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Article

eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions

by
Guyue Tang
1,*,†,
Zhidiankui Xu
2,† and
Shinichi Koyama
3
1
College of Art and Design, Division of Arts, Shenzhen University, Shenzhen 518060, China
2
School of Design, Jiangnan University, Wuxi 214122, China
3
Institute of Art and Design, University of Tsukuba, Tsukuba 305-8574, Ibaraki, Japan
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Systems 2025, 13(12), 1070; https://doi.org/10.3390/systems13121070
Submission received: 24 October 2025 / Revised: 12 November 2025 / Accepted: 26 November 2025 / Published: 27 November 2025
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

Over-the-counter medication electronic health records (OTC-EHRs) play a significant role in users’ self-medication practices. In this study, we consider the potential advantages of wearable smart devices in health management, along with the information processing capabilities of artificial intelligence (AI), and we propose a conceptual design for an AI-assisted wearable OTC-EHR system. Our objective was to systematically explore the relationship between eHealth literacy, users’ attitudes, and willingness to use the proposed system, as well as to discuss AI interventions. Internet users from China participated in an online survey examining eHealth literacy, subjective attitudes, and motivation to use this conceptual design. Descriptive statistical, correlation, difference, and regression analyses were conducted on 372 valid responses to test the research hypotheses. The results showed that the wearable-device-based OTC-EHR system with AI assistance was accepted by most responders and positively associated with eHealth literacy, which was, in turn, associated with decision-making preferences. This study suggests that AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance.

1. Introduction

Over-the-counter medication electronic health records (OTC-EHRs) are expected to play a significant role in self-medication in the near future [1,2,3]. Considering the potential advantages of wearable devices in health management, as well as the information processing capabilities of artificial intelligence (AI), we propose a conceptual design for an AI-assisted wearable OTC-EHR. To test the research assumptions, an online survey was conducted to investigate the relationship between the eHealth literacy of users (defined as patients and consumers of medicines), their attitudes toward the conceptual design, and their willingness to use it. The findings contribute to a preliminary assessment of the feasibility of the AI-assisted wearable OTC-EHR system.

1.1. Application of OTC-EHR in Wearable Devices

Electronic health records (EHRs) are based on the mature concept of health information technology (HIT) and have been actively used in clinical fields, medical care, and self-medication management [4,5,6,7,8]. OTC-EHRs are based on the expansion of the application of EHR in the field of over-the-counter (OTC) medicines [1,3]. Through OTC-EHRs, users purchasing OTC medicines can use digital devices such as smartphones and tablets to access real user feedback on medication usage, effectiveness, and side effects. This information helps them make informed decisions regarding the purchase of OTC medicines [1,2]. Empirical studies conducted in Japan indicate that consumers and patients hold a positive attitude toward the use of OTC-EHRs on tablet PCs. Moreover, the application of these records in tablet-based digital pharmacies demonstrates their potential to enhance consumers’ abilities to access information and improve eHealth literacy [1,2]. However, studies on the application of OTC-EHRs in broader use scenarios, particularly those involving smart wearable devices, are necessary [3].

1.2. The Definition of Wearable Devices and Potential Advantages in Health Management

Wearable devices are defined as wrist-worn devices equipped with computing capabilities, biometric sensors, and connectivity to the internet and other smart devices [9,10]. Owing to their capacity for real-time health monitoring, their adoption has grown rapidly [11,12,13]. The global wearable electronics market is projected to grow at an annual growth rate of 23.3% and reach USD 173.7 billion by 2030 [14]. Meanwhile, users are increasingly willing to share device-generated data with healthcare providers [15]. With regulatory approval, such as Food and Drug Administration (FDA) clearance for detecting major medical conditions and processing clinical data [11,16], wearable devices are expected to play an expanding role in personal health management.
Recent advancements in smartwatch research have facilitated numerous applications in the fields of remote health monitoring and mobile health (mHealth) [17,18]. Wearable devices can detect an increasing range of physiological data at a range of accuracies, allowing individual users to access personalized medical data. This potentially aids in the prevention and treatment of diseases [11,19], as well as enabling timely communication with healthcare professionals and doctors [17,20]. Evidence suggests that, by analyzing personal health data, smartwatches can improve healthcare efficiency and support conditions, with applications ranging from stress management to cardiovascular disease, COVID-19 detection, and seizure risk monitoring [11,17,21,22,23,24,25,26,27].
Wearable technologies are thus emerging as critical platforms for diagnosis, symptom reporting, and health decision-making [11,28,29]. Researchers have proposed the integration of smartwatch data with EHRs and highlighted the importance of considering factors, such as participants’ demographic variables and health literacy, in such integration [20]. However, empirical studies on integrating OTC-EHRs with wearable devices remain limited.

1.3. Self-Medication, eHealth Literacy, and Interventions with Artificial Intelligence

Self-medication is a prevalent self-care activity that is practiced worldwide [30,31,32,33], and it plays a critical role in supporting consumers and patients in making informed decisions in terms of digital health management [1,2]. With advances in AI, there is a growing demand for AI assistants that can process complex health information, improve data accuracy, and enhance decision-making. Integrating wearable device data with AI algorithms has the potential to improve self-medication and enable more personalized healthcare solutions [11,34].
eHealth literacy refers to the ability to utilize digital health information to address health-related issues [35]. Most importantly, it requires users to assess the reliability of information from the internet and other electronic sources and make appropriate decisions [36,37]. Higher eHealth literacy has been linked to greater engagement in health-promoting practices, including effective use of digital health tools, and is consistently associated with positive health behaviors [1,35,36,37,38,39,40,41].
Empirical studies in Japan have shown that eHealth literacy is positively associated with both access to OTC medicine information and the perceived usefulness of OTC-EHRs on tablet PCs [1,2,37]. Despite these advances, research on AI interventions in self-medication remains limited, particularly regarding the relationship between eHealth literacy and attitudes toward these interventions.

1.4. Application of AI-Assisted Wearable-Device-Based OTC-EHRs

Medical AI is regarded as a critical resource for advancing innovative individual and public healthcare in the future [42] and is especially well suited to addressing both the complex data types and the pressing questions in healthcare [43]. Within the domain of EHRs, AI demonstrates advantages in information processing and predictive assistance, underscoring its significant potential for enhancing EHR utilization [44,45,46]. However, research on the application of AI in OTC-EHRs remains limited and requires further development.
Meanwhile, wearable devices such as smartwatches enable multi-scenario applications, interoperability with other smart technologies, large-scale data transmission and analysis, continuous computation, and ongoing user interaction [9,17,47]. Despite this promise, their role in healthcare requires further empirical validation, particularly in relation to technological functionality, user acceptability, and health efficacy [17,20].
To address this gap, this study proposes a conceptual framework for integrating AI-assisted OTC-EHRs into wearable devices, hereafter referred to as the AI-assisted wearable OTC-EHR system (Figure 1). Within this framework, users anonymously contribute health-related data, such as side effects, efficacy, and physiological responses, during self-medication. These data are analyzed by AI, validated by official research institutions or public medical institutions, and incorporated into a cloud-based OTC-EHR database [1]. Users can then access comprehensive medication information and receive AI-assisted personalized medical information via wearable devices, while the AI flags potential risks for medical oversight [37].
In this conceptual design, AI is defined as (i) processing and analyzing anonymous health information shared by users in a database, (ii) supporting users in obtaining appropriate medical information through the OTC-EHR, and (iii) detecting and reporting potential risks to professionals during self-medication decision-making. Employing this, the wearable OTC-EHR system leverages the advanced data processing capabilities of AI not to substitute but to augment human decision-making, thereby supporting safer and more informed self-medication practices.

1.5. Research Hypotheses

In summary, given the potential benefits of integrating wearable devices with AI-assisted OTC-EHRs for self-medication and the central role of eHealth literacy in digital healthcare, this study extends prior research by proposing hypotheses from two perspectives [1,20]: (i) users’ adoption of wearable OTC-EHRs and (ii) attitudes toward the AI-assisted wearable OTC-EHR system. The specific hypotheses are outlined below and illustrated in Figure 2.
H1. 
eHealth literacy is positively associated with the use of wearable devices.
H2. 
Users with higher eHealth literacy are more willing to utilize wearable OTC-EHRs.
H3. 
Users with higher eHealth literacy are more willing to share health information through wearable OTC-EHRs.
H4. 
Users with higher eHealth literacy are more inclined to personalize health information using wearable OTC-EHRs for self-medication.
H5. 
Users with higher eHealth literacy perceive wearable OTC-EHRs as more helpful.
H6. 
Attitudes toward AI intervention are positively correlated with users’ motivation to use AI-assisted wearable OTC-EHRs.
H7. 
Users with higher eHealth literacy are more inclined to use AI-assisted wearable OTC-EHRs to make medication-related decisions.

2. Materials and Methods

This study tested the research hypotheses through an online survey and explored the feasibility of applying AI-assisted wearable OTC-EHR systems. The analysis focused on three aspects: the use of wearable devices, the use of health-related information recorded by these devices, and the use of medication-related information within the AI-assisted OTC-EHR. These were examined in conjunction with consumers’ subjective attitudes and their motivation to use AI interventions.
In the present study, we collected responses online. To improve the quality of the data, we implemented control measures through two approaches. Firstly, we tested the time required to randomly complete the test process without reading any content, which was approximately one minute. Responses completed in less than one minute were identified as abnormal by the system and automatically excluded [1,37]. Secondly, two screening questions were included for validation: “(i) Have you ever used a wearable device?” and “(ii) How frequently do you use a wearable device?” If the responses to these two questions were contradictory, the response was also identified as abnormal and excluded.

2.1. Participants and Sample Size

In total, data from 372 participants were entered in the data analyses.
In this study, we referred to the characteristics of participants from previous studies and considered potential internet-based device usage experiences [1,37,38]. We considered an online survey to be appropriate because the internet is widely accessible, therefore making the survey representative.
G*Power version 3.1 suggested that to detect a correlation coefficient of r = 0.21 with 80% power (α = 0.05; two-tailed), which is an average effect size in social psychology [37,48,49], the present study would need 175 participants [37,49,50]. The 372 valid responses made this study sensitive to the effect size r = 0.14 with 80% power, allowing it to reliably detect correlations greater than r = 0.14, and constituted a reasonable sample size [51,52]. In addition, the corresponding actual effect sizes for other statistical methods were reported.

2.2. Measures

This study began by outlining research ethics and the goal of the anonymous survey. To increase the survey’s dependability, the following instructions were initially given: (i) complete the survey by yourself, without seeking input from others; (ii) avoid eating or drinking during the survey; (iii) complete the survey in a calm environment free from TVs, music, and other distractions. Participants were free to stop taking part in the survey at any moment [1,37].
The online survey comprised five sections: (i) the Chinese eHealth literacy scale (C-eHEALS) [53]; (ii) usage of wearable devices; (iii) attitudes toward utilizing health data with wearable-device-based OTC-EHRs; (iv) attitudes toward AI interventions in wearable-device-based OTC-EHRs; (v) participant characteristics, including gender, age and occupations.
To ensure consistent understanding of the survey items, wearable devices were defined as smartwatches (e.g., Apple Watch (Apple Inc., Cupertino, CA, United States), Huawei Watch (Huawei Technologies Co., Ltd., Shenzhen, Guangdong, China), Google Watch (Alphabet Inc., Mountain View, CA, United States)) and smart bracelets with intelligent functions and computing capabilities. This definition was clearly communicated to all participants.

2.2.1. Participant Characteristics

In addition to gender, age, and occupation, eHealth literacy was measured using C-eHEALS, which is the Chinese version of the eHealth literacy scale (Appendix A) [53,54]. Responses to questions on literacy were provided on a five-point Likert scale ranging from “strongly agree = 5” to “strongly disagree = 1”. A higher score indicated better perceived eHealth literacy. The reliability of the eHealth literacy scale has been demonstrated by numerous studies [41]. The Cronbach alpha value of C-eHEALS was 0.89 in the present study. A reliability coefficient of 0.70 is generally considered acceptable, while a coefficient exceeding 0.80 is indicative of high internal consistency [23,55].

2.2.2. Usage of Wearable Devices

This section included two survey questions. First, the primary question was: “Do you use wearable devices for health management?” The responses ranged from “Never = 1” to “Very Frequently = 5.” The second question served as a supplementary item to assess the reliability of the questionnaire: “Have you ever used smart wearable devices?” The response options were “Yes” and “No.” If the answers to this question contradicted the responses to the primary question, they were classified as abnormal responses. This section defined the dependent variable for H1 and was used to test hypothesis H1.

2.2.3. Attitudes Toward Utilizing Wearable OTC-EHR

This section was divided into four aspects, shown in Table 1: utilizing wearable-device-based OTC-EHRs, sharing health data, perceived helpfulness, and motivation for using personalized health management data. This section was designed to test hypotheses H2 through H5. Variables (i), (ii), (iii), and (iv) defined the dependent variables for H2, H3, H4, and H5, respectively, while (i), (ii), and (iii) served as the independent variables for H5.

2.2.4. Attitudes Toward AI Intervention for Wearable OTC-EHR

This part investigated the participants’ attitudes towards the use of OTC-EHRs based on wearable devices with AI intervention using three aspects and four questions, as shown in Table 2. Building on the previous section, this part incorporated the influence of AI intervention and was designed to test hypotheses H6 and H7. Variables (i) and (ii) defined the dependent variables for H6, while variable (iii) defined the dependent variable for H7.

2.3. Statistical Analyses

First, this study referred to the statistical methods of previous studies to evaluate the demographic data of the participants [1,30,37]. The degree of eHealth literacy was designated as the primary independent variable for analysis, while gender, occupation, and age were utilized as descriptive statistics to support the analysis. More specifically, age was divided into four groups: 18–29, 30–39, 40–49, and over 50 years (since the legal age of adulthood in China is 18 years old, the statistical age started at 18). Occupations were divided into two groups: medical practitioners and non-medical practitioners [2]. Second, the willingness of users to utilize the wearable-device-based OTC-EHR, sharing health information to wearable OTC-EHRs, and users’ attitudes toward the AI-assisted wearable OTC-EHR, were examined as major variables.
The outcomes of C-eHEALS were calculated as both continuous and categorical variables [53,56]. The average score for C-eHEALS was 31.61 (SD = 4.93). Participants were divided into two categories (high or low literacy) relative to the median group value (median = 32.00; IQR: 29.00–35.00) based on previous studies [1,37,38,57,58,59].
The Chi-square test, the t-test, and one-way ANOVA were used to examine differences based on eHealth literacy, gender, and age. SPSS version 29.0 was used to perform the analysis, and p-values less than 0.05 were considered statistically significant. The effect sizes for the Chi-square goodness of fit, Chi-square crosstab, t-test, and ANOVA were reported as Cohen’s W, Cramér’s V, Cohen’s d, and η2, respectively [60,61,62].

3. Results

3.1. Characteristics of Participants

The demographic data and C-eHealth scores are shown in Table 3. The results of this study show that C-eHealth scores were not significantly related to age (r = −0.05; p = 0.34). The t-test showed that there was no significant difference between the C-eHealth scores of men and women (t (369) = 0.79; d = 0.09; p = 0.43). Meanwhile, the scores of medical practitioners were significantly higher than those of non-medical practitioners (t (370) = 2.96; d = 0.56; p = 0.003). The degree of eHealth literacy was not significantly related to age or gender but was related to whether one was a practitioner in a medical-related occupation.

3.2. The Relationship Between eHealth Literacy and Wearable Device Usage

Regarding the question “Do you use wearable devices for health management?”, the response distribution was as follows: Never = 50 (13.44%), Occasionally = 45 (12.10%), Sometimes = 150 (40.32%), Often = 99 (26.61%), and Very often = 28 (7.53%) (χ2(4) = 133.51; W = 0.60; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Never”) to 5 (“Very often”).
Firstly, there was a significant positive correlation between C-eHealth scores and the frequency of wearable device use, as shown in Figure 3a (r = 0.35; p < 0.001). The higher the degree of eHealth literacy was, the more frequently a wearable device was used. Secondly, the t-test showed that participants with high C-eHealth scores had a significantly higher usage frequency (3.38 ± 1.07) compared with those with lower C-eHealth scores (2.72 ± 1.05) (t (370) = 6.02; d = 0.63; p < 0.001), as shown in Figure 3b.

3.3. eHealth Literacy and Attitudes Toward Utilizing Wearable-Device-Based OTC-EHR

3.3.1. eHealth Literacy and Attitudes Toward Using Wearable-Device-Based OTC-EHR

Regarding the question “Are you willing to use the wearable devices to record your health status during medication use?”, the response distribution was as follows: Absolutely unwilling = 50 (13.44%), Unwilling = 7 (1.88%), Not sure = 60 (16.13%), Willing = 236 (63.44%), and Very willing = 47 (12.63%) (χ2(4) = 461.84; W = 1.11; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Absolutely unwilling”) to 5 (“Very willing”). One-sample t-testing (compared to “Not sure” values) showed a relatively strong willingness to use devices (3.79 ± 0.81) (t (371) = 18.89; d = 0.98; p < 0.001).
Firstly, a significant positive correlation was found between C-eHealth scores and the willingness to use wearable devices to record medication health status (r = 0.37; p < 0.001), as shown in Figure 4a. The higher the degree of eHealth literacy was, the higher the willingness was. Thirdly, t-testing showed that participants with high C-eHealth scores had a significantly higher willingness to record medication health status through wearable devices (4.10 ± 0.69) compared with participants with lower C-eHealth scores (3.52 ± 0.80) (t (369.88) = 7.36; d = 0.77; p < 0.001), as shown in Figure 4b.

3.3.2. eHealth Literacy and Attitudes Toward Sharing Anonymized Health Data to Wearable-Device-Based OTC-EHR

Regarding sharing anonymized health data in terms of OTC medicine use to wearable-device-based OTC-EHRs, the question “If the health information recorded by wearable devices during your medication use is anonymized and protected, are you willing to share this information?” was asked. The response distribution was as follows: Absolutely unwilling = 9 (2.42%), Unwilling = 49 (13.17%), Not sure = 79 (21.24%), Willing = 187 (50.27%), and Very willing = 48 (12.90%) (χ2(4) = 246.23; W = 0.81; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Absolutely unwilling”) to 5 (“Very willing”). One-sample t-testing (compared to “Not sure” values) showed a relatively strong willingness to share anonymized health data (4.13 ± 0.34) (t (371) = 11.72; d = 0.61; p < 0.001). A significant positive correlation was found between C-eHealth scores and this willingness (r = 0.36; p < 0.001). Compared with participants with lower eHealth literacy, as shown in Figure 5a, those with high eHealth literacy showed a higher willingness to share their own anonymized data to wearable-device-based OTC-EHRs (t (368.04) = 7.08; d = 0.73; p < 0.001).

3.3.3. eHealth Literacy and Motivation to Personalize Health Management Through Physiological Data and OTC Medicine Usage Information with Wearable-Device-Based OTC-EHRs

Regarding the motivation to use physiological data (e.g., heart rate) recorded by smartwatches and health information during the use of OTC medicines to self-medicate, the question “If the health information recorded by wearable devices during your medication use is anonymized and protected, are you willing to share this information?” was asked. The response distribution was as follows: Absolutely unwilling = 4 (1.08%), Unwilling = 19 (5.11%), Not sure = 63 (16.94%), Willing = 222 (59.68%), and Very willing = 64 (17.20%) (χ2(4) = 403.89; W = 1.04; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Absolutely unwilling”) to 5 (“Very willing”). A significant positive correlation was found between C-eHealth scores and the motivation to use physiological data (r = 0.44; p < 0.001). One-sample t-testing (compared to “Not sure” values) showed a very strong motivation to personalize health management through physiological data and OTC medicine usage information (3.87 ± 0.79) (t (371) = 21.15; d = 1.10; p < 0.001). Moreover, as shown in Figure 5b, those with high C-eHealth scores demonstrated higher motivation compared with those with lower scores (t (368.13) = 7.53; d = 0.77; p < 0.001).

3.3.4. eHealth Literacy, Wearable Device Usage, Sharing Health Information, Motivation to Personalize Health Management Through Physiological Data, and Perceived Helpfulness of OTC-EHRs

Regarding the perceived helpfulness of wearable-device-based OTC-EHRs, the question “If you can check anonymized health information shared by other patients when purchasing OTC medicines, do you think this information could help you choose the appropriate medicines?” was asked. The response distribution was as follows: Absolutely not helpful = 3 (0.81%), Not helpful = 29 (7.80%), Not sure = 80 (21.51%), Helpful = 204 (54.84%), and Very helpful = 56 (15.05%) (χ2(4) = 326.95; W = 0.94; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Absolutely not helpful”) to 5 (“Very helpful”). One-sample t-testing (compared to “Not sure” values) showed a relatively strong perceived helpfulness (3.76 ± 0.83) (t (371) = 17.51; d = 0.91; p < 0.001).
In order to assess the potential factors explaining participants’ perception of the helpfulness of wearable-device-based OTC-EHRs, as shown in Table 4, linear regression analysis was applied; the independent variables were C-eHealth scores, attitudes toward using wearable devices, sharing health information to wearable-device-based OTC-EHRs, and motivation to personalize health management through physiological data, and the dependent variable was the perceived helpfulness of OTC-EHRs. A significant regression equation was found, F (4,367) = 54.40 (p < 0.001), with an R2 of 0.37 and an adjusted R2 of 0.37. In comparison to attitudes toward using wearable devices, sharing health information to wearable-device-based OTC-EHRs, and motivation to personalize health management through physiological data, eHealth literacy did not emerge as a significant factor of the perceived helpfulness of wearable-device-based OTC-EHRs.

3.4. Attitude Towards AI Intervention in Wearable-Device-Based OTC-EHRs Considering Ehealth Literacy

3.4.1. Whether Wearable Devices or AI-Assisted Wearable Devices Are Appropriate for Recording and Managing Medication-Related Health Information

Firstly, regarding whether wearable devices are appropriate for recording and managing medication-related health information, the question “Do you consider wearable devices to be appropriate digital tools for recording and managing health information related to medication use?” was asked. The response distribution was as follows: Definitely not = 3 (0.81%), Not = 23 (6.18%), Not sure = 84 (22.58%), Yes = 209 (56.18%), and Definitely yes = 53 (14.25%) (χ2(4) = 354.94; W = 0.98; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Absolutely not”) to 5 (“Definitely yes”). One-sample t-testing (compared to “Not sure” values) showed a relatively high acceptance to record and manage medication-related health information through wearable devices (3.77 ± 0.80) (t (371) = 18.59; d = 0.96; p < 0.001). As shown in Figure 6a,b, those with high C-eHealth scores showed more positive attitudes regarding recording and managing health information through wearable OTC-EHRs compared with those with lower C-eHealth scores (t (368.32) = 8.34; d = 0.86; p < 0.001). Additionally, those with high scores showed more positive attitudes regarding recording and managing health information through AI-assisted wearable OTC-EHRs compared with those with lower scores (t (369.45) = 6.40; d = 0.74; p < 0.001).
Comparing the attitudes towards whether wearable devices are appropriate for recording and managing medication-related health information, as shown in Figure 6c, the results of the independent t-test showed that the presence or absence of AI intervention did not significantly change these attitudes (t (742) = 0.51; d = 0.04; p = 0.61).

3.4.2. Trust in Health Management Advice from AI-Assisted Wearable OTC-EHRs

Regarding the question “Do you trust AI to analyze the health information related to medication use recorded and managed through smart wearable devices to provide you with medication advice?”, the response distribution was as follows: Totally distrust = 2 (0.54%), Distrust = 33 (8.87%), Not sure = 111 (29.84%), Trust = 185 (49.73%), and Very trust = 41 (11.02%) (χ2(4) = 290.90; W = 0.88; p < 0.001). These responses were converted into corresponding scores ranging from 1 (“Totally distrust”) to 5 (“Very trust”). One-sample T-testing (compared to “Not sure” values) showed relatively strong trust (3.62 ± 0.82) (t (371) = 14.59; d = 0.76; p < 0.001). Moreover, the participants with higher C-eHealth scores had significantly higher trust in AI use for self-medication compared with those with lower C-eHealth scores (t (369.64) = 7.77; d = 0.80; p < 0.001), as shown in Figure 7.

3.4.3. Attitudes Towards Approaches to Receiving Personalized Advice on Medication Decisions Considering AI Intervention

First, as shown in Table 5, the highest proportion of participants preferred to review medication information themselves, consider AI recommendations, and then make their own decisions. This was followed by participants who relied entirely on AI to make decisions, those who made decisions entirely on their own, those who reviewed the information but relied on doctors and pharmacists for decisions, those who relied entirely on doctors, and, finally, those who reviewed the information themselves but allowed AI to make the decisions (χ2(5) = 128.32; W = 0.59; p < 0.001).
Among the participants, those with lower C-eHealth scores were more likely to rely entirely on AI for decision-making or to review the information themselves and then rely on doctors and pharmacists to make decisions. In contrast, participants with higher C-eHealth scores were more inclined to make decisions entirely on their own or review the information themselves and then allow AI to make the decisions (χ2(5) = 16.69; V = 0.21; p = 0.005).

4. Discussion

4.1. eHealth Literacy and Wearable Device Use

Regarding the relationship between respondent characteristics and eHealth literacy, in this study, we found that age and gender are not significantly associated with eHealth literacy, which is consistent with a previous study [37]. Meanwhile, participants employed in medical-related occupations demonstrated significantly higher levels of eHealth literacy compared to those in non-medical fields, a finding that corroborates earlier studies [2].
Moreover, as shown in Figure 8, this study suggests that eHealth literacy and wearable device use are significantly positively correlated. Users with higher eHealth literacy have higher motivation to use wearable devices in taking OTC medicines and managing health information because of their relatively higher ability to obtain medical information and make health-related decisions [35,37,39,63]. Hypothesis H1 is supported.

4.2. Attitudes Toward Wearable OTC-EHRs

Since the information shared by users is crucial for training AI models [42,64,65], users’ attitudes toward information sharing are a core consideration in this conceptual design. In this study, we found that most users hold a positive attitude toward using wearable devices and sharing personal health information through wearable-device-based OTC-EHR systems. This may be attributed to their positive perception of the benefits of OTC-EHRs [1,9]. Furthermore, users with higher eHealth literacy exhibit more positive attitudes compared with those with lower eHealth literacy. This may be because individuals with higher eHealth literacy tend to hold more favorable views regarding health information privacy and technological transparency [1,66]. In system design, it is therefore necessary to consider issues such as health information privacy and technological and policy transparency to promote the broader adoption of wearable OTC-EHR systems [67]. Thus, Hypotheses H2 and H3 are supported.
Meanwhile, this study indicates that users perceive a high level of helpfulness in using wearable OTC-EHRs, which may actively encourage them to adopt OTC-EHRs on wearable devices [1,6,68]. Users also maintain a positive attitude toward receiving personalized medication advice derived from physiological data detected by wearable devices. Those with higher eHealth literacy are more inclined to utilize personalized health information via wearable OTC-EHRs for self-medication. Therefore, in combination with previous studies on OTC-EHR use through smartphones and tablet PCs [1,2], it can be inferred that the conceptual design of personalized self-medication, enabled by sharing OTC-EHR data across various digital devices (e.g., smartphones, smartwatches, tablets, personal computers, and other wearable devices), has promising application potential. Hypothesis H4 is not supported.
However, the present study suggests that perceived helpfulness towards utilizing health information and physiological data in wearable OTC-EHRs may be more strongly correlated with attitude and motivation rather than the degree of eHealth literacy. This may be because eHealth literacy is more strongly correlated with users’ ability to seek and utilize information and related health behaviors [1,2,37], while perceived helpfulness is potentially more associated with users’ subjective cognition and motivation in the use of wearable OTC-EHRs. Hypothesis H5 is not supported.

4.3. eHealth Literacy, Decision-Making, and AI Intervention

Regarding AI intervention, our results highlight the complexity of the relationship between user decision-making and trust in AI. On the one hand, the presence or absence of AI intervention does not significantly influence users’ motivation or attitudes toward using wearable OTC-EHRs. On the other hand, in terms of making medication-related decisions, regardless of their degree of eHealth literacy, users exhibit a positive inclination toward using AI for auxiliary decision-making.
Moreover, concerning preferences between AI-assisted and professional-assisted decision-making, this study suggests that some users with relatively low eHealth literacy tend to rely entirely on AI or seek guidance from healthcare professionals such as doctors and pharmacists. In contrast, users with higher eHealth literacy are more likely to prefer making their own decisions or using AI as a supportive tool in their decision-making process.
Based on the results of this study, we can infer potential contradictions in users’ attitudes toward AI. On the one hand, users express concerns about the reliability of AI; on the other hand, they maintain a generally positive and open attitude toward AI’s decision-making capabilities. This cognitive dissonance may be related to the lack of transparency regarding AI mechanisms and algorithms [46]. Since users often cannot understand how AI systems function, they tend to be skeptical about their reliability. Additionally, this contradiction may stem from the tension between perceived benefits and privacy concerns [9]. While users are concerned about the privacy of their personal health information [1,69,70,71], they also recognize the potential value of AI in processing medical information and are thus reluctant to forgo its assistance. This paradox highlights both the promise and the challenges of integrating AI into wearable devices for self-medication purposes [11,34,44]. Consequently, Hypothesis H6 is partially supported.
Further discussion suggests that differences in users’ perceptions of AI-assisted decision-making may be related to their confidence in managing health and making decisions using digital health and medical information, as well as their overall self-medication ability. Users with high eHealth literacy tend to possess stronger self-determination and greater confidence in their capacity for self-medication [63,72,73]. Meanwhile, foundational research on human–automation interaction demonstrates that appropriate reliance on automated systems is contingent upon users’ comprehension of the system’s purpose, process, and performance [67,74]. Individuals with higher eHealth literacy are more adept at sourcing and critically appraising medical information and are therefore better positioned to discern when AI-generated recommendations are trustworthy and when human judgment should be prioritized.
Consequently, users with higher eHealth literacy tend to exhibit calibrated, bounded trust in AI, rather than unquestioning deference. Thus, although they demonstrate greater motivation to use AI-assisted wearable OTC-EHRs, they do not exhibit overreliance on AI. In contrast, users with low eHealth literacy are less inclined to make independent decisions, have lower confidence in managing their health, and are more likely to rely on AI and healthcare professionals for decision-making support [63,72,73]. While their overall motivation to adopt such technologies may be lower, they may demonstrate greater dependency on AI. Accordingly, Hypothesis H7 is not supported.

4.4. Contribution

First, this study advances prior work by extending the application of OTC-EHR systems to wearable devices [1,2], thereby broadening the conceptual and technological boundaries of OTC-EHR research. The empirical findings demonstrate that deploying OTC-EHRs on wearable platforms is generally acceptable to users and reveals the potential for seamless, cross-device integration in self-medication contexts. Building upon this contribution, this study also offers a preliminary examination of the relationships among eHealth literacy, user attitudes, and the acceptance of AI-assisted OTC-EHR usage. These results provide early evidence of the nuanced interplay between eHealth literacy and users’ willingness to engage in AI-supported decision-making, underscoring both the opportunities and the complexities of human–AI collaboration in self-medication. Collectively, these findings indicate new research questions, lay a conceptual foundation for future studies, and offer initial implications for the design and development of digital self-medication systems.

4.5. Limitations and Future Study

In this exploratory study, we investigated the conceptual design of AI-assisted wearable OTC-EHRs from users’ perspectives, though this research has certain limitations. First, referring to previous studies, we considered eHealth literacy as the primary independent variable, without accounting for other complex factors such as socioeconomic status, family background, and educational level. More potential influencing factors need to be considered in subsequent larger-scale studies. Secondly, we did not conduct in-depth research on the problems and impacts of AI technology. Considering that transparency regarding AI algorithms and mechanisms is crucial for decision-making, further studies need to focus on the impact of such transparency on users and, for example, how to present AI technology to consumers and patients in a way that they can understand easily, improve transparency, and thus minimize transparency-related decision-making problems. Thirdly, this study did not consider the elderly demographic. Considering the complexity of this demographic in terms of digital technology acceptance and health management, it is necessary to conduct in-depth research on this group separately. Regarding research methodology, future studies may incorporate qualitative approaches, such as user interviews and focus groups, to gain deeper insights into users’ needs and knowledge. Fourth, this study did not engage with the technical design of smart wearable devices. These detailed specifications will be the focus of future investigations. Fifth, this study may have contained potential cultural biases. Future research will incorporate cross-cultural comparisons to examine the impact of different countries and cultural backgrounds on results.

5. Conclusions

This study suggests a tendency toward positive acceptance of AI-assisted wearable OTC-EHR systems, and this acceptance is associated with eHealth literacy. Perceived system helpfulness appears to be associated with users’ subjective attitudes and motivations regarding using AI-assisted wearable OTC-EHRs. With respect to AI intervention, the present study suggests a nuanced relationship: AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance.

Author Contributions

Conceptualization, G.T., Z.X. and S.K.; methodology, G.T., Z.X. and S.K.; software, G.T., Z.X. and S.K.; validation, G.T. and Z.X.; formal analysis, G.T. and Z.X.; investigation, G.T. and Z.X.; data curation, G.T. and Z.X.; writing—original draft preparation, G.T.; writing—review and editing, G.T., Z.X. and S.K.; visualization, G.T. and Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Scientific Research Start-up Funding for Young Teachers at Shenzhen University, Grant Number RC20240301.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Shenzhen University, No.PN-202400107. All participants were informed of the purpose of the study, data processing, privacy, and other relevant information before participation. All respondents completed the survey after providing informed consent.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Acknowledgments

The authors wish to acknowledge all respondents for supporting the survey.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
OTCOver-the-Counter
EHRElectronic Health Record
OTC-EHROver-the-Counter Medication Electronic Health Record
HITHealth Information Technology
FDAFood and Drug Administration

Appendix A

The eHealth literacy scale [54] is shown in Table A1.
Table A1. eHealth literacy scale.
Table A1. eHealth literacy scale.
QuestionsAnswer
I know what health resources are available on the Internet“Strongly Disagree = 1” to “Strongly Agree = 5”
I know where to find helpful health resources on the Internet
I know how to find helpful health resources on the Internet
I know how to use the Internet to answer my questions about health
I know how to use the health information I find on the Internet to help me
I have the skills I need to evaluate the health resources I find on the Internet
I can tell high quality health resources from low quality health resources on the Internet
I feel confident in using information from the Internet to make health decisions

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Figure 1. Conceptual framework for AI-assisted wearable OTC-EHR.
Figure 1. Conceptual framework for AI-assisted wearable OTC-EHR.
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Figure 2. Research hypotheses regarding eHealth literacy, attitudes, and willingness to use the AI-assisted wearable OTC-EHR system.
Figure 2. Research hypotheses regarding eHealth literacy, attitudes, and willingness to use the AI-assisted wearable OTC-EHR system.
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Figure 3. The degree of eHealth literacy and wearable device usage. (a) Usage frequency of wearable devices. (b) Comparison of usage frequency of wearable devices. *** p < 0.001.
Figure 3. The degree of eHealth literacy and wearable device usage. (a) Usage frequency of wearable devices. (b) Comparison of usage frequency of wearable devices. *** p < 0.001.
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Figure 4. eHealth literacy and attitudes toward using wearable-device-based OTC-EHR. (a) Willingness to use wearable devices to record medication health status. (b) Comparison of willingness to use wearable devices to record medication health status. *** p < 0.001.
Figure 4. eHealth literacy and attitudes toward using wearable-device-based OTC-EHR. (a) Willingness to use wearable devices to record medication health status. (b) Comparison of willingness to use wearable devices to record medication health status. *** p < 0.001.
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Figure 5. eHealth literacy and attitudes toward using health data with wearable-device-based OTC-EHRs. (a) Attitudes toward sharing anonymized health data to wearable devices-based OTC-EHRs. (b) Motivation to personalize health management through physical data and OTC medicine usage information. *** p < 0.001.
Figure 5. eHealth literacy and attitudes toward using health data with wearable-device-based OTC-EHRs. (a) Attitudes toward sharing anonymized health data to wearable devices-based OTC-EHRs. (b) Motivation to personalize health management through physical data and OTC medicine usage information. *** p < 0.001.
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Figure 6. Attitude towards AI intervention considering eHealth literacy. (a) Appropriateness of wearable devices for recording and managing medication-related health information. (b) Appropriateness of AI-assisted wearable devices for recording and managing medication-related health information. (c) Appropriateness of conventional and AI-assisted wearable devices for recording and managing medication-related health information. *** p < 0.001; ns: not statistically significant.
Figure 6. Attitude towards AI intervention considering eHealth literacy. (a) Appropriateness of wearable devices for recording and managing medication-related health information. (b) Appropriateness of AI-assisted wearable devices for recording and managing medication-related health information. (c) Appropriateness of conventional and AI-assisted wearable devices for recording and managing medication-related health information. *** p < 0.001; ns: not statistically significant.
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Figure 7. Trust in health management advice from AI-assisted wearable OTC-EHRs. *** p < 0.001.
Figure 7. Trust in health management advice from AI-assisted wearable OTC-EHRs. *** p < 0.001.
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Figure 8. Presentation of eHealth literacy, attitudes, and willingness to use an AI-assisted wearable OTC-EHR system.
Figure 8. Presentation of eHealth literacy, attitudes, and willingness to use an AI-assisted wearable OTC-EHR system.
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Table 1. Questions regarding attitudes toward utilizing wearable OTC-EHR.
Table 1. Questions regarding attitudes toward utilizing wearable OTC-EHR.
(i) Attitude towards using wearable devices to record health status
Question: Are you willing to use the wearable devices to record your health status during medication use?
The responses ranged from “Completely Unwilling = 0” to “Very Willing = 5.”
(ii) Attitude towards sharing health data
Question: If the health information recorded by wearable devices during your medication use is anonymized and protected, are you willing to share this information?
The responses ranged from “Completely Unwilling = 1” to “Very Willing = 5.”
(iii) Motivation to personalize health management through physiological data and OTC medicines usage information
Question: Are you willing to use physiological data recorded by wearable devices, combined with health information during OTC medicines use, to personalize your health management?
The responses ranged from “Completely Unwilling = 1” to “Very Willing = 5.”
(iv) Perceived helpfulness from the wearable device-based OTC-EHR
Question: If you can check anonymized health information shared by other patients when purchasing OTC medicines, do you think this information could help you choose the appropriate medicines?
The responses ranged from “Not Helpful at All = 1” to “Very Helpful = 5.”
Table 2. Questions regarding attitudes toward AI interventions for wearable-device-based OTC-EHR.
Table 2. Questions regarding attitudes toward AI interventions for wearable-device-based OTC-EHR.
(i) Regarding whether wearable devices or Ai-based wearable devices are appropriate for recording and managing medication-related health information
Question: Do you consider wearable devices to be appropriate digital tools for recording and managing health information related to medication use? The responses ranged from “Absolutely Not = 1” to “Absolutely Yes = 5.”
Question: Do you consider wearable devices combined with AI interventions to be an appropriate tool for recording and managing health information related to medication use?
The responses ranged from “Absolutely Not = 1” to “Absolutely Yes = 5.”
(ii) Trust in Health Management Advice from AI-assisted wearable OTC-EHR
Question: Do you trust AI to analyze the health information related to medication use recorded and managed through wearable devices to provide you with medication advice?
The responses ranged from “Absolutely Not = 1” to “Absolutely Yes = 5.”
(iii) Attitude towards approaches to get personalized advice regarding OTC medicines usage and medication decisions
Question: Which approach do you prefer for obtaining personalized OTC medication advice?
Options:
(A) I prefer to review medication information and make decisions on my own.
(B) I prefer to review medication information, consider suggestions provided by AI, and then make my own decisions.
(C) I prefer to review medication information myself and rely on AI to make decisions.
(D) I prefer AI to make all decisions for me.
(E) I prefer to review medication information myself and rely on doctors or pharmacists to make decisions.
(H) I prefer doctors or pharmacists to make all decisions for me.
Table 3. Demographic data of participants.
Table 3. Demographic data of participants.
CharacteristicsRespondents N (%)C-eHealth Scores (Mean ± SD)
Age brackets (years)
18–29135 (36.29%)31.46 ± 5.36
30–39199 (53.50%)31.88 ± 4.45
40–4930 (8.06%)31.07 ± 5.71
Over 508 (2.15%)29.50 ± 5.95
Gender
Female136 (36.56%)31.86 ± 5.47
Male235 (63.17%)31.44 ± 4.60
Others1 (0.27%)37.00 ± 0.00
Occupation
Medical practitioner31 (8.33%)34.10 ± 5.74
Non-medical practitioner341 (91.67%)31.38 ± 4.80
Total372 (100.00%)31.61± 4.93
Note: N = numbers; SD = standard deviation.
Table 4. Effects of perceived helpfulness based on attitudes and motivation regarding wearable-device-based OTC-EHR usage.
Table 4. Effects of perceived helpfulness based on attitudes and motivation regarding wearable-device-based OTC-EHR usage.
EffectsEstimateSE95% CIp
LLUL
Fixed effects
Intercept1.110.240.641.59<0.001
eHealth literacy0.000.01−0.010.020.85
Attitude towards using wearable-device-based OTC-EHRs0.250.060.140.37<0.001
Attitude towards sharing health information to wearable-device-based OTC-EHRs0.250.050.160.34<0.001
Motivation to personalize health management through physiological data0.190.060.070.310.001
Note: N = 372; SE = standard error; CI = confidence interval; LL = lower limit; UL = upper limit.
Table 5. Attitudes towards approaches to receiving personalized advice on medication decisions.
Table 5. Attitudes towards approaches to receiving personalized advice on medication decisions.
Question: Which Approach Do You Prefer to Obtain Personalized OTC Medication Advice?
Respondents % (N)I prefer to review medication information and make decisions on my ownI prefer to review medication information, consider suggestions provided by AI, and then make my own decisionsI prefer to review medication information myself and rely on AI to make decisionsI prefer AI to make all decisions for meI prefer to review medication information myself and rely on doctors or pharmacists to make decisionsI prefer doctors or pharmacists to make all decisions for me
High C-eHealth34 (19.54%)55 (31.61%)14 (8.05%)42 (24.14%)13 (7.47%)16 (9.20%)
Low C-eHealth21 (10.61%)62 (31.31%)5 (2.53%)63 (31.82%)29 (14.65%)18 (9.09%)
Total55 (14.78%)117 (31.45%)19 (5.11%)105 (28.23%)42 (11.29%)34 (9.14%)
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MDPI and ACS Style

Tang, G.; Xu, Z.; Koyama, S. eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions. Systems 2025, 13, 1070. https://doi.org/10.3390/systems13121070

AMA Style

Tang G, Xu Z, Koyama S. eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions. Systems. 2025; 13(12):1070. https://doi.org/10.3390/systems13121070

Chicago/Turabian Style

Tang, Guyue, Zhidiankui Xu, and Shinichi Koyama. 2025. "eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions" Systems 13, no. 12: 1070. https://doi.org/10.3390/systems13121070

APA Style

Tang, G., Xu, Z., & Koyama, S. (2025). eHealth Literacy, Attitudes, and Willingness to Use an Artificial Intelligence-Assisted Wearable OTC-EHR System for Self-Medication: An Empirical Study Exploring AI Interventions. Systems, 13(12), 1070. https://doi.org/10.3390/systems13121070

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