data curation; X.N., Writing—review and editing, Methodology, and Investigation; Y.T., Writing—review and editing, Methodology, and Investigation; M.L., Writing—review and editing, Methodology, and Investigation; L.F.: Writing—review and editing, Methodology, Investigation, and Funding acquisition; J.Y.: Writing—review and editing, Methodology, Formal analysis, and Conceptualization; W.D., Writing—review and editing, Methodology, Formal analysis, Funding acquisition, and Conceptualization. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Social Science Foundation of China (Grant no. 23CGL072), Ministry of Science and Technology (Grant no. G2023141005L), Ministry of Education (Grant no. 1125000172), Jiangsu Provincial Department of Science and Technology, and Jiangsu Social Science Foundation.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee for Clinical Research of Zhongda Hospital Affiliated with Southeast University (No. 2023ZDSYLL456-P01).

Informed Consent Statement

Informed consent was obtained from all participants and/or their guardians involved in the study.

Data Availability Statement

All relevant data are shown within the manuscript, but original datasets cannot be shared because of involving students’ personal privacy.

Conflicts of Interest

The authors have no conflicts of interest to disclose.

Abbreviations

DMFT (Decayed, Missing, Filled Teeth); SSB (sugar-sweetened beverages); IV (instrumental variable); OR (odds ratio); CI (confidence intervals); ERM (extended regression model).

References

  1. Ji, W.; Shen, J.; Yang, B.; Ji, L. Annual Report on the Internet Use of Chinese Minors (2021); Social Sciences Academic Press: Beijing, China, 2021. [Google Scholar]
  2. CNNIC. Research Report on Internet Behavior of Chinese Adolescents in 2014; China Internet Network Information Center: Beijing, China, 2015. [Google Scholar]
  3. CNNIC. Research Report on Internet Behavior of Chinese Adolescents in 2019; China Internet Network Information Center: Beijing, China, 2020. [Google Scholar]
  4. Odgers, C.L.; Jensen, M.R. Annual Research Review: Adolescent mental health in the digital age: Facts, fears, and future directions. J. Child Psychol. Psychiatry 2020, 61, 336–348. [Google Scholar] [CrossRef] [PubMed]
  5. Tortorella, G.L.; Narayanamurthy, G.; Sunder, M.V.; A Cauchick-Miguel, P. Operations Management teaching practices and information technologies adoption in emerging economies during COVID-19 outbreak. Technol. Forecast. Soc. Chang. 2021, 171, 120996. [Google Scholar] [CrossRef] [PubMed]
  6. Pearson, N.; Biddle, S.J. Sedentary behavior and dietary intake in children, adolescents, and adults. A systematic review. Am. J. Prev. Med. 2011, 41, 178–188. [Google Scholar] [CrossRef] [PubMed]
  7. Lissak, G. Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study. Environ. Res. 2018, 164, 149–157. [Google Scholar] [CrossRef] [PubMed]
  8. Asaka, Y.; Sekine, M.; Yamada, M.; Tatsuse, T.; Sano, M. Association of short sleep duration and long media use with caries in school children. Pediatr. Int. 2020, 62, 214–220. [Google Scholar] [CrossRef]
  9. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016, 388, 1545–1602. [Google Scholar] [CrossRef]
  10. Turton, B.; Sullivan, S.; Chher, T.; Hak, S.; Sokal-Gutierrez, K.; Wieringa, F.; Singh, A. Caries Incidence Is Associated with Wasting among Cambodian Children. J. Dent. Res. 2023, 102, 157–163. [Google Scholar] [CrossRef]
  11. Zhao, J.; Yu, Z.; Sun, X.; Wu, S.; Zhang, J.; Zhang, D.; Zhang, Y.; Jiang, F. Association Between Screen Time Trajectory and Early Childhood Development in Children in China. JAMA Pediatr. 2022, 176, 768–775. [Google Scholar] [CrossRef]
  12. Vujicic, M.; Buchmueller, T.; Klein, R. Dental Care Presents the Highest Level of Financial Barriers, Compared to Other Types of Health Care Services. Health Aff. 2016, 35, 2176–2182. [Google Scholar] [CrossRef]
  13. Moussa, D.G.; Ahmad, P.; Mansour, T.A.; Siqueira, W.L. Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era. Front. Cell. Infect. Microbiol. 2022, 12, 887907. [Google Scholar] [CrossRef]
  14. Zhang, X.-Y.; Yang, W.-Y.; Yang, J.; Wang, Y.; Xiang, Y.; Gao, L.-W.; Zhang, F.-Y.; Zhou, Y.-L. Epidemiological characteristics of childhood obesity in eastern China, 2017–2019. World J. Pediatr. 2019, 15, 412–414. [Google Scholar] [CrossRef] [PubMed]
  15. Huang, F.; Wang, Y.; Xue, H.; Zhang, X.; Tian, Y.; Du, W.; Fan, L.; Yang, J. Are Rural–Urban Differences in Bullying and Poly-Bullying Victimization Associated with Internet Addiction or Depressive Symptoms among Adolescents in Jiangsu Province of China. Future 2024, 2, 1–15. [Google Scholar] [CrossRef]
  16. World Health Organization. Oral Health Surveys: Basic Methods, 5th ed.; WHO: Geneva, Switzerland, 2013; Available online: https://www.who.int/publications-detail-redirect/9789241548649 (accessed on 20 June 2024).
  17. Xu, J.; Gao, C. Physical activity guidelines for Chinese children and adolescents: The next essential step. J. Sport Health Sci. 2018, 7, 120–122. [Google Scholar] [CrossRef] [PubMed]
  18. Lu, C.; Shen, T.; Huang, G.; Corpeleijn, E. Environmental correlates of sedentary behaviors and physical activity in Chinese preschool children: A cross-sectional study. J. Sport Health Sci. 2022, 11, 620–629. [Google Scholar] [CrossRef] [PubMed]
  19. Friedman, J.; Hastie, T.; Tibshirani, R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J. Stat. Softw. 2010, 33, 1–22. [Google Scholar] [CrossRef]
  20. Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
  21. Hafeman, D.M. “Proportion explained”: A causal interpretation for standard measures of indirect effect? Am. J. Epidemiol. 2009, 170, 1443–1448. [Google Scholar] [CrossRef]
  22. Fu, R.; Kim, S.J. Inferring causality from observational studies: The role of instrumental variable analysis. Kidney Int. 2021, 99, 1303–1308. [Google Scholar] [CrossRef]
  23. Stata. 2017. Available online: https://www.stata.com/features/overview/extended-regression-models/ (accessed on 20 June 2024).
  24. Campbell, J.A.; Walker, R.J.; Egede, L.E. Associations Between Adverse Childhood Experiences, High-Risk Behaviors, and Morbidity in Adulthood. Am. J. Prev. Med. 2016, 50, 344–352. [Google Scholar] [CrossRef]
  25. Oh, C.; Carducci, B.; Vaivada, T.; A Bhutta, Z. Digital Interventions for Universal Health Promotion in Children and Adolescents: A Systematic Review. Pediatrics 2022, 149 (Suppl. 5), e2021053852H. [Google Scholar] [CrossRef]
  26. Liu, S.; Zou, S.; Zhang, D.; Wang, X.; Wu, X. Problematic Internet use and academic engagement during the COVID-19 lockdown: The indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. J. Affect. Disord. 2022, 309, 9–18. [Google Scholar] [CrossRef] [PubMed]
  27. Reichenberger, J.; Richard, A.; Smyth, J.M.; Fischer, D.; Pollatos, O.; Blechert, J. It’s craving time: Time of day effects on momentary hunger and food craving in daily life. Nutrition 2018, 55–56, 15–20. [Google Scholar] [CrossRef] [PubMed]
  28. Robinson, T.N.; Banda, J.A.; Hale, L.; Lu, A.S.; Fleming-Milici, F.; Calvert, S.L.; Wartella, E. Screen media exposure and obesity in children and adolescents. Pediatrics 2017, 140, S97–S101. [Google Scholar] [CrossRef] [PubMed]
  29. Markovitz, A.A.; Hollingsworth, J.M.; Ayanian, J.Z.; Norton, E.C.; Yan, P.L.; Ryan, A.M. Performance in the Medicare Shared Savings Program After Accounting for Nonrandom Exit: An Instrumental Variable Analysis. Ann. Intern. Med. 2019, 171, 27–36. [Google Scholar] [CrossRef]
Future 02 00012 g001
Figure 1. Directed acyclic graph for the hypothesized causal relationships among the variables of interest. a To explore the association between screen time on mobile devices and dental caries. b To address the potential endogeneity of screen time on mobile devices, we utilized the IV approach for estimation. c To investigate the mediating effect of sugar-sweetened beverages consumption between screen time on mobile devices and dental caries.
Figure 1. Directed acyclic graph for the hypothesized causal relationships among the variables of interest. a To explore the association between screen time on mobile devices and dental caries. b To address the potential endogeneity of screen time on mobile devices, we utilized the IV approach for estimation. c To investigate the mediating effect of sugar-sweetened beverages consumption between screen time on mobile devices and dental caries.
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Figure 2. Marginal effects of screen time on mobile devices in extended regression model. *** p < 0.001.
Figure 2. Marginal effects of screen time on mobile devices in extended regression model. *** p < 0.001.
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Table 1. Participant characteristics according to different categories of screen time on mobile devices.
Table 1. Participant characteristics according to different categories of screen time on mobile devices.
VariablesTotal
n = 24,374		Screen Time on Mobile Devices
(Hours/Day), n (%) a		p-Value b
<2≥2
n = 19,315n = 5059
Covariates:
Age (year)
12~1516,318 (66.9)13,339 (69.1)2979 (58.9)<0.001 ***
16~178056 (33.1)5976 (30.9)2080 (41.1)
Gender
Boy12,658 (51.9)10,037 (52.0)2621 (51.8)0.843
Girl11,716 (48.1)9278 (48.0)2438 (48.2)
Geographic region
South9377 (38.5)7696 (39.8)1681 (33.2)<0.001 ***
Middle5596 (23.0)4411 (22.8)1185 (23.4)
North9401 (38.6)7208 (37.3)2193 (43.3)
Maternal educational level
Primary school and below3220 (13.2)2306 (11.9)914 (18.1)<0.001 ***
Junior and senior high school17,410 (71.4)13,723 (71.0)3687 (72.9)
More advanced attainment3744 (15.4)3286 (17.0)458 (9.1)
Tooth brushing habit
Yes17,944 (73.6)14,626 (75.7)3318 (65.6)<0.001 ***
No6430 (26.4)4689 (24.3)1741 (34.4)
Obesity
Yes15,304 (62.8)7183 (37.2)1887 (37.3)0.884
No9070 (37.2)12,132 (62.8)3172 (62.7)
Parental intervention
Yes15,400 (63.2)13,076 (67.7)2324 (45.9)<0.001 ***
No8974 (36.8)6239 (32.3)2735 (54.1)
Dietary behaviors:
Sugar-sweetened beverage intake
Never5610 (23.0)4886 (25.3)724 (14.3)<0.001 ***
Sometimes16,368 (67.2)12,845 (66.5)3523 (69.6)
Every day2396 (9.8)1584 (8.2)812 (16.1)
Dental outcome:
Dental caries
Yes9340 (38.3)7137 (37.0)2203 (43.5)<0.01 **
No15,034 (61.7)12,178 (63.0)2856 (56.5)
Decayed teeth (DT)
Yes7892 (32.4)5953 (30.8)1939 (38.3)<0.001 ***
No16,445 (67.5)13,334 (69.0)3111 (61.5)
Missing teeth (MT)
Yes574 (2.4)436 (2.3)138 (2.7)<0.05 *
No23,763 (97.5)18,851 (97.6)4912 (97.1)
Filled teeth (FT)
Yes2072 (8.5)1651 (8.5)421 (8.3)0.612
No22,265 (91.3)17,636 (91.3)4629 (91.5)
a Variables were expressed as n (%), whichever was appropriate; b * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2. Associations between screen time on mobile devices and dental caries.
Table 2. Associations between screen time on mobile devices and dental caries.
VariablesModel 1 aModel 2 b
OR (95%CI)OR (95%CI)
Screen time on mobile devices (hour/day)
<21.001.00
≥21.32 (1.24–1.40) ***1.24 (1.17–1.33) ***
Covariates:
Age (year)
12~15 1.00
16~17 1.27 (1.20–1.35) ***
Gender
Boy 1.00
Girl 1.68 (1.59–1.77) ***
Geographic region
South 1.00
Middle 1.38 (1.28–1.47) ***
North 1.05 (0.99–1.12)
Maternal educational level
Primary school and below 1.00
Junior and senior high school 0.87 (0.80–0.94) **
More advanced attainment 0.78 (0.71–0.87) ***
Tooth brushing habit
Yes 1.00
No 0.99 (0.94–1.06)
Obesity
Yes 1.00
No 1.28 (1.22–1.36) ***
Parental intervention
Yes 1.00
No 1.09 (1.03–1.15) **
a Model 1 was unadjusted for covariates; b Model 2 was adjusted for covariates including age, sex, geographic region, maternal educational level, tooth brushing habit, obesity, and parental intervention. Abbreviations: OR, odds ratio; CI, confidence interval. ** p < 0.01, *** p < 0.001.
Table 3. Extended regression model to address any potential concerns about endogeneity.
Table 3. Extended regression model to address any potential concerns about endogeneity.
VariableModel 1 aModel 2 b
Dental CariesScreen Time on Mobile Devices (hour/day)Dental CariesScreen Time on Mobile Devices (hour/day)
β (95%CI)β (95%CI)β (95%CI)β (95%CI)
Screen time on mobile devices (hours/day)
<20 0
≥21.35 (0.97–1.73) *** 1.27 (0.80–1.75) ***
The number of co-occupants 0.03 (0.01–0.04) *** 0.03 (0.01–0.04) ***
Corr (screen time on devices, dental caries)−0.72 ***−0.67 ***
a Model 1 was adjusted for covariates including age, sex, geographic region, maternal educational level, tooth brushing habit, obesity, and parental intervention; b Model 2 was adjusted for covariates including age, sex, geographic region, maternal educational level, tooth brushing habit, obesity, parental intervention, and SSBs. Abbreviations: CI, confidence interval; SSBs, sugar-sweetened beverages. *** p < 0.001.
Table 4. Mediating proportion of sugar-sweetened beverages intake on the association between screen time on mobile devices and dental caries.
Table 4. Mediating proportion of sugar-sweetened beverages intake on the association between screen time on mobile devices and dental caries.
VariablesModel 1 aModel 2 bModel 2 b
Dental CariesMediating Effect cDental CariesMediating Effect cDental CariesMediating Effect c
Marginal Effect (95%CI)(% of Association Explained by Sugar-Sweetened Beverages)Marginal Effect (95%CI)(% of Association Explained by Sugar-Sweetened Beverages)Marginal Effect (95%CI)(% of Association Explained by Sugar-Sweetened Beverages)
Screen time on mobile devices (hours/day) d <2:≤0
≥2:0.9		 <2:≤0
≥2:0.7		 <2:≤0
≥2:0.7
    <20.371 (0.364–0.377) ***0.374 (0.367–0.380) ***0.374 (0.351–0.396) ***
    ≥20.432 (0.419–0.446) ***0.420 (0.406–0.434) ***0.418 (0.215–0.621) ***
SSBs intake
    Not every day0.378 (0.372–0.384) ***0.377 (0.371–0.384) ***0.377 (0.350–0.405) ***
    Every day0.433 (0.413–0.453) ***0.440 (0.420–0.459) ***0.438 (0.413–0.463) ***
a Model 1 was unadjusted for covariates. b Model 2 was adjusted for covariates including age, sex, geographic region, maternal educational level, tooth brushing habit, obesity, and parental intervention. c Model 3 was an extended regression model treated by instrumental variables and adjusted for covariates including age, sex, geographic region, maternal educational level, tooth brushing habit, obesity, and parental intervention. d The proportion of association explained by sweets was computed using marginal effects.*** p < 0.001. Abbreviations: OR, odds ratio; CI, confidence interval.
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Xue, H.; Wang, X.; Lai, L.; Li, Y.; Huang, F.; Ni, X.; Tian, Y.; Li, M.; Fan, L.; Yang, J.; et al. An Instrumental Analysis of the Triad Association Between Sugar-Sweetened Beverages, Screen Time, and Dental Caries in Adolescents. Future 2024, 2, 149-163. https://doi.org/10.3390/future2040012

AMA Style

Xue H, Wang X, Lai L, Li Y, Huang F, Ni X, Tian Y, Li M, Fan L, Yang J, et al. An Instrumental Analysis of the Triad Association Between Sugar-Sweetened Beverages, Screen Time, and Dental Caries in Adolescents. Future. 2024; 2(4):149-163. https://doi.org/10.3390/future2040012

Chicago/Turabian Style

Xue, Hui, Xin Wang, Linyuan Lai, Ying Li, Feng Huang, Xiaoyan Ni, Yong Tian, Meng Li, Lijun Fan, Jie Yang, and et al. 2024. "An Instrumental Analysis of the Triad Association Between Sugar-Sweetened Beverages, Screen Time, and Dental Caries in Adolescents" Future 2, no. 4: 149-163. https://doi.org/10.3390/future2040012

APA Style

Xue, H., Wang, X., Lai, L., Li, Y., Huang, F., Ni, X., Tian, Y., Li, M., Fan, L., Yang, J., & Du, W. (2024). An Instrumental Analysis of the Triad Association Between Sugar-Sweetened Beverages, Screen Time, and Dental Caries in Adolescents. Future, 2(4), 149-163. https://doi.org/10.3390/future2040012

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