Category: 3. Business

NYC Health + Hospitals/Metropolitan Hosts Annual Holiday Toy Giveaway for More than 1,000 Children and Families

The event is part of Metropolitan Hospital’s ongoing efforts to support local families during a time of year when community need is high and the spirit of giving is in focus

Dec 24, 2025

NYC Health + Hospitals/Metropolitan today announced its annual holiday toy giveaway has distributed gifts to over 1,000 children and families this holiday season. The event is part of Metropolitan Hospital’s ongoing efforts to support local families during a time of year when community need is high and the spirit of giving is in focus. This year’s toy drive was made possible through the generous support of MetroPlusHealth, the Metropolitan Hospital Auxiliary, and Municipal Credit Union (MCU). New York City Council Member-elect Elsie Encarnacion (District 8) and New York State Assemblyman Eddie Gibbs (District 68), joined staff and Santa Claus to distribute presents and spread holiday cheer.

The annual toy drive is one of several community initiatives hosted by Metropolitan Hospital over the past few months. Located in East Harlem — where nearly 30 percent of residents live below the poverty line — Metropolitan Hospital operates under the guiding principle that the strongest care is rooted in supporting community. This is especially true during the holiday season, when small acts can make a meaningful difference for children and families in need.

“We view our community as extended family and recognize that community health extends beyond physical and mental health care,” said NYC Health + Hospitals/Metropolitan Chief Executive Officer, Julian S. John, MPA. “It involves embracing and supporting families by going beyond their healthcare needs. The Metropolitan community believes health services coupled with moments of joy and giving work to together to uplift others.”

“This toy drive reminds us that joy is the best medicine,” said NYC Health + Hospitals/Metropolitan Chief Operating Officer, Elsa Cosme, MBA. “While healthcare is at our core, we recognize that moments of joy and connection are essential for the well-being of our community.”

“This time of year, when many families face additional challenges, the kindness and support from our partners bring more than just gifts — they bring joy and hope,” said NYC Health + Hospitals/Metropolitan Hospital Auxiliary President, Betsy Mendez White, MPH. “The Metropolitan Hospital Auxiliary is proud to play a role in making this holiday season a little brighter for our pediatric patients and their families.”

MEDIA CONTACT: Domonique Chaplin, NYC Health + Hospitals/Metropolitan, (212) 423-7782

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About NYC Health + Hospitals/Metropolitan
NYC Health + Hospitals/Metropolitan is a 338-bed facility serving East Harlem and surrounding communities. Known historically as the primary health care provider in El Barrio, Metropolitan delivers high-quality, compassionate care to over 400,000 clinic visits and more than 60,000 emergency room visits annually. The hospital is a designated Sexual Assault Forensic Examination (SAFE) Center of Excellence and a recognized “Leader in LGBTQ+ Healthcare Equality” by the Human Rights Campaign. Since 2021, the hospital has been ranked #1 in New York State for health equity and inclusivity across all categories by the Lown Institute. Metropolitan has earned numerous honors, including the AORN, Beacon, Lantern, and Pathway to Excellence with Distinction awards, and national recognition in U.S. News & World Report’s Best Hospitals list. With a legacy spanning 150 years, Metropolitan maintains the nation’s oldest municipal hospital-medical school affiliation through its longstanding partnership with New York Medical College.

https://www.nychealthandhospitals.org/locations/metropolitan

About NYC Health + Hospitals
NYC Health + Hospitals is the largest municipal health care system in the nation serving more than a million New Yorkers annually in more than 70 patient care locations across the city’s five boroughs. A robust network of outpatient, neighborhood-based primary and specialty care centers anchors care coordination with the system’s trauma centers, nursing homes, post-acute care centers, home care agency, and MetroPlusHealth plan—all supported by 11 essential hospitals. Its diverse workforce of more than 46,000 employees is uniquely focused on empowering New Yorkers, without exception, to live the healthiest life possible. For more information, visit www.nychealthandhospitals.org and stay connected on Facebook, Twitter, Instagram and LinkedIn.

Introduction

Digital therapeutics (DTx) are software-based interventions that use digital devices such as smartphones and computers to prevent, manage, and treat health conditions, including mental health problems in children and adolescents [-]. They improve accessibility, transcending temporal and spatial limitations, and provide personalized treatment experiences [,]. DTx products should be reviewed for their effectiveness and risks based on clinical evidence and approval from regulatory authorities (eg, the US Food and Drug Administration) []. Several DTx designed for children and adolescents use game-based elements to enhance engagement and therapeutic effectiveness [,].

Gamification, incorporating elements such as achievable challenges, immediate rewards, and personalization, shows promise in enhancing user engagement in digital mental health interventions []. A review of 7 products tailored for young individuals with mental health challenges (ie, EndeavorRx [Akili Interactive Labs, Inc], ATENTIVmynd [BrainFutures], RECOGNeyes [University of Nottingham], REThink [Babeș-Bolyai University, PsyTechs Research and Innovation Center], Mightier [Boston Children’s Hospital and Harvard Medical School Teaching Hospital], MindLight [PlayNice LLC], and SPARX [University of Auckland]) [] found that despite using gamification strategies as interventions that could lead to overdependence, only 3 products (EndeavorRx, ATENTIVmynd, and SPARX) reported specific side effects in their clinical trials. Commonly reported side effects included headache, eye strain, and emotional reactions [-]. These side effects may negatively impact children and adolescents, who are in the most active growth phase of their lives. Although DTx are noninvasive and their side effects are relatively minor, they are still medical interventions applied to vulnerable populations. Thus, it is concerning that only 3 of 7 products explicitly addressed their side effects. A systematic literature review on DTx children with attention-deficit/hyperactivity disorder also found that most studies did not report safety outcomes, suggesting the need to identify its potential side effects and adverse effects []. In South Korea, during this research, DTx targeting children and adolescents were in the developmental phase, and no approved products were available.

Additionally, excessive screen time in children and adolescents, including the use of digital devices such as smartphones, televisions, and computers, may negatively affect their cognitive and socioemotional development [,]. The average daily screen time among children and adolescents aged 6 to 14 years is 2.77 hours [], exceeding the AAP’s (American Academy of Pediatrics) recommended limit. Notably, this issue is of increasing importance given the rise in screen time among children and adolescents since the COVID-19 pandemic []. Given its association with mental health issues—such as obesity, sleep disturbances, depression, and anxiety—and its negative impact on parent-child interactions [,,], to ensure the safe use of DTx for mental health in children and adolescents, it is essential to establish guidelines that prevent overdependence [].

Although DTx may cause side effects ranging from mild to severe, and children and adolescents are particularly vulnerable when using digital devices, no specialized guidelines exist to address DTx overdependence. Government and organizational publications concerning media use in children and adolescents highlight safe practices for general digital engagement. However, given that DTx are used as therapeutic interventions, they necessitate a separate set of preventive measures to account for potential risks beyond those captured by conventional media-use guidelines. Thus, this study sought to define the requirements for such strategies and to evaluate their effectiveness, necessity, reliability, and satisfaction.

Methods

Study Design

This study was conducted in 2 phases: guideline development (phase I) and guideline evaluation (phase II). During phase I, participants responded to a basic survey designed to develop the guidelines to prevent overdependence on DTx. They also completed a survey on smartphone usage and a questionnaire on mental health. Randomization was performed after phase I using a random number table, assigning participants to either the experimental or control group in a 1:1 ratio. In phase II, participants were asked to complete an online survey that included detailed descriptions of DTx and guidelines for each assigned group. They also subsequently undertook an OX quiz (O=yes, X=no) to assess whether they had read the materials appropriately ().

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Participants

A total of 87 participants were recruited between June and October 2023 at 2 tertiary hospitals, Severance Children’s Hospital and Gangnam Severance Hospital, in Seoul, South Korea. The participants included children and adolescents along with their caregivers, who visited outpatient clinics for mental health problems. Recruitment was conducted based on the recommendations of the doctors and after obtaining informed consent. Children and adolescents aged between 9 and 16 years, able to use their own smartphones, and who had permission from their caregivers were eligible to participate in this study. Individuals unable to complete the surveys independently or with mental health conditions that could interfere with study participation were excluded.

Measurements

In phase I, a basic survey (phase I survey) was conducted with both caregivers and children and adolescents to inform the development of guidelines to prevent overdependence on DTx. Caregivers responded to a total of 16 items regarding DTx and how to manage their child’s smartphone usage. The survey used a combination of descriptive responses, multiple choice responses, and visual analog scales (VAS; range 0 to 10, 0=not at all, 10=extremely).

The following questions were answered descriptively: efforts to prevent the child from smartphone addiction (eg, “If there are any efforts you are currently making to prevent your child’s smartphone addiction, please describe them.”), acceptable daily DTx usage time (eg, “If your child is prescribed DTx, how many minutes/hours per day do you think is an appropriate amount of time to use it?”), features that may contribute to DTx overdependence (eg, “When your child uses DTx, what features do you think might lead to overdependence?”), features that may help prevent DTx overdependence (eg, “If you have any suggestions for ways to help prevent your child from becoming overly dependent on DTx, please describe them.”), and opinions on guideline-based DTx (eg, “If your child were to use DTx given guidelines to prevent overdependence on DTx, what would be the most looking forward to (potential advantages) and the most concerning (potential disadvantages)?”, “If there are concerns, what features would you like to see to support them (expectation of guidelines)?”). The question regarding the acceptable duration for DTx intervention was answered using a 4-option multiple-choice (eg, “What would be an acceptable duration for DTx intervention?”). The following questions were answered using VAS: features that may contribute to DTx overdependence (eg, communication, educational videos, and gamification), features that may help prevent DTx overdependence (eg, blocking mobile applications or game-based applications, and shutdown), and anticipated effectiveness of guideline-based DTx (eg, “How helpful do you think DTx given guidelines will be in managing your child’s symptoms?”).

Children and adolescents responded to a total of 3 items using descriptive responses and a VAS (range 0 to 10, where 0=not at all, and 10=extremely). Opinions on guideline-based DTx were collected through descriptive responses (eg, “If you use DTx given guidelines to prevent overdependence on DTx, what would be the most looking forward to (potential advantages) and the most concerning (potential disadvantages)?”). Perceived effectiveness of guideline-based DTx was assessed using the VAS (eg, “How helpful do you think DTx given guidelines will be in managing your symptoms?”).

The questionnaires on mental health were also conducted to assess participants’ baseline mental health status in phase I. Caregivers completed 3 questionnaires (ie, Smartphone Addiction Scale [SAS], Internet Gaming Use-Elicited Symptom Screen [IGUESS], and Children’s Behavior Checklist for Ages 6-18), while children and adolescents completed 8 questionnaires (ie, SAS, IGUESS, Patient health Questionnaire 9-Items [PHQ-9], Generalized Anxiety Disorder 7-Items [GAD-7], Perceived Stress Scale, Brief Fear of Negative Evaluation Scale [BFNE], Difficulties in Emotion Regulation Scale – Short Form, and Family Communication Scale [FCS]).

SAS is a screening tool derived from the Korea Youth Risk Behavior Web-Based Surveys self-reporting scale, consisting of 10 items with a total of 40 points. Responses were measured on a 4-point Likert scale, with scores determining addiction levels []. IGUESS is a 9-item survey to screen the risk of internet gaming disorder, in the fifth edition of the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]). A score of 10 or above is indicative of a positive diagnosis of internet gaming disorder []. Children’s Behavior Checklist for Ages 6-18 is a component of the Achenbach System of Empirically Based Assessment. It is administered by caregivers and is used to identify behavioral and emotional issues in children and adolescents aged 6 to 18 years [].

PHQ-9 is a brief instrument for assessing the severity of depression, completed by patients. PHQ-9 consists of 9 items, each rated on a 4-point Likert scale []. GAD-7 is a preliminary screening instrument comprising 7 items, designed to identify the presence of anxiety disorders. GAD-7 uses a 4-point Likert scale for rating []. The Perceived Stress Scale is a classic instrument for assessing perceived stress, containing 10 items. It is rated on a 5-point Likert scale []. BFNE is a widely used instrument aimed at assessing an individual’s tolerance for the possibility that they might be judged disparagingly or hostilely by others. BFNE consists of 12 items, and respondents provide their answers using a 5-point Likert scale []. Difficulties in Emotion Regulation Scale – Short Form is an 18-item self-report questionnaire, measuring difficulties in emotion regulation. Each of these items is evaluated using a 5-point Likert scale []. FCS is a component included in the Family Adaptability and Cohesion Evaluation Scale IV. FCS comprises 10 items designed to measure the degree of positive communication among family members. Each of these items is evaluated using a 5-point Likert scale [].

In phase II, guidelines were evaluated using a VAS (range 0 to 10, 0=not at all, 10=extremely), covering 4 aspects: effectiveness, necessity, reliability, and satisfaction (Table S1 in ).

Interventions

The intervention of this study was guidelines to prevent overdependence on DTx (guideline A), developed by our research team based on the findings of phase I, reflecting the needs of potential users of DTx for children and adolescents. These guidelines were also organized according to research that explored the potential challenges of digital interventions for children and adolescents, such as ethical issues, safety from side effects and privacy, and interpersonal relationships of family or caregivers [,]. Additionally, development was informed by publications related to media use and the Family Media Plan issued by the AAP.

The primary focus of this development was to enable users to create practical action plans using the guidelines. Guideline A comprised four sections: (1) check, (2) plan, (3) action, and (4) smart (Note S1 in ).

The check section starts with the assessment of the digital dependency of children and adolescents through SAS and IGUESS. It also includes information on the negative symptoms that may arise from DTx overuse. This section provides users with information on identifying potential side effects and precautions, addressing concerns raised in the phase I survey.

The plan section helps users develop personalized strategies for DTx, allowing them to specify the tools or devices needed for treatment and their usage (eg, purpose, duration, and frequency). This section reflects the need for individualized guidelines based on differences in diagnosis and prescription, as identified in the participants’ requirements in the phase I survey. Plans differ depending on the child’s age, with children ≤12 developing plans with caregivers, and adolescents aged 13 years and older developing their own plans.

The action section includes specific behavioral actions to perform in real life to prevent overdependence on DTx. It addresses concerns such as increased exposure to digital devices and conflicts with family members due to DTx usage. Caregivers and children, and adolescents can discuss when and where they use DTx (eg, storage location and usage time) and plan activities to balance their online and offline lives (eg, daily physical activities and DTx free time). This content refers to the recommendations for physical activity among children and adolescents from the World Health Organization and AAP.

Lastly, the smart section describes the use of functions or mobile applications that can help prevent overdependence on DTx. This section also includes regular contact with professionals, reflecting the participants’ need to receive ongoing and timely specialist feedback, as identified in the phase I survey. Guideline A was reviewed by a panel of professional experts, including 2 pediatric psychiatrists (EK and JL) and digital health care professionals (MK and JS).

In phase II, guideline A was provided to the experimental group, while the control group received a reorganized version of general smartphone usage guidelines (guideline B) published by government institutions in South Korea, serving as treatment as usual (Note S1 in ) [,]. Each guideline was tailored to different user groups: caregivers, children (aged 6-12 years), and adolescents (aged 13-18 years).

Statistical Analysis

The sample size was determined to detect a clinically meaningful difference in mean outcomes between the experimental group, which received a tailored guideline for DTx (guideline A), and the control group, which received a conventional smartphone use guideline (guideline B). Using G*Power (Heinrich-Heine-Universität Düsseldorf) software, a total sample size of 52 participants was calculated based on an effect size of 0.8, a 2-tailed significance level of 5%, and a statistical power of 80%. To account for an anticipated dropout rate of 10%-15%, the required minimum sample size was adjusted to at least 60 participants. A total of 87 and 50 participants (owing to missed follow-ups after phase I) were included in the phase I and phase II analyses, respectively.

All statistical analyses were conducted using Jupyter Notebook (version 6.5.4). Descriptive statistics were used to summarize questionnaire responses, survey data, and other quantitative outcomes. Differences in baseline characteristics were analyzed using independent-samples t tests for continuous variables and chi-square tests for categorical variables. Mann-Whitney U tests were used to analyze nonparametric outcome variables in phase II. Thematic analysis was used to analyze the qualitative data from the phase I survey and the evaluation of guidelines in phase II. The participants’ responses to the questionnaire were used as received, without any modifications.

Ethical Considerations

This study was approved by the institutional review board (IRB) at Severance Children’s Hospital (IRB No. 4-2023-0366) and Gangnam Severance Hospital (IRB No. 3-2023-0129) and registered with the Clinical Research Information Service (KCT0008893). Written informed consent was obtained from all participants. To accommodate age-related differences in comprehension, consent materials were developed in 3 versions: for caregivers, for children (aged 9-13 years), and for adolescents (aged 13-16 years). All data collected were fully deidentified and securely stored to maintain participant privacy and confidentiality. Participants were compensated with a gift card after completing all surveys (Korean won 30,000 [US $30] for each phase).

Results

Overview

The flow of participants from recruitment to analysis is shown in . There were no substantial differences in the baseline characteristics between the groups. shows the detailed baseline characteristics of each caregiver and children and adolescents. In total, 24 caregivers (age: mean 46.4, SD 5.5 years) and 26 children and adolescents (age: mean 12.3, SD 1.9 years) completed phase II. Figure S1 in presents the results of smartphone usage among children and adolescents.

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Basic Survey for the Development of Guideline A (Phase I Survey)

Using the VAS, caregivers assessed features that may contribute to or may help prevent overdependence on DTx. According to , gamification showed the highest score (mean 6.5, SD 2.6) as potential overdependence inducers in the DTx, followed by communication (mean 6.4, SD 2.6) and educational videos (mean 4.1, SD 2.6). The most effective features for overdependence prevention were blocking mobile applications or notifications (mean 8.5, SD 1.8), parental monitoring (mean 8.5, SD 2.1), and shutdown (mean 8.2, SD 1.7). Blocking mobile applications or notifications is a function to prevent access to other applications or notifications while using DTx. Parental monitoring refers to a function that allows caregivers to monitor their children’s use of DTx; shutdown refers to a function that automatically switches the device off after a set DTx treatment time.

presents the anticipated effects of the guideline (mean 6.3, SD 1.6) among caregivers and children and adolescents (mean 6.4, SD 1.8). Caregivers considered 38.6 (SD 23.2) minutes as an acceptable daily average usage time for DTx. The most acceptable duration for the DTx intervention period was 12 weeks (16 individuals, 38.1%), followed by 4 weeks (11 individuals, 26.2%), 8 weeks (9 individuals, 21.4%), and 16 weeks (6 individuals, 14.3%).

Participants also provided descriptive responses regarding potential advantages, disadvantages, and expectations of guidelines to prevent DTx overdependence (see Table S3 in for details). Caregivers mentioned potential advantages such as preventing side effects and overdependence, enhancing children’s self-regulation, reducing caregivers’ anxiety, and improving motivation for treatment. Potential disadvantages mentioned were side effects and overdependence, increased exposure to digital devices, and decreased effectiveness of treatments due to nonindividualized guidelines. Additionally, caregivers wanted the guidelines to include parental monitoring, blocking other applications or notifications while using DTx, ongoing feedback from professionals, integration with nondigital treatments, and activities such as stretching or mental relief time.

Children and adolescents also mentioned potential advantages such as the prevention of side effects and overdependence, improved self-regulation, and increased reliance on DTx. However, potential disadvantages included side effects and overdependence, treatment failure due to nonindividualized or strict guidelines, and conflicts with family members in children and adolescents’ responses.

Primary Outcome: VAS Scores for Guideline Evaluation

The primary outcomes were VAS scores for guideline evaluation, focusing on effectiveness, necessity, reliability, and satisfaction. In the experimental group, 25 of the 43 (58.1%) participants completed the primary outcome measures, while 19 (44.2%) individuals did not complete this study. Similarly, in the control group, 26 of 44 (59.1%) participants completed these measures, while 18 (41%) participants did not.

According to , the overall VAS scores were generally higher in the experimental group, except for necessity among caregivers, which was higher in the control group (mean 8.7, SD 1.2; mean 8.5, SD 1.3). For caregivers in the experimental group, the highest scores were for necessity, followed by similar scores for reliability and satisfaction (mean 7.7, SD 1.2 and mean 7.7, SD 1.1, respectively), and effectiveness (mean 7.6, SD 1.0). Caregivers in the control group also attributed the highest score to necessity (mean 8.7, SD 1.2), followed by satisfaction (mean 7.1, SD 1.4), effectiveness (mean 7.0, SD 1.6), and reliability (mean 6.9, SD 1.8). The largest difference was in the reliability scores, which showed a mean difference of 0.8 between the caregiver groups.

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In the experimental group, satisfaction scored the highest (mean 8.2, SD 1.3), followed by effectiveness (mean 8.1, SD 1.6), necessity (mean 8.0, SD 2.1), and reliability (mean 7.7, SD 2.1). In the control group, children and adolescents rated necessity and satisfaction at 7.2 (SD 1.8) and 7.2 (SD 2.0), respectively, followed by effectiveness (mean 7.1, SD 2.1) and reliability (mean 6.9, SD 2.4). The largest differences among children and adolescents were noted in the effectiveness and satisfaction scores, with each showing a difference of 1.

Secondary Outcome: The Qualitative Data Analysis

In phase II, qualitative data were organized into four primary themes: (1) satisfaction, (2) effectiveness, (3) necessity, and (4) knowledge. Verbatim examples of each theme are presented in .

Feedback on guideline A was more favorable compared to guideline B. Participants in the experimental group evaluated the guidelines more positively, using phrases such as “more detailed than I expected,” indicating a high level of satisfaction. Conversely, participants in the control group expressed a need for more detailed guidelines, with comments such as “would be good to have more detailed guidelines,” reflecting their relative dissatisfaction. Moreover, caregivers and children, and adolescents in the experimental group reported being satisfied with the contents of guideline A, such as planning with the caregiver, monitoring treatment progress, and recommendations for physical activities.

Responses regarding the effectiveness of the guidelines varied between the groups. A caregiver in the control group criticized the guidelines for being too generalized, stating that they “might feel like a general overview.” Contrastingly, a caregiver in the experimental group reported reduced anxiety related to the use of DTx, noting that “I can allow myself to be less concerned about DTx,” suggesting a positive perception of guideline A’s effectiveness.

The perception of necessity also differed markedly between the groups. A caregiver from the control group questioned the applicability of guideline B to DTx, commenting that it seemed more suited for general smartphone use than for specifically addressing DTx. Meanwhile, a caregiver in the experimental group emphasized that “the therapeutic objectives could be compromised without the provided guidelines,” indicating a strong perceived necessity for guideline A.

The knowledge gained from each guideline reveals a distinct focus. Participants in the experimental group highlighted learning about specific overdependence prevention contents, such as designating locations and understanding overdependence risks associated with DTx. By contrast, the control group focused more on general smartphone issues, such as eye problems and smartphone usage.

These themes illustrated the different levels of satisfaction, perceived effectiveness, necessity, and knowledge acquisition between the experimental and control groups, highlighting the impact of guideline specificity and relevance on user reception and learning outcomes.

Discussion

Principal Findings

This randomized trial comprised 2 phases. Phase I investigated the needs of potential users regarding guidelines to prevent overdependence on DTx among children and adolescents, to inform its development. In phase II, we evaluated the developed guidelines. As DTx administration differs fundamentally from general smartphone usage, the guidelines developed in this study are more viable than existing smartphone addiction guidelines. The co-design process used to develop these guidelines with actual users contributes to their acceptability among a range of potential users.

Phase I results showed that both caregivers and children and adolescents were concerned about side effects and overdependence on DTx, indicating the need for prevention guidelines for the children and adolescents population to ensure ethical use. Gamification was identified as the most addictive component among various DTx intervention elements for children and adolescents. Additionally, gamification may lead to privacy infringements and social overload []. Around 70% of DTx for children and adolescents (7 of 10 DTx products) in the United States and over 80% of them (5 of 6 DTx products) in South Korea use gamification as their core intervention component []. However, these products did not clearly address caregivers’ concerns regarding potential side effects and overdependence on DTx. Nonetheless, gamification is an effective strategy to encourage participation and enhance the effectiveness of digital intervention [,]. Given the ambivalence surrounding gamification, it is necessary to establish guidelines to prevent overdependence on DTx in children and adolescents, achieving a balance between their benefits and risks.

Furthermore, the phase I results indicated that DTx users would prefer developers to include overdependence prevention features, such as blocking other applications or notifications while using DTx, parental monitoring tools, and shutting down the application. Currently, some applications can restrict access to other applications using blockers such as Digital Wellbeing (Google LLC), Google Family Link (Google LLC), and AirDroid Parental Control for Androids (Sand Studio) [], as well as features on Content and Privacy Restrictions (Apple Inc) for iPhones []. Although each operating system offers some third-party applications or features, there are currently no cases where DTx for children and adolescents has been tested to incorporate these features or functions into their intervention protocols. Our findings may help DTx developers include these features or functions in their systems to better reflect users’ concerns about DTx overdependence.

Along with overdependence prevention features for DTx developers, DTx users demonstrated a need for individualized and actionable guidelines to prevent DTx overdependence for children and adolescents, which can be implemented collaboratively by the family. To the best of our knowledge, however, there are no customized or practical guidelines to prevent overdependence on DTx. In order to address these needs and issues, we developed a guideline, which is called guideline A in this study, that includes individualized and actionable components to prevent DTx overdependence in children and adolescents. The guideline begins by assessing the individual level of digital dependency of the children and adolescents and the potential adverse effects of DTx in the check section. This establishes the rationale for developing individual strategies and behavioral actions in the following sections. The plan and action sections lead the users in adapting the guidelines to their individual contexts, including reviewing their prescribed DTx, appropriately storing devices when not to be used or during sleep, and maintaining a balance with offline activities. Finally, the smart section connects children and adolescents and their caregivers with health care professionals, ensuring the guideline not only engages family members but also invites health care professionals to continue monitoring the patient’s condition progression.

After the guideline development, we compared guideline A with an existing general-use guideline (guideline B). The effectiveness, reliability, and satisfaction of guideline A were higher among caregivers and children and adolescents than those of guideline B. This aligns with the results from the qualitative data. Guideline A showed more promising results than guideline B across all themes, including satisfaction, effectiveness, necessity, and knowledge. These findings are consistent with previous research showing that interventions targeting screen time reduction in children had statistically significant effects. A meta-analysis demonstrated that such interventions—often incorporating knowledge dissemination and increased physical activity—were effective in reducing screen time []. These components are also reflected in our guidelines, particularly in the plan and action sections, which emphasize education and promotion of offline activities. Given the consistent advantages demonstrated across both quantitative and qualitative measures, guideline A appears suitable for distribution in the DTx market and for integration into DTx protocols for children and adolescents.

Despite its strengths, this study has some limitations. First, the sample did not include individuals who had actually used DTx, given that there are no publicly available DTx for children and adolescents []. Only a few DTx for children and adolescents are available for specific populations in clinical trials as part of the regulatory process [,-]. Since children and adolescents are the potential future users of DTx, we described a fictitious DTx when testing the validity of the developed guidelines. Second, although participants were recruited from multiple tertiary hospitals, the selection process involved physician referrals, and this study was conducted in a hospital-based setting. This purposive selection of study sites and participants may have introduced selection bias. In interpreting these findings, it is important to consider South Korea’s unique cultural and technological context. The country has one of the highest smartphone penetration rates among adolescents [], and strong parental involvement in children’s digital usage is common []. Unlike Western countries that emphasize autonomy and privacy, South Korea tends to adopt a more centralized approach, with government and public institutions offering structured guidance on youth digital health practices [,-]. These contextual factors may influence the acceptance and expectations of such interventions. Future studies should therefore test the effectiveness, necessity, reliability, and satisfaction of this guideline with actual DTx users for children and adolescents. Furthermore, future research should examine how these guidelines affect the development process of DTx developers and the therapeutic process of health care professionals. Future studies should also explore whether these guidelines are useful in developing reliable DTx for children and adolescents or beneficial in improving health outcomes for those who use DTx for children and adolescents.

Conclusions

This is the first study to represent the development and testing of the guideline to prevent overdependence on DTx in children and adolescents. The results of this study provide insights into the concerns about DTx overdependence for children and adolescents, which inform the need for preventative guidelines regarding this issue. Additionally, this study provides evidence that the guideline which we developed for preventing DTx overdependence for children and adolescents may be acceptable to be used in therapeutic protocols in the real world. The impact of this guideline will not only be on the DTx users, but across the diverse health care settings and systems where DTx are used.

Implications and Contributions

This study develops and evaluates guidelines to prevent overdependence on DTx in children and adolescents. The findings indicate that personalized guidelines may mitigate overuse concerns, and these guidelines are likely to be applicable in clinical settings, providing practical strategies for health care providers and caregivers.

The holidays are often called the most wonderful time of the year, but for many, they can also be the most stressful. From financial strain to feelings of loneliness and grief, mental health struggles often spike this time of year.

“Holidays are certainly festive, joyous, happy, but can bring up a lot of other emotions,” said Dr. Kelly Causey, deputy commissioner with Colorado’s Behavioral Health Administration. “There can be a lot of stress this time of year. There’s financial stress, maybe some family conflicts. You might be sad and grieving someone who isn’t with us this year for the holidays.”

Causey says those feelings are normal, even when the world seems full of cheer. 

“What’s misunderstood is that everybody feels lots of different emotions in the holidays, that it’s okay, that it isn’t perfect, that it’s okay to feel sad, even when other people are happy,” she said.

To help, Colorado has built a safety net of resources, including the state’s 988 Mental Health Lifeline. The free, confidential service is available 24 hours a day, seven days a week. Anyone can call or text 988 or use the live chat function.

“It is not going to be closed for the holidays,” Causey said. “If you’re struggling, if you’re overwhelmed, 988 is there for you, 24 hours a day, seven days a week. Doesn’t matter what holiday it is. If it’s just a typical Monday, it doesn’t matter.”

Last year, Coloradans turned to 988 for support with over 94,000 calls, plus thousands of texts and chats. That’s a 15% increase from the year before. 

And when you reach out, Causey says you’ll connect with a real person. 

“You will have somebody that answers that call and that chat and that text, and they can do it in lots of different languages,” she explained.

Also, 988 isn’t just for those in crisis. 

“We can also call 988 and ask for help and see what we could do to help our friend or a family member who’s feeling overwhelmed, too,” Causey said.

During a season built around togetherness, Colorado wants to make sure everyone knows — you’re not alone:

• 988 Colorado Mental Health Line: Call or text 988, or use live chat. Free, confidential, available 24/7 in English, Spanish and 240+ languages.
• Colorado LIFTS: Connects individuals and families to mental health, substance use and crisis support statewide.
• Health First Colorado: Medicaid members qualify for behavioral health services at no cost. Call 303-839-2120 or 888-367-6557.
• I Matter Colorado: Free therapy for youth 18 and under (21 if in special education). Services in English and Spanish.
• Lift The Label: Campaign to reduce stigma around addiction and connect people to treatment; available in English and Spanish.
• CRAFT: Free support for families helping loved ones with addiction.
• Tough as a Mother: Connects pregnant and parenting people to treatment and recovery support.

For some, the holiday season means packing their bags to spend time with loved ones, some of whom are half a world away.

The St. John’s International Airport, located in the city’s east end, was busy on Christmas Eve as people were arriving at their destination and catching flights.

Curtis Collier, who landed at the airport in the morning, said he’s looking forward to his mother’s home cooked meals and having turkey dinner on Christmas Day.

He added his mother’s family has Polish-Ukrainian roots so she makes perogies and cabbage rolls on Christmas Eve.

“It feels great to be home, I haven’t been here since October so I’m very much looking forward to seeing my family and friends,” Collier told CBC News.

Marion and Dave Osborne were at the airport to catch a flight to Halifax so they could join their daughter and grandchildren for the holidays.

She said the grandchildren are “very, very excited” to see them, with Dave Osborne adding when they visited in October “the first words they asked — ‘You’re coming back for Christmas?’ So that would give you an indication.”

She said it is a relief that the weather was clear too, so there would be no travel delays.

“I was so upset, thinking I wasn’t going to get there. It would have ruined our Christmas.”

Rome for the holidays

Martin O’Driscoll was at the airport to begin the first leg of his journey that will bring him to Rome, where he will be visiting his brother’s family.

He’s looking forward to seeing his young niece and nephew, adding he saw them in Paris last year.

“They’re so happy to see me. I’m so happy to see them. It brings joy to my life,” said O’Driscoll.

It was also “fantastic” to start the first leg of his long journey on time with no delays, he said.

“As you know about Newfoundland weather, it’s unpredictable. So I’ve been the victim of several cancellations and postponements,” said O’Driscoll.

“I’m very grateful to be getting out today.”

He expects to arrive in Rome at 9 a.m. on Christmas morning.

O’Driscoll said people travelling on the holidays tend to be in a “jolly mood” and while airport staff are still working they exude a certain joy.

Gabriella Walsh and Benjamen Bragg, along with their small dog Winnie, started their day in Halifax before landing in St. John’s.

Walsh said her family lives on Newfoundland’s southern shore, so they’re headed to Kingman’s Cove for the holidays.

“We’re really excited to go do that. And we always have a big Boxing Day party, so I’ll get to see all my family,” she said.

Bragg said he’s also looking forward to spending time with friends and family.

Walsh said they “lucked out for sure” when it came to travel weather.

“We were really worried. We’re always really worried, travelling around this time of year, you never know with delays,” she said.

Bragg added he gives a “sigh of relief” when they can sit down in their plane seats.

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The Yukon’s energy provider says Whitehorse is not at imminent risk of blackouts, but with very cold weather forecasted through the holidays, residents are still being asked to conserve power.

“We know people need to eat their Christmas dinner, that’s not going to be deferred,” said ATCO Electric vice-president Jay Massie. “But the dishwasher, the laundry, electrical heat, if you have baseboards in multiple rooms, some can be turned down while you’re cooking.”

On Wednesday, temperatures sank below -40 C in most Yukon communities, including Whitehorse, and fell to -50 C in Ross River and Faro, according to Environment Canada. The cold temperatures started on Dec. 9 — the result of a cold front from Siberia — and plunged most of the territory into a much colder-than-average December.

The Yukon’s energy minister warned on Tuesday that the territory’s energy grid was experiencing peak demand, and that Whitehorse could be subject to rolling blackouts as a last resort if maximum capacity is exceeded. 

Rolling blackouts are a measure to mitigate widespread power outages when demand exceeds supply. Specific neighbourhoods would temporarily lose power to give the whole system a break.

ATCO Electric is the territory’s power distributor and Yukon Energy is the energy utility.

Stephanie Cunha with Yukon Energy said on Wednesday that Yukoners are using 80 to 90 per cent of the territory’s available energy supply.

She said industrial customers and mine remediation sites have already been asked to disconnect from the grid and switch to diesel generators. Communities have also been switched over to diesel. 

But Massie with ATCO Electric said the territory has a “good buffer” before it needs to move to rolling blackouts as a last resort.

“We’re in a fairly decent spot,” he said. “But it’s good to have this conversation [about rolling blackouts] so that people understand what they’re about.”

Yukon Energy has a multi-step emergency plan for managing peak demand, according to the utility’s website. There are multiple steps to alleviate pressure on the grid before rolling blackouts are necessary, including potentially sending an emergency alert to Yukoners asking them to immediately stop “all non-essential electricity use.”

In the meantime, Massie said, every little bit helps when it comes to saving energy.

Residents can conserve energy by using large appliances, like dishwashers and washing machines, outside of peak hours; turning off unused lights; unplugging unused electronics, like laptops; and using smaller appliances, like microwaves and toaster ovens, for cooking. Peak hours are between 7 a.m. and 10 a.m., and between 4 p.m. and 8 p.m.

Introduction

Sleep is a fundamental physiological process essential for maintaining physical health, mental well-being, and overall quality of life. However, it remains an underrecognized priority in public health agendas, particularly in low- and middle-income countries []. According to the 2025 China National Health Sleep White Paper, sleep quality among residents remains suboptimal, with approximately 64% experiencing sleep disturbances once or twice per week. []. The prevalence and severity of sleep disturbances vary across age groups. Young and middle-aged adults, as a core segment of the workforce, are particularly vulnerable to sleep disturbances due to occupational stress, long working hours, and irregular schedules [-]. Chronic sleep disturbance impairs stress management and exacerbates emotional distress while also being associated with poorer health outcomes, placing a burden on health care systems and reducing workplace productivity [,].

Sleep quality is influenced by multiple factors, including socioeconomic, physiological, psychological, and behavioral elements [,]. While these factors contribute to variations in sleep quality, increasing attention has been directed toward the role of eHealth literacy in health management. Defined as an individual’s ability to access, understand, evaluate, and apply health information from digital sources to make informed health decisions [], eHealth literacy has been shown to facilitate changes in health-related behaviors by bridging the gap between health information acquisition and actionable practices []. A systematic review further confirmed that eHealth literacy is associated with positive outcomes, including improved health behaviors, better psychological well-being, and increased use of health services []. These findings suggest that individuals with higher eHealth literacy are better equipped to adopt and maintain health-promoting behaviors that improve sleep outcomes.

While the significance of eHealth literacy in facilitating health-related behavior changes is recognized, its specific impact on sleep quality remains inadequately investigated. Some studies have suggested potential pathways through which eHealth literacy may influence sleep quality. For example, higher eHealth literacy has been associated with greater adherence to sleep hygiene practices [], potentially by enhancing individuals’ ability to identify and apply credible health information, thereby promoting better sleep quality. Another study indicated that higher eHealth literacy could reduce the risk of cyberchondria, which is subsequently associated with improved sleep quality []. However, evidence regarding the direct relationship between eHealth literacy and sleep quality is limited.

The association between eHealth literacy and sleep quality may vary by age. Previous research indicates that eHealth literacy is typically higher among younger populations [,]. Younger adults tend to engage more with digital health resources and may benefit significantly from them in managing sleep-related issues []. In contrast, middle-aged adults often face barriers in accessing and using such tools effectively despite a growing need for sleep management as sleep quality tends to decline with age [,]. This age-related disparity, coupled with unequal engagement with digital health resources, could contribute to widening gaps in sleep health—reflecting a digital health divide []. Therefore, understanding how eHealth literacy influences sleep quality across age groups is critical in addressing this divide.

This study aimed to examine the association between eHealth literacy and sleep quality across age groups among adults aged 18 to 59 years in Shanghai, China. By providing empirical evidence on age-specific associations, this study sought to inform tailored sleep interventions that incorporate eHealth literacy enhancement and address disparities arising from the digital health divide.

Methods

Participants and Procedure

This study was conducted between October and December 2022 in Shanghai. Three districts representing urban, periurban, and rural areas were randomly selected. Seven community health service centers from these districts that agreed to participate in the study were included. At each center, community residents were recruited using a convenience sampling approach. Before completing the survey, trained staff provided a detailed explanation of the study’s purpose and requirements, emphasizing the anonymity of responses. Participants were required to sign an informed consent form before proceeding with the questionnaire.

The inclusion criteria were (1) residence in Shanghai, (2) age 18 to 59 years, and (3) provision of informed consent and agreement to participate in the survey. The exclusion criteria were (1) severe hearing or speech impairments and (2) inability to comprehend the survey due to mental or cognitive conditions. Anonymous questionnaires were completed through the online survey platform Wenjuanxing.

The sample size was calculated using the prevalence of poor sleep quality as the primary outcome. On the basis of previous literature, the prevalence of poor sleep quality was estimated to be approximately 35%, with an allowable error of 3.5%. Using the PASS software for cross-sectional survey sample size calculation (NCSS, LLC), the minimum required sample size was determined to be 740. Considering the design effect of 2 due to convenience sampling and an anticipated nonresponse rate of 15%, the adjusted minimum required sample size was 1742.

A total of 1872 eligible participants were invited, and 1810 valid questionnaires were collected, yielding an effective response rate of 96.7%. The final sample size met the minimum requirement for analysis.

Measurements

eHealth Literacy

The Chinese version of the eHealth Literacy Scale (eHEALS), a translation of the original scale developed by Norman and Skinner [], was used to assess participants’ eHealth literacy []. This scale consists of 8 items, each rated on a 5-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”), yielding a total score between 8 and 40. Higher average scores indicate better self-perceived skills, knowledge, and comfort regarding online health information. The eHEALS has good reliability and validity among Chinese adults [,]. In this study, the scale showed good internal consistency, with a Cronbach α of 0.98.

Sleep Quality

The Pittsburgh Sleep Quality Index (PSQI) was used to measure participants’ sleep quality over the previous month []. The scale consists of 19 items evaluating 7 components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. The sum of the component scores yields a total score ranging from 0 to 21, with higher scores indicating poorer sleep quality. According to the recommended cutoff in the original study describing the PSQI, a total score of 5 or lower indicates good sleep quality, whereas a score above 5 indicates poor sleep quality []. The PSQI has demonstrated good psychometric robustness and factorial structure among Chinese adults [,].

Covariates

The selection of covariates was guided by the biopsychosocial theoretical framework [], which conceptualizes sleep quality as an outcome shaped by biological, psychological, and social determinants. Therefore, covariates were categorized into 3 domains as follows.

Biological Factors

Biological factors included sex (male or female), age, and weight status. Weight status was derived from self-reported height and weight, with BMI calculated as weight (kg) divided by height squared (m²). Overweight or obesity was defined based on the BMI classification criteria recommended by the Working Group on Obesity in China [].

Psychological Factors

Psychological factors included depressive and anxiety symptoms. Depressive symptoms were assessed using the Patient Health Questionnaire–9, with total scores ranging from 0 to 27. Scores of ≥5, 10, and 15 represent mild, moderate, and severe depressive symptoms, respectively []. In this study, the Patient Health Questionnaire–9 demonstrated good internal consistency (Cronbach α=0.964).

Anxiety symptoms were measured using the Generalized Anxiety Disorder–7 scale, with total scores ranging from 0 to 21. Scores of ≥5, 10, and 15 represent mild, moderate, and severe anxiety symptoms, respectively []. In this study, the Generalized Anxiety Disorder–7 exhibited good internal consistency (Cronbach α=0.978).

Social Factors

Social factors included educational attainment (junior high school or lower, senior high school, or college or higher), employment status (employed or unemployed), family monthly income (<¥5000 [US $707.30], ¥5001-¥9999 [US $707.44-$1414.45], ¥10,000-¥19,999 [US $1414.59-$2829.04], or ≥¥20,000 [US $2829.18]), marital status (either married or single, divorced, or widowed), and residential area (urban, periurban, or rural).

Statistical Analysis

Participants were first categorized into 3 age groups: emerging adults (18‐29 years) [], established adults (30‐45 years) [], and middle-aged adults (46‐59 years). Descriptive statistics were used to summarize their background variables, eHealth literacy, and sleep quality by age group. Given the skewed distribution of eHEALS scores, eHealth literacy was categorized into 3 groups based on IQRs: 25th percentile or below (lowest quartile), 25th to 75th percentile (middle quartiles), and 75th percentile or above (highest quartile). Differences in eHealth literacy and sleep quality among the 3 age groups were examined using chi-square tests.

To examine the association between eHealth literacy and sleep quality, multivariable logistic regression analyses were conducted in a stepwise manner. Model 1 adjusted for biological factors, including sex, age, and BMI. Model 2 incorporated additional adjustments for psychological factors, including depressive and anxiety symptoms. Model 3 further adjusted for social factors, including educational attainment, household monthly income, employment status, marital status, and residential area, to evaluate whether these factors influenced the association between eHealth literacy and sleep quality.

Finally, age-stratified analyses were performed to explore whether the association between eHealth literacy and sleep quality varied across age groups. In sensitivity analyses, we further included potential confounding variables, including chronic disease status and health behaviors (smoking and alcohol consumption), to assess the robustness of the findings.

Ethical Considerations

The study protocol was approved by the ethics committee of the Xuhui District Center for Disease Control and Prevention (XHLL202205). Written informed consent was obtained from all participants. Participant privacy and confidentiality were strictly protected. All data were anonymized and securely stored, with access limited to the research team.

Results

Descriptive Characteristics of the Sample

presents the sample characteristics. Of the 1810 participants, 673 (37.2%) were male, and 1137 (62.8%) were female, with a mean age of 40.0 (SD 10.1) years. Of these, 15.7% (285/1810) were emerging adults (18‐29 years), 53.3% (965/1810) were established adults (30-45 years), and 30.9% (560/1810) were middle-aged adults (45‐59 years). Most had a college degree or higher (n=1351, 74.6%), were employed (n=1533, 84.7%), and were married (n=1429, 79%). Approximately half (n=927, 51.2%) reported a monthly household income of ≥¥10,000 (US $1414.59). Regarding residence, 20.7% (375/1810) lived in urban areas, 30.4% (550/1810) lived in periurban areas, and 48.9% (885/1810) lived in rural areas. A total of 21.2% (384/1810) had moderate to severe depressive symptoms, and 15% (271/1810) had moderate to severe anxiety symptoms.

The median score on the eHEALS was 32 (IQR 28-40). The prevalence of poor sleep quality was 37.9% (686/1810). Chi-square analysis revealed significant associations between age group and both eHealth literacy (χ²₄=32.0; P<.001) and sleep quality (χ²₂=11.1; P=.004). Compared to younger adults, a lower proportion of middle-aged adults had eHealth literacy scores above the 75th percentile. Additionally, the proportion of middle-aged adults reporting poor sleep quality was higher than that of younger adults.

Association Between eHealth Literacy and Poor Sleep Quality: Multimodel Regression

The association between eHealth literacy and poor sleep quality was examined using multimodel logistic regression (). In model 1, after adjusting for biological factors, participants with eHealth literacy scores between the 25th and 75th percentiles (odds ratio [OR] 1.876, 95% CI 1.463-2.406, P<.001) and those with scores below the 25th percentile (OR 2.289, 95% CI 1.726-3.037, P<.001) had a significantly higher likelihood of reporting poor sleep quality compared to those with scores above the 75th percentile. After further adjusting for psychological factors in model 2, this association remained statistically significant (OR 1.574, 95% CI 1.204-2.058, P<.001 for scores between the 25th and 75th percentiles; OR 1.526, 95% CI 1.115-2.088, P=.008 for scores below the 25th percentile). The association persisted even after additional adjustment for social factors in model 3 (OR 1.594, 95% CI 1.216-2.089, P<.001 for scores between the 25th and 75th percentiles; OR 1.584, 95% CI 1.149-2.182, P=.005 for scores below the 25th percentile).

In the fully adjusted model (model 3), several covariates were also associated with poor sleep quality. Specifically, age (OR 1.030, 95% CI 1.016-1.044), depressive symptoms (OR 2.297, 95% CI 1.711-3.085 for mild; OR 3.402, 95% CI 2.132-5.429 for moderate; OR 2.092, 95% CI 1.156-3.784 for severe), educational attainment (OR 0.654, 95% CI 0.432-0.990 for senior high school), and marital status (OR 1.423, 95% CI 1.073-1.889 for single, divorced, or widowed) were associated with poor sleep quality.

Age-Stratified Analysis of the Association Between eHealth Literacy and Poor Sleep Quality

presents the age-stratified analysis of the association between eHealth literacy and sleep quality across the 3 age groups. Among emerging adults, participants with eHealth literacy scores between the 25th and 75th percentiles (OR 2.491, 95% CI 1.133‐5.479, P=.02) and those with scores below the 25th percentile (OR 2.975, 95% CI 1.230‐7.195, P=.02) had significantly higher odds of reporting poor sleep quality compared with those with scores above the 75th percentile.

Among established adults, participants with scores below the 25th percentile showed no statistically significant association (OR 1.303, 95% CI 0.834‐2.036, P=.24), whereas the group between the 25th and 75th percentiles showed a positive association (OR 1.439, 95% CI 1.001‐2.067, P=.049). However, this association was not statistically significant among middle-aged adults (OR 1.651, 95% CI 0.985‐2.770, P=.06 for scores between the 25th and 75th percentiles; OR 1.639, 95% CI 0.901‐2.980, P=.11 for scores below the 25th percentile).

Sensitivity Analysis

Sensitivity analyses adjusting additionally for smoking, alcohol consumption, and chronic disease status are presented in . Among emerging adults, lower eHealth literacy remained significantly associated with higher odds of poor sleep quality (OR 2.330, 95% CI 1.045‐5.197, P=.04 for scores between the 25th and 75th percentiles; OR 2.564, 95% CI 1.017‐6.464, P=.046 for scores below the 25th percentile). Among established adults, lower eHealth literacy did not show a statistically significant association after additional adjustment (OR 1.377, 95% CI 0.953‐1.991, P=.09 for scores between the 25th and 75th percentiles; OR 0.776, 95% CI 0.776‐1.930, P=.39 for scores below the 25th percentile). Among middle-aged adults, results also remained nonsignificant (OR 1.539, 95% CI 0.910‐2.600, P=.11 for scores between the 25th and 75th percentiles; OR 1.476, 95% CI 0.802‐2.710, P=.21 for scores below the 25th percentile).

Discussion

Principal Findings

This study investigated the association between eHealth literacy and sleep quality among adults aged 18 to 59 years in Shanghai, China. Overall, lower eHealth literacy scores were associated with a higher likelihood of poor sleep quality even after adjusting for biological, psychological, and social factors. The stratified analysis revealed that this association was significant among younger adults but not among middle-aged adults. These findings provide empirical evidence supporting the role of eHealth literacy as a potential determinant of sleep quality, particularly among younger populations.

The significant association observed in this study is consistent with prior research linking limited health literacy to poorer sleep outcomes and increased sleep disturbances [,]. While existing studies have largely focused on traditional health literacy, emerging research suggests that eHealth literacy may play a comparable role in health management in digital contexts []. Extending previous findings that link eHealth literacy to better adherence to sleep hygiene practices [], our results suggest a more direct association between eHealth literacy and overall sleep quality. Individuals with higher eHealth literacy are better equipped to critically evaluate online health information and adopt evidence-based sleep practices. In contrast, limited eHealth literacy may increase vulnerability to online misinformation and suboptimal sleep practices, ultimately leading to poorer sleep outcomes.

Beyond eHealth literacy, several other factors, including age, educational level, marital status, and depressive symptoms, were also associated with sleep quality in the overall model, consistent with findings from previous research [,]. Among these factors, depressive symptoms emerged as a well-established and particularly strong predictor of sleep disturbances []. Individuals with mild to severe depressive symptoms had approximately 2 to 3 times higher odds of reporting poor sleep quality compared with those without depressive symptoms. This strong psychological effect may have attenuated the independent contributions of other covariates when adjusting simultaneously. In addition, prior studies have shown that individuals with lower eHealth literacy tend to experience greater psychological distress [], partly due to the misuse of misleading or low-quality information encountered online. These patterns suggest that mental health may play an important role in the pathway through which eHealth literacy relates to sleep quality.

In the age-stratified analysis, lower eHealth literacy was associated with poorer sleep quality only among emerging and established adults. This finding aligns with those of prior research indicating that younger adults typically engage more actively with digital health information [,] and rely more on online resources for health-related decisions. In contrast, middle-aged and older adults tend to depend more on traditional health care resources [], making their sleep quality less influenced by online health information use. Furthermore, this age-specific association may also reflect distinct underlying mechanisms of sleep disturbances. Among middle-aged adults, sleep disturbance is more frequently attributed to age-related neurophysiological and neurochemical changes (eg, reduced sleep duration and increased fragmentation) []. Such physiologically driven sleep disturbances are only minimally related to eHealth literacy. Conversely, younger adults often experience irregular sleep patterns driven by external demands (eg, academic or occupational stress) [,], which may be more amenable to modification through improved eHealth literacy.

The age-specific association between eHealth literacy and sleep quality aligns with broader concerns about the digital health divide []. Although digital health technologies offer scalable and cost-effective solutions for health management, their benefits are not equitably distributed across age groups. Structural barriers such as limited access, lower digital confidence, and affordability disproportionately affect marginalized and older populations []. As sleep disturbances tend to increase with age, middle-aged adults may face a dual challenge: increased physiological susceptibility to poor sleep and reduced capacity to engage with digital resources. However, existing eHealth interventions aimed at improving sleep outcomes have predominantly targeted younger populations [-]. Without targeted support, the expansion of digital health tools may unintentionally widen existing age-related disparities in sleep health.

Our findings highlight the importance of improving eHealth literacy to promote better sleep outcomes. For example, a 6-week online intervention during the COVID-19 pandemic integrated health education and digital skill training to improve university students’ eHealth literacy and related health behaviors []. Although short-term sleep improvements were limited, the study highlighted the potential of eHealth literacy–based interventions and the importance of long-term evaluation []. Given the age-stratified association observed in our study, tailoring interventions to address age-specific barriers is essential. Middle-aged adults, who have lower digital engagement, may require additional support to effectively benefit from digital tools—such as affordable internet access, community-based digital skill training, and user-friendly interface design []. By accounting for the unique needs of different age groups, eHealth literacy can be leveraged to improve health outcomes for all, ultimately advancing digital health equity.

Strengths and Limitations

This study has several strengths, including adjustment for multiple confounders at different levels and an age-stratified analysis, offering a more nuanced understanding of the association between eHealth literacy and sleep quality. However, several limitations should be acknowledged. First, the cross-sectional design precludes causal inferences between eHealth literacy and sleep quality. Future studies should use longitudinal or experimental designs to clarify temporal relationships and causal pathways between eHealth literacy and sleep outcomes. Second, the study was conducted exclusively among adults in Shanghai, limiting generalizability to other regions with different levels of health literacy. Third, reliance on self-reported measures for eHealth literacy and sleep quality may introduce recall and social desirability biases, potentially affecting the accuracy of the results. Fourth, due to the need to minimize respondent burden, alcohol use and smoking were assessed using frequency-based measures rather than consumption volume. This may not fully capture the potentially nonlinear associations between these behaviors and sleep quality. Fifth, the overall sample size was relatively limited, particularly within certain age groups, which may have affected the statistical precision of the findings. Finally, we did not include key determinants of sleep such as work or study pressure, exercise habits, and online behaviors, which may introduce residual confounding. Future studies should incorporate these psychosocial and behavioral factors to more fully disentangle the association between eHealth literacy and sleep quality.

Conclusions

This study examined the association between eHealth literacy and sleep quality among adults aged 18 to 59 years in Shanghai, China. Findings showed that lower eHealth literacy was significantly associated with a higher likelihood of reporting poor sleep quality. Age-stratified analysis further revealed that this relationship was significant among younger adults but not among middle-aged adults. These findings underscore the potential of enhancing eHealth literacy as an effective strategy for improving sleep health, particularly when tailored to age-specific needs and digital access levels. Targeted measures to reduce the digital health divide will be essential in promoting more equitable health outcomes across age groups.

Although the Valkyries’ season has concluded, the action continues for the team’s athletes. Currently, 13 Valkyries players are competing overseas, with more scheduled to participate in the Unrivaled league which has begun training camp. For a complete guide and all the latest updates on the Valkyries “Beyond the Bay,” click here.

Team: Uni Girona CB | Spain
Valkyries Teammates: Justė Jocytė

Team: Reyer Venezia | Italy
Valkyries Teammates: N/A

Team: USK Praha | Czech Republic
Valkyries Teammates: Janelle Salaün

Team: PF Schio | Italy
Valkyries Teammates: Cecilia Zandalasini

Team: Uni Girona CB | Spain
Valkyries Teammates: Laeticia Amihere

Team: Fenerbahçe | Turkey
Valkyries Teammates: Monique Billings

Team: USK Praha | Czech Republic
Valkyries Teammates: Kaitlyn Chen

Team: PF Schio | Italy
Valkyries Teammates: María Conde