Perceived effectiveness and use of personal biomedical devices for noncommunicable disease self-management among adult university students in Costa Rica: a cross-sectional study

Perceived effectiveness and use of personal biomedical devices for noncommunicable disease self-management among adult university students in Costa Rica: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Perceived effectiveness and use of personal biomedical devices for noncommunicable disease self-management among adult university students in Costa Rica: a cross-sectional study Luis Diego Salazar Sandoval This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8389948/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Personal biomedical devices have become increasingly accessible tools for supporting self-monitoring and self-care in the prevention and self-management of noncommunicable diseases. However, evidence suggests that ownership of these devices does not always translate into sustained use, particularly among young and generally healthy populations. This study aimed to examine the perceived effectiveness and use of personal biomedical devices among adult university students in Costa Rica and to explore behavioral factors influencing their integration into self-care practices. Methods A quantitative, descriptive, cross-sectional study was conducted among adult university students at a private university in Costa Rica between July and August 2025. Data were collected using a structured, self-administered online questionnaire assessing ownership and use of personal biomedical devices, perceived effectiveness, behavioral determinants of technology use, and self-care practices. Descriptive statistics were used to summarize participant characteristics and response distributions. Results A total of 234 students participated, of whom 59.9% reported owning at least one personal biomedical device. Among device owners, most reported rare use, despite generally positive perceptions of device accuracy, usefulness, and ease of use. Trust in device measurements and perceived benefits for health monitoring were high; however, fewer than half of participants considered device use part of their regular self-care routine. Behavioral determinants related to effort expectancy and performance expectancy were favorable, while habit formation indicators were weak. Most participants reported healthy lifestyle behaviors but infrequent monitoring of physiological parameters. Conclusions Although personal biomedical devices were widely perceived as effective and easy to use, their integration into routine self-care among adult university students was limited. These findings highlight a gap between technological acceptance and sustained behavioral adoption, emphasizing the need for strategies that promote habit formation, digital health literacy, and contextual support. Universities and public health initiatives may play a key role in fostering consistent preventive self-monitoring practices among young adults. personal biomedical devices digital health self-care technology acceptance noncommunicable diseases university students preventive health Costa Rica Introduction Noncommunicable chronic diseases (NCDs) such as type 2 diabetes, hypertension, cardiovascular disease, and obesity remain the dominant causes of morbidity and mortality worldwide, accounting for about 75% of global deaths annually ( 1 ). More than 85% of premature deaths from NCDs occur in low- and middle-income countries, underscoring major global health inequities and the need for effective prevention strategies ( 2 ). In Costa Rica, the situation reflects this global pattern: 80.7% of total deaths in 2019 were attributed to NCDs, particularly cardiovascular diseases and cancer ( 3 ). These epidemiological patterns reveal a health system increasingly burdened by chronic conditions that demand continuous, long-term management rather than acute care alone. In response to these challenges, biomedical technologies are being integrated into public health strategies to support the prevention and self-management of chronic diseases. The rise of personal biomedical devices, such as smart glucometers, digital sphygmomanometers, wearable activity trackers, and smart scales, has created new possibilities for individual health monitoring and preventive self-care ( 4 ). These tools allow users to measure physiological parameters in real time, detect early warning signs, and make informed lifestyle adjustments ( 5 ). However, despite these advances, population-level adoption and consistent use remain low due to implementation challenges, limited health literacy, and user skepticism about device accuracy ( 2 , 6 ). Understanding how individuals perceive and use these devices is crucial for designing effective strategies that promote active participation in health self-management. Adult university students represent an ideal population for this study: they are typically technologically literate and comfortable with digital tools but often exhibit irregular health habits due to academic stress and lifestyle factors ( 7 ). At the Latin American University of Science and Technology (ULACIT) in Costa Rica, students have broad access to biomedical devices and educational resources; however, little is known about how effectively they integrate these tools into their daily self-care routines. Studying this population can offer valuable insights into the behavioral and motivational factors that influence the adoption of preventive health practices among young adults. Recent studies have shown that users’ perceptions of effectiveness play a decisive role in whether they continue using biomedical monitoring devices. Individuals tend to maintain consistent use when they trust the data to be accurate, meaningful, and relevant to their health decisions ( 8 ). However, when users see little personal benefit, they often abandon the devices, even when clinical evidence shows the technology's value ( 6 ). This suggests that usability and technical reliability alone are insufficient; the user’s interpretation of the device’s usefulness is equally important for sustained engagement. To explain these behavioral mechanisms, researchers often draw on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The framework identifies factors such as performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions as key determinants of technology acceptance and ongoing use ( 9 – 11 ). Considering these factors helps clarify the psychological and social processes that influence how individuals adopt and maintain digital-health behaviors. Combining biomedical engineering with behavioral science provides a practical framework for analyzing user interaction with health technology. Device precision and reliability alone cannot guarantee adoption; users must find the tools intuitive, relevant, and aligned with their daily routines ( 11 , 12 ). Research shows that training, feedback, and sense of control substantially influence adherence to digital monitoring programs ( 5 ). For example, self-measured blood pressure monitoring is most effective when paired with health education and professional guidance rather than isolated self-measurement ( 5 ). Similarly, in diabetes management, continuous glucose monitoring improves glycemic control primarily among individuals who receive targeted training and ongoing support ( 13 ). These findings suggest that behavioral reinforcement and contextual support are as important as technological sophistication in determining public-health impact. At the population level, incorporating personal biomedical technologies into everyday routines could lead to measurable improvements in the prevention and early detection of NCDs. By allowing individuals to identify and respond to early physiological changes, such tools have the potential to reduce hospitalizations, improve quality of life, and lower healthcare costs ( 12 ). However, adoption varies widely across regions and socioeconomic groups. In Latin America, limited digital literacy, unequal internet access, and fragmented implementation of digital-health strategies hinder the equitable deployment of these innovations ( 14 ). Even in relatively advanced health systems like Costa Rica’s, there are gaps between technological availability and user engagement. Institutions of higher education can play a pivotal role in addressing these disparities by promoting digital literacy and health self-management among students, helping to normalize preventive health behaviors early in adulthood ( 3 , 14 ). The World Health Organization stresses that self-care and digital health are integral to achieving universal health coverage and long-term sustainability ( 13 ). Its 2022 guideline on self-care interventions highlights the importance of technologies that not only collect data but also support users in interpreting and applying that information responsibly. Similarly, the Pan American Health Organization (PAHO) notes that digital transformation in the Americas requires not only infrastructure but also behavioral adoption and institutional commitment ( 14 ). Both organizations advocate for research that explores user perceptions, contextual barriers, and determinants of adherence, precisely the objectives guiding this study. This research, therefore, examines the perceived effectiveness and use of personal biomedical devices, specifically digital scales, digital sphygmomanometers, and glucometers, in the self-management of noncommunicable diseases such as hypertension, diabetes, and obesity among adult university students at ULACIT. It applies a quantitative, descriptive, cross-sectional design with validated questionnaires administered to 237 participants, of whom 234 provided informed consent and completed the survey. The study pursues three main objectives: ( 1 ) to identify the types of personal biomedical devices commonly used by students, ( 2 ) to analyze the frequency and context of their use, and ( 3 ) to evaluate perceived effectiveness as a factor influencing adherence to self-care practices. In addition, the study incorporates the UTAUT2 theoretical framework to assess behavioral determinants such as effort expectancy, hedonic motivation, and facilitating conditions that may influence continuous use ( 9 – 11 ). The study applies both behavioral and biomedical approaches to explore how young adults use health technologies for preventive self-care. The results are expected to inform evidence-based strategies for promoting digital self-care and technological literacy within higher-education settings. These findings also have wider implications for public-health policy, as early adoption of self-monitoring practices can lead to long-term benefits and help reduce the burden of noncommunicable diseases (NCDs) at the national level ( 7 , 12 , 13 ). This approach is consistent with global health priorities outlined in Sustainable Development Goal 3, which emphasizes the prevention and control of NCDs and the advancement of health promotion through inclusive innovation ( 15 ). Ultimately, this study aims to demonstrate that when biomedical technology is perceived as effective, accessible, and meaningful, it can serve not merely as a monitoring tool but as a catalyst for behavioral change. Such change represents a crucial step toward building more sustainable and equitable healthcare systems worldwide. Methods Study Aim, Design, and Setting This study examined the perceived effectiveness and use of personal biomedical devices in the self-management of noncommunicable diseases (NCDs) among adult university students in Costa Rica. A quantitative, descriptive, cross-sectional design was applied to understand behavioral and technological factors associated with self-care. The research was conducted at the Latin American University of Science and Technology (ULACIT) in San José, Costa Rica, between July and August 2025, following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies ( 16 ). ULACIT was chosen for its diverse adult student population across health, engineering, and business disciplines, offering a heterogeneous and well-educated sample for assessing digital health behaviors. Justification of the Methodological Approach This quantitative, descriptive, cross-sectional design is particularly suitable for addressing the research question because it enables the systematic measurement of students’ behaviors, perceptions, and use patterns of personal biomedical devices within a large and diverse university population. This approach aligns directly with the study objectives, as it allows the identification of which devices are used, their frequency and context of use, and the extent to which students perceive them as effective. The structured response formats and standardized Likert-scale items further support the consistent assessment of constructs such as perceived effectiveness and behavioral determinants derived from the UTAUT2 model. By providing a detailed snapshot of current self-care practices and digital health engagement among adult students, this design offers a methodologically robust and efficient framework for an exploratory investigation of this nature, even though it does not allow inference of causal relationships. Participants and Sampling The study population included adult students enrolled in undergraduate and graduate programs at ULACIT during the second academic term of 2025. Inclusion criteria required participants to be 18 years or older and actively enrolled at the university. No exclusion criteria were applied based on prior experience with biomedical devices or health status. A convenience sampling strategy yielded 234 valid responses, after excluding three individuals who declined the informed consent and were automatically exited from the form. Sociodemographic variables included age, gender, and academic program, and participants indicated whether they had experience with hypertension, diabetes, or overweight, as these represent prevalent NCDs in Costa Rica ( 3 ). Given the exploratory and descriptive nature of the study, a formal sample size calculation was not performed, as the primary goal was to describe rather than infer population-level relationships. While this cross-sectional design enables a detailed snapshot of current behaviors, it does not allow inference of causal relationships. Variables and Instruments Four primary variables were analyzed: Use of personal biomedical devices — specifically digital scales, sphygmomanometers, and glucometers, focusing on usage frequency and context. Perceived effectiveness — participants’ belief in the accuracy, usefulness, and impact of these devices on health monitoring and self-management. Behavioral determinants — derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, encompassing effort expectancy, hedonic motivation, social influence , and facilitating conditions ( 9 – 11 ). Self-care practices — behaviors related to monitoring, prevention, and adherence to healthy-lifestyle recommendations. Data were collected using a structured, self-administered online questionnaire developed in Google Forms (Google LLC, Mountain View, CA, USA). The instrument consisted of 36 items divided into five sections: [1] sociodemographic information, [2] use of personal biomedical devices, [3] perceived effectiveness, [4] UTAUT2 constructs, and [5] self-care practices. Response formats varied by variable: sociodemographic and device-use questions were categorical or multiple-choice, while the scales assessing perceived effectiveness, acceptance constructs, and self-care used a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The questionnaire was developed specifically for this study based on relevant literature and the UTAUT2 framework and has not been previously published elsewhere. An English-language version of the full questionnaire is provided as Supplementary File 1. Before accessing the questionnaire, participants were required to read and accept a digital informed-consent statement. Those who selected “I do not consent” were automatically exited from the form. Instrument Validation and Reliability Instrument validation followed two complementary procedures: content validation and internal reliability testing. Content validity was assessed by at least two professionals with expertise in engineering and research methodology, who evaluated the questionnaire’s clarity, relevance, and congruence with its variables. Revisions were made based on expert feedback to refine wording and improve precision. Internal reliability was examined using Cronbach’s alpha for the main scales of the questionnaire, including perceived effectiveness, technology acceptance, and self-care practices. The analysis indicated acceptable to high internal consistency across all constructs, consistent with commonly accepted reliability standards in behavioral research ( 17 , 18 ). Data Collection and Statistical Analysis The validated questionnaire was distributed between July and August 2025 via Google Forms. Responses were automatically stored in the platform and exported to Microsoft Excel for Windows, Version 2021 (Microsoft Corp., Redmond, WA, USA) ( 19 ) for cleaning and analysis. Descriptive statistics (frequencies, percentages, and means) were used to summarize participant characteristics and variable distributions. Missing data were coded as “Sin responder” (no response). Given the descriptive and cross-sectional nature of the research, no inferential statistical tests or power analyses were performed, in accordance with BMC Public Health recommendations for exploratory observational studies. Ethical Considerations The study protocol was reviewed and approved by the ULACIT Research Ethics Committee prior to data collection. All procedures complied with the ethical principles outlined in the World Medical Association’s Declaration of Helsinki (2024 revision) ( 20 ). Participants were informed of the objectives, voluntary nature, and confidentiality of the study through a detailed consent statement embedded in the questionnaire. Those who declined consent were unable to proceed to the survey questions. The study posed minimal risk, as all data were anonymous, non-identifiable, and self-reported. No sensitive or clinical information was collected beyond general self-reports of chronic conditions. All data were stored securely in encrypted devices, with access restricted to the research team. ChatGPT (OpenAI, 2025) was used to assist in refining the wording and structure of sections of this manuscript. All content was reviewed, verified, and approved by the author, who takes full responsibility for the final text. Results Participant flow and response structure A total of 237 individuals accessed the questionnaire and responded to the informed-consent item. Three declined consent and were automatically redirected out of the survey, providing no additional data. The final analytic sample therefore consisted of 234 participants who accepted the consent statement and completed the full questionnaire. Because the instrument employed conditional branching, only participants who reported owning a personal biomedical device were shown the device-specific sections. Consequently, denominators vary across subsections: sociodemographic characteristics and self-care practices were assessed using the full analytic sample (N = 234), while device use, perceived effectiveness, and UTAUT2 constructs were analyzed exclusively among respondents who owned a device (n = 142). Scale reliability Internal consistency across the main scales was strong. Cronbach’s alpha analysis indicated acceptable to high reliability for perceived effectiveness, technology acceptance (UTAUT2), and self-care practices, supporting the internal consistency of the measurement instrument. Item non-response was minimal in sociodemographic items and increased as expected in sections conditioned on device ownership, reflecting the questionnaire structure rather than respondent dropout. Participant characteristics The analytic sample (N = 234) was predominantly young, with a mean age of 20.7 years (range 18–59) and nearly all respondents (94.8%) between 18 and 25 years. Most participants identified as female (59.5%), followed by male (38.4%), while less than one percent selected another gender identity. Academic representation was diverse. Biomedical Engineering accounted for the largest share (33.8%), followed by Industrial Engineering, Psychology, Computer Engineering, and Graphic Design and Visual Communication, each contributing around 8–10% of the sample. Smaller proportions were enrolled in Chemical Engineering, Nutrition, Business Administration, and other disciplines, resulting in a heterogeneous sample with a strong concentration in engineering and health-related fields. Health status reports indicated that 90.7% of participants did not have a diagnosed noncommunicable disease. Among those reporting a diagnosis, obesity (7.2%) was the most common, and only a single respondent (0.4%) indicated both obesity and hypertension (Table 1 ). These findings suggest that the sample largely consisted of healthy young adults, consistent with typical university population profiles. Table 1 Participant characteristics (N = 234) Variable Category n (%) Age Mean (range) 20.7 (18–59) Gender Female 139 (59.5) Male 89 (38.0) Other 2 (0.8) No response 4 (1.7) Academic program Biomedical Engineering 79 (33.8) Industrial Engineering 23 (9.7) Psychology 19 (8.0) Computer Engineering 19 (8.0) Graphic Design & Visual Communication 19 (8.0) Chemical Engineering 16 (6.8) Nutrition 8 (3.4) Business Administration 6 (2.5) Other 45 (19.2) NCD diagnosis None 212 (90.7) Obesity 17 (7.2) Hypertension + Obesity 1 (0.4) Ownership and use of personal biomedical devices Among the 234 participants, 59.9% (n = 142) reported owning at least one personal biomedical device, while 38.8% reported not owning any device. Among owners, digital or smart scales were the most frequently reported devices (62.0%), followed by digital blood pressure monitors (29.6%) and glucometers (23.9%). Notably, some respondents who initially indicated device ownership later selected “none” when identifying the type of device, suggesting inconsistencies in how respondents classified certain tools. Patterns of use indicated limited integration into daily routines. Most device owners (78.9%) reported using their device rarely, while 19.0% used them occasionally and only 2.1% used them frequently (Table 2 ). Duration of use showed a similar pattern: nearly 60% were not using any device regularly at the time of the survey, while the remaining respondents reported periods of regular use ranging from less than one month to more than six months. Students generally felt comfortable operating their devices; more than half rated their familiarity as medium to very high, although 15.5% rated it low or very low. Despite this familiarity, only 19.0% reported using a connected mobile application or digital platform, whereas most (71.1%) did not and 9.9% were unsure, indicating limited integration with digital health platforms. Table 2 Ownership and use of personal biomedical devices (n = 142) Variable Category n (%) Device ownership (full sample) Yes 142 (59.9) No 91 (38.8) No response 1 (0.4) Device type Digital/Smart scale 88 (62.0) Digital blood pressure monitor 42 (29.6) Glucometer 34 (23.9) “None” after indicating ownership 31 (21.8) Frequency of use Rarely (≤ 1/month) 112 (78.9) Occasionally (1–3/week) 27 (19.0) Frequently (≥ 4/week) 3 (2.1) Duration of regular use Not currently using 85 (59.9) 6 months 34 (23.9) Familiarity Low/Very low 22 (15.5) Medium 45 (31.7) High/Very high 75 (52.8) Use of mobile app Yes 27 (19.0) No 101 (71.1) Not sure 14 (9.9) Perceived effectiveness of personal biomedical devices Among the 142 device owners, perceptions of device usefulness were generally positive. Overall, 66.2% agreed or strongly agreed that their device helped them maintain better control of their health, while only a small proportion (8.5%) disagreed. Confidence in measurement accuracy was similarly strong, with 72.5% expressing trust in the readings produced by their device (Table 3 ). Table 3 Perceived effectiveness and technology acceptance (UTAUT2) among device owners (n = 142) Construct / Item Disagree n (%) Neutral n (%) Agree n (%) Perceived effectiveness Device helps control my health 12 (8.5) 36 (25.4) 94 (66.2) I trust the accuracy of the device 8 (5.6) 31 (21.8) 103 (72.5) Device is useful for health decisions 11 (7.7) 30 (21.1) 101 (71.1) Device motivates healthier habits 12 (8.5) 29 (20.4) 101 (71.1) Device is part of my self-care routine 52 (36.6) 34 (23.9) 56 (39.4) Device improves my quality of life 17 (11.9) 36 (25.4) 89 (62.7) Performance expectancy Device improves my health monitoring 12 (8.5) 33 (23.2) 97 (68.3) Device makes health management easier 13 (9.2) 31 (21.8) 98 (69.0) Effort expectancy Learning to use the device is easy 7 (4.9) 27 (19.0) 108 (76.1) Using the device requires little effort 5 (3.5) 24 (16.9) 113 (79.6) Facilitating conditions I have the resources to use the device 6 (4.2) 20 (14.1) 116 (81.7) I know whom to contact if I have issues 39 (27.5) 28 (19.7) 75 (52.8) Social influence People important to me encourage use 15 (10.6) 27 (19.0) 100 (70.4) Others’ use motivates me 20 (14.1) 27 (19.0) 85 (59.9) Hedonic motivation I enjoy using the device 23 (16.2) 36 (25.4) 83 (58.4) Using the device is worthwhile 24 (16.9) 32 (22.5) 86 (60.6) Habit Using the device is part of my routine 71 (50.0) 25 (17.6) 46 (32.4) I use the device almost automatically 52 (36.6) 33 (23.2) 57 (40.1) Trust and anxiety I trust the data the device provides 13 (9.2) 38 (26.8) 91 (64.1) I feel nervous or insecure using it 74 (52.1) 27 (19.0) 40 (28.2) Note: Likert responses were collapsed into three categories (Disagree = 1–2; Neutral = 3; Agree = 4–5). Students also perceived these devices as supportive tools for decision-making and lifestyle regulation. A majority (71.1%) believed their device helped them make informed health decisions and motivated them to adopt healthier habits. Nevertheless, everyday integration of device use remained low. Only 39.4% viewed device use as part of their self-care routine, while 36.6% reported that it was not. Even so, a clear majority (62.7%) agreed that using the device had a positive effect on their quality of life, indicating that students appreciate the benefits of these technologies even if they do not consistently incorporate them into daily habits. Technology acceptance (UTAUT2 constructs) Technology-acceptance findings reflected overall favorable attitudes. Performance expectancy was high, with 68.3% of participants agreeing that their device improved their ability to monitor their health and 69.0% noting that it made health management easier. Effort expectancy was even more positive: 76.1% indicated that learning to use the device was easy, and 79.6% felt that using it required little effort. Regarding facilitating conditions, most students (81.7%) felt they had the resources to use their device effectively, though only 52.8% reported knowing whom to contact for support if they encountered technical issues. Social influence trends showed that 70.4% perceived support from close contacts, and nearly 60% reported feeling more motivated when others used similar devices. Motivational responses were also strong. Nearly half of all device owners agreed that they enjoyed using their device or found it rewarding. However, habit-formation indicators were weaker: only 32.4% reported that device use was part of their daily routine, and 40.1% said they used it almost automatically, with substantial percentages disagreeing on both items. Trust remained moderately high (64.1%), while anxiety levels were mixed; about one-third (32.4%) reported feeling nervous or insecure using such technology, while nearly half (48.6%) did not (Table 3 ). Self-care practices Self-care behaviors were assessed among all 234 participants. Overall, nearly half of respondents (50.0%) reported rarely or never measuring physiological parameters such as blood pressure, weight, or glucose, while 26.1% did so occasionally and 23.9% did so frequently or very frequently. In contrast, healthier behaviors such as maintaining a balanced diet and engaging in regular physical activity were more common. 61.1% frequently or always followed a healthy diet, and 59.4% engaged in physical activity at least three times per week. However, roughly one-quarter reported rarely or never meeting these recommendations. Students demonstrated variable adherence regarding the avoidance of excessive sugar, fat, or salt. While 45.7% frequently or always avoided these substances, 24.9% rarely or never did so. Among the small number of students with chronic conditions, 65.4% reported frequently or always following medical recommendations. Finally, most participants (67.5%) agreed that they felt responsible for actively monitoring their health, suggesting strong perceived self-responsibility even if monitoring behaviors did not always align with this sense of obligation (Table 4 ). Table 4 Self-care practices (N = 234) Self-care item Rarely/Never n (%) Sometimes n (%) Often/Always n (%) Measures BP, weight, or glucose 117 (50.0) 61 (26.1) 56 (23.9) Maintains a healthy diet 25 (10.7) 66 (28.2) 143 (61.1) Physical activity ≥ 3×/week 65 (27.8) 30 (12.8) 139 (59.4) Avoids excessive sugar, fat, or salt 59 (24.9) 68 (29.1) 107 (45.7) Follows medical recommendations 30 (12.8) 51 (21.8) 153 (65.4) Feels responsible for monitoring own health 22 (9.4) 54 (23.1) 158 (67.5) Note: Likert responses were collapsed into three categories (Rarely/Never = 1–2; Sometimes = 3; Often/Always = 4–5). Discussion This study examined the perceived effectiveness and use of personal biomedical devices among adult university students in Costa Rica, offering insight into how young adults incorporate digital health tools into their self-care practices. The findings indicate a clear gap between favorable perceptions of device usefulness and limited behavioral adoption. Although nearly 60% of participants owned at least one device, the majority reported using them rarely, and only a small fraction used them regularly. Despite this low frequency, most device owners expressed trust in measurement accuracy and believed their devices helped them monitor their health, make informed decisions, and adopt healthier habits. These results suggest that the factors limiting device use are not related to perceived value or usability but rather to behavioral patterns, contextual conditions, and the absence of established routines for physiological self-monitoring. The discrepancy between positive perceptions and limited use mirrors trends observed in digital health research. Several reviews have shown that users often report satisfaction with biomedical devices yet fail to maintain consistent use over time, especially when the perceived urgency for monitoring is low ( 2 , 6 , 7 ). The predominantly healthy profile of the sample (over 90% reported no diagnosed noncommunicable disease) may partly explain this pattern, as individuals without immediate clinical needs are less likely to incorporate monitoring into daily routines. Similar observations have been made in interventions targeting blood pressure and glucose self-monitoring, where adherence improves primarily among individuals with clear medical indications or structured follow-up programs ( 5 , 12 ). In this population of mostly young and healthy students, episodic or reactive use may therefore be more common than sustained preventive monitoring. The behavioral findings are also consistent with the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). While performance expectancy, effort expectancy, and social influence were generally high, indicators of habit formation were notably weaker. Only one-third of participants reported that device use had become routine, despite reporting that devices were easy to learn and operate. Prior research applying UTAUT2 in health contexts has shown that habit is a central determinant of continuous technology use, often stronger than initial motivation or perceived usefulness ( 8 – 10 ). Students in this study may therefore be “accepting” the technology at a cognitive level but not translating that acceptance into regular preventive behavior. Limited use of companion mobile applications (only 19% reported using them) may further weaken habit formation, as digital platforms often provide feedback, alerts, and trend visualization that reinforce regular engagement ( 4 , 12 , 13 ). Alternative explanations should also be considered. Some participants reported device ownership but later selected “none” when specifying device type, suggesting possible misclassification or uncertainty about what constitutes a biomedical device. This may reflect lower digital health literacy among some students, even within a technologically oriented population. Social desirability bias may also have influenced perceptions of usefulness, especially among students in health or engineering programs who may feel pressure to endorse positive attitudes toward technology. Additionally, devices such as scales and sphygmomanometers are often used situationally rather than habitually; students might find value in having access to a device when needed without feeling the necessity for frequent use. These interpretations highlight the importance of considering contextual and psychological factors beyond device attributes alone. The implications of these findings extend to public health, biomedical engineering, and higher-education settings. At a public-health level, young adults represent a critical population for early adoption of preventive practices. Although students in this study infrequently measured physiological parameters, they demonstrated strong feelings of personal responsibility for monitoring their health. Harnessing this sense of responsibility through structured interventions (such as digital-health education, goal-setting programs, or integration of self-monitoring into university wellness initiatives) could promote more consistent engagement. Such efforts align with global recommendations emphasizing the role of digital self-care in the early detection and prevention of noncommunicable diseases ( 13 , 14 ). From a biomedical engineering perspective, the findings suggest that device usability and accuracy are not major barriers; instead, greater emphasis may be needed on ecosystem integration, user engagement strategies, and passive monitoring technologies that reduce the need for deliberate action. Wearable devices that automatically track physiological parameters have shown promise in improving adherence and long-term engagement ( 7 , 11 ), indicating that future innovations should prioritize seamless integration into daily life. Furthermore, improving app-based features (such as personalized insights, automatic alerts, or gamified health challenges) could enhance motivation and strengthen behavioral routines. In higher-education environments, universities can play a pivotal role in shaping health behaviors. Given that most students reported healthy dietary and physical-activity habits yet rarely measured physiological parameters, academic institutions may need to incorporate preventive monitoring into broader wellness programs. Educational sessions on interpreting personal health data, practical workshops on device use, and peer-supported health challenges could reinforce the value of routine monitoring. Such initiatives may be particularly impactful in contexts like Costa Rica, where national strategies for NCD prevention emphasize the importance of self-care and digital health literacy ( 3 , 14 ). This study enriches the existing literature by providing context-specific evidence on the gap between perceived effectiveness and actual use of personal biomedical devices among young adults in a Latin American university setting. While previous studies have primarily focused on clinical populations or populations in high-income countries, where device use is driven by diagnosed conditions or structured follow-up programs ( 2 , 5 – 7 ), the present findings extend current knowledge by demonstrating that positive perceptions, high usability, and technological acceptance do not necessarily translate into habitual self-monitoring behaviors in predominantly healthy student populations. By integrating behavioral constructs from the UTAUT2 framework, this study also adds to prior research by highlighting the central role of habit formation and contextual factors in shaping sustained engagement with digital health technologies ( 8 – 10 ). Several limitations should be acknowledged. The cross-sectional design precludes assessment of causal relationships or temporal changes in device use. The convenience sampling limits generalizability, and the analytic subsample for device-specific variables was restricted to respondents who reported ownership. Self-reported data may be subject to recall or desirability bias, and the study did not include objective usage metrics such as app logs or device-record histories. Despite these limitations, the study offers valuable baseline data and highlights areas for targeted intervention and further investigation. Future research should incorporate longitudinal designs to examine how device use evolves over time and whether perceived effectiveness predicts sustained engagement. Mixed-methods research could provide richer insights into motivational, emotional, and contextual factors influencing device adoption. Studies incorporating objective usage data or evaluating behavioral interventions (such as app-based nudges, educational programs, or wearable-device integration) may help identify strategies that effectively promote routine self-monitoring in young adult populations. Expanding research to individuals with diagnosed chronic conditions could further illuminate differences in adoption patterns and health outcomes. Conclusions This study demonstrates that while adult university students in Costa Rica generally view personal biomedical devices as useful, accurate, and easy to operate, these positive perceptions do not consistently translate into routine self-monitoring behaviors. Device ownership was relatively high, yet actual use was infrequent and rarely integrated into daily self-care practices. These findings highlight a critical gap between technological acceptance and sustained behavioral adoption, underscoring that usability alone is insufficient to promote regular engagement with digital health tools among young adults. The relevance of these results lies in the growing importance of preventive strategies for noncommunicable diseases. Young adulthood represents a formative period for establishing lifelong health behaviors, and strengthening self-monitoring habits could contribute meaningfully to early detection and long-term risk reduction. The study also emphasizes the need for holistic approaches that combine accessible technology with behavioral reinforcement, digital-health literacy, and supportive environments, particularly within higher-education institutions. By clarifying the factors that shape engagement with personal biomedical devices, this research provides evidence that can inform the development of targeted interventions, public-health initiatives, and user-centered innovations aimed at improving preventive self-care in technologically literate populations. Abbreviations NCDs: noncommunicable diseases; UTAUT2: Unified Theory of Acceptance and Use of Technology 2; ULACIT: Latin American University of Science and Technology; WHO: World Health Organization; PAHO: Pan American Health Organization; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology. Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the ULACIT Research Ethics Committee prior to data collection. All procedures complied with the ethical principles outlined in the World Medical Association’s Declaration of Helsinki (2024 revision). Participation was voluntary, and informed consent was obtained electronically from all participants before completion of the questionnaire. Consent for publication Not applicable. No individual-level or identifiable data are included in this manuscript. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to ethical and privacy considerations but are available from the corresponding author on reasonable request, subject to institutional approval. Competing interests The author declares that there are no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions The author was responsible for the study conception and design, data collection, data analysis, interpretation of results, and drafting and revision of the manuscript. The author read and approved the final manuscript. Acknowledgements The author would like to thank the participants for their time and willingness to contribute to this study. Authors’ information Luis Diego Salazar Sandoval is a medical doctor and a biomedical engineering student at the Latin American University of Science and Technology (ULACIT), San José, Costa Rica. References World Health Organization. Noncommunicable diseases: key facts [Internet]. Geneva: World Health Organization. 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases Pong C, Tseng RMW, Tham YC, Lum E. Current implementation of digital health in chronic disease management: scoping review. J Med Internet Res. 2024;26:e53576. 10.2196/53576 . Ministerio de Salud de Costa Rica. Plan de Acción 2022–2025 de la Estrategia Nacional de Abordaje Integral de las Enfermedades No Transmisibles y Obesidad 2022–2030. San José (CR): Ministerio de Salud; 2022. Jafleh AI, Al-Hassan M, Haddad R, Alsalem M, Khasawneh M. The role of wearable devices in chronic disease monitoring and patient care: a comprehensive review. Sens (Basel). 2024;24(3):1254. 10.3390/s24031254 . Uhlig K, Patel K, Ip S, Kitsios G, Balk EM. Self-measured blood pressure monitoring in the management of hypertension: a systematic review and meta-analysis. Ann Intern Med. 2013;159(3):185–94. 10.7326/0003-4819-159-3-201308060-00008 . Mattison ML, Ratanawongsa N, Hsue PY. The influence of wearables on health care outcomes in chronic disease: systematic review. J Med Internet Res. 2022;24(10):e38491. 10.2196/38491 . Gagnon MP, Desmartis M, Lepage-Simard J, Grenier S. Wearable devices for supporting chronic disease self-management: scoping review. BMC Public Health. 2024;24(1):212. 10.1186/s12889-024-17623-1 . AlQudah AA, Al-Emran M, Shaalan K. Technology acceptance in healthcare: a systematic review. J Biomed Inf. 2021;118:103795. 10.1016/j.jbi.2021.103795 . Tamilmani K, Rana NP, Wamba SF, Dwivedi YK. The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): a systematic literature review and theory evaluation. Int J Inf Manag. 2021;57:102269. 10.1016/j.ijinfomgt.2020.102269 . Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012;36(1):157–78. Scholte R, van der Meer A, Jansen M. Advancements in noninvasive wearable technology for heart failure management: a scoping review. Health Technol. 2024;14(2):115–28. 10.1007/s12553-024-00789-4 . Kerr D, Karamanos B, Lomas J. Digital interventions for self-management of type 2 diabetes mellitus: systematic review and meta-analysis. JMIR Diabetes. 2024;9(1):e54211. 10.2196/54211 . World Health Organization. WHO guideline on self-care interventions for health and well-being. Geneva: World Health Organization; 2022. Pan American Health Organization. Inclusive digital health: policy overview [Internet]. Washington (DC): Pan American Health Organization. 2023. Available from: https://iris.paho.org/handle/10665.2/58409 Sachs JD, Lafortune G, Fuller G. Sustainable Development Report 2024: the SDGs and the UN Summit of the Future. Paris: Sustainable Development Solutions Network; 2024. 10.25546/108572 . von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. PLoS Med. 2007;4(10):e296. 10.1371/journal.pmed.0040296 . Trizano-Hermosilla I, Alvarado JM. Best alternatives to Cronbach’s alpha reliability index: theory and user’s guide. Front Psychol. 2021;12:631527. 10.3389/fpsyg.2021.631527 . Taber KS. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Res Sci Educ. 2018;48(6):1273–96. 10.1007/s11165-016-9602-2 . Microsoft Corporation. Microsoft Excel for Windows , version 2021. Redmond (WA): Microsoft Corporation; 2021. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human participants. JAMA. 2024;332(15):1532–44. 10.1001/jama.2024.21972 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1Questionnaire.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Editor invited by journal 24 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 23 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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More than 85% of premature deaths from NCDs occur in low- and middle-income countries, underscoring major global health inequities and the need for effective prevention strategies (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Costa Rica, the situation reflects this global pattern: 80.7% of total deaths in 2019 were attributed to NCDs, particularly cardiovascular diseases and cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These epidemiological patterns reveal a health system increasingly burdened by chronic conditions that demand continuous, long-term management rather than acute care alone.\u003c/p\u003e \u003cp\u003eIn response to these challenges, biomedical technologies are being integrated into public health strategies to support the prevention and self-management of chronic diseases. The rise of personal biomedical devices, such as smart glucometers, digital sphygmomanometers, wearable activity trackers, and smart scales, has created new possibilities for individual health monitoring and preventive self-care (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These tools allow users to measure physiological parameters in real time, detect early warning signs, and make informed lifestyle adjustments (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, despite these advances, population-level adoption and consistent use remain low due to implementation challenges, limited health literacy, and user skepticism about device accuracy (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding how individuals perceive and use these devices is crucial for designing effective strategies that promote active participation in health self-management. Adult university students represent an ideal population for this study: they are typically technologically literate and comfortable with digital tools but often exhibit irregular health habits due to academic stress and lifestyle factors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). At the Latin American University of Science and Technology (ULACIT) in Costa Rica, students have broad access to biomedical devices and educational resources; however, little is known about how effectively they integrate these tools into their daily self-care routines. Studying this population can offer valuable insights into the behavioral and motivational factors that influence the adoption of preventive health practices among young adults.\u003c/p\u003e \u003cp\u003eRecent studies have shown that users\u0026rsquo; perceptions of effectiveness play a decisive role in whether they continue using biomedical monitoring devices. Individuals tend to maintain consistent use when they trust the data to be accurate, meaningful, and relevant to their health decisions (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, when users see little personal benefit, they often abandon the devices, even when clinical evidence shows the technology's value (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This suggests that usability and technical reliability alone are insufficient; the user\u0026rsquo;s interpretation of the device\u0026rsquo;s usefulness is equally important for sustained engagement. To explain these behavioral mechanisms, researchers often draw on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The framework identifies factors such as performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions as key determinants of technology acceptance and ongoing use (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Considering these factors helps clarify the psychological and social processes that influence how individuals adopt and maintain digital-health behaviors.\u003c/p\u003e \u003cp\u003eCombining biomedical engineering with behavioral science provides a practical framework for analyzing user interaction with health technology. Device precision and reliability alone cannot guarantee adoption; users must find the tools intuitive, relevant, and aligned with their daily routines (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Research shows that training, feedback, and sense of control substantially influence adherence to digital monitoring programs (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For example, self-measured blood pressure monitoring is most effective when paired with health education and professional guidance rather than isolated self-measurement (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, in diabetes management, continuous glucose monitoring improves glycemic control primarily among individuals who receive targeted training and ongoing support (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These findings suggest that behavioral reinforcement and contextual support are as important as technological sophistication in determining public-health impact.\u003c/p\u003e \u003cp\u003eAt the population level, incorporating personal biomedical technologies into everyday routines could lead to measurable improvements in the prevention and early detection of NCDs. By allowing individuals to identify and respond to early physiological changes, such tools have the potential to reduce hospitalizations, improve quality of life, and lower healthcare costs (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, adoption varies widely across regions and socioeconomic groups. In Latin America, limited digital literacy, unequal internet access, and fragmented implementation of digital-health strategies hinder the equitable deployment of these innovations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Even in relatively advanced health systems like Costa Rica\u0026rsquo;s, there are gaps between technological availability and user engagement. Institutions of higher education can play a pivotal role in addressing these disparities by promoting digital literacy and health self-management among students, helping to normalize preventive health behaviors early in adulthood (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe World Health Organization stresses that self-care and digital health are integral to achieving universal health coverage and long-term sustainability (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Its 2022 guideline on self-care interventions highlights the importance of technologies that not only collect data but also support users in interpreting and applying that information responsibly. Similarly, the Pan American Health Organization (PAHO) notes that digital transformation in the Americas requires not only infrastructure but also behavioral adoption and institutional commitment (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Both organizations advocate for research that explores user perceptions, contextual barriers, and determinants of adherence, precisely the objectives guiding this study.\u003c/p\u003e \u003cp\u003eThis research, therefore, examines the perceived effectiveness and use of personal biomedical devices, specifically digital scales, digital sphygmomanometers, and glucometers, in the self-management of noncommunicable diseases such as hypertension, diabetes, and obesity among adult university students at ULACIT. It applies a quantitative, descriptive, cross-sectional design with validated questionnaires administered to 237 participants, of whom 234 provided informed consent and completed the survey. The study pursues three main objectives: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to identify the types of personal biomedical devices commonly used by students, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to analyze the frequency and context of their use, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to evaluate perceived effectiveness as a factor influencing adherence to self-care practices. In addition, the study incorporates the UTAUT2 theoretical framework to assess behavioral determinants such as effort expectancy, hedonic motivation, and facilitating conditions that may influence continuous use (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The study applies both behavioral and biomedical approaches to explore how young adults use health technologies for preventive self-care.\u003c/p\u003e \u003cp\u003eThe results are expected to inform evidence-based strategies for promoting digital self-care and technological literacy within higher-education settings. These findings also have wider implications for public-health policy, as early adoption of self-monitoring practices can lead to long-term benefits and help reduce the burden of noncommunicable diseases (NCDs) at the national level (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This approach is consistent with global health priorities outlined in Sustainable Development Goal 3, which emphasizes the prevention and control of NCDs and the advancement of health promotion through inclusive innovation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Ultimately, this study aims to demonstrate that when biomedical technology is perceived as effective, accessible, and meaningful, it can serve not merely as a monitoring tool but as a catalyst for behavioral change. Such change represents a crucial step toward building more sustainable and equitable healthcare systems worldwide.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Aim, Design, and Setting\u003c/h2\u003e \u003cp\u003eThis study examined the perceived effectiveness and use of personal biomedical devices in the self-management of noncommunicable diseases (NCDs) among adult university students in Costa Rica. A quantitative, descriptive, cross-sectional design was applied to understand behavioral and technological factors associated with self-care. The research was conducted at the Latin American University of Science and Technology (ULACIT) in San Jos\u0026eacute;, Costa Rica, between July and August 2025, following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cross-sectional studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). ULACIT was chosen for its diverse adult student population across health, engineering, and business disciplines, offering a heterogeneous and well-educated sample for assessing digital health behaviors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eJustification of the Methodological Approach\u003c/h3\u003e\n\u003cp\u003eThis quantitative, descriptive, cross-sectional design is particularly suitable for addressing the research question because it enables the systematic measurement of students\u0026rsquo; behaviors, perceptions, and use patterns of personal biomedical devices within a large and diverse university population. This approach aligns directly with the study objectives, as it allows the identification of which devices are used, their frequency and context of use, and the extent to which students perceive them as effective. The structured response formats and standardized Likert-scale items further support the consistent assessment of constructs such as perceived effectiveness and behavioral determinants derived from the UTAUT2 model. By providing a detailed snapshot of current self-care practices and digital health engagement among adult students, this design offers a methodologically robust and efficient framework for an exploratory investigation of this nature, even though it does not allow inference of causal relationships.\u003c/p\u003e\n\u003ch3\u003eParticipants and Sampling\u003c/h3\u003e\n\u003cp\u003eThe study population included adult students enrolled in undergraduate and graduate programs at ULACIT during the second academic term of 2025. Inclusion criteria required participants to be 18 years or older and actively enrolled at the university. No exclusion criteria were applied based on prior experience with biomedical devices or health status.\u003c/p\u003e \u003cp\u003eA convenience sampling strategy yielded 234 valid responses, after excluding three individuals who declined the informed consent and were automatically exited from the form. Sociodemographic variables included age, gender, and academic program, and participants indicated whether they had experience with hypertension, diabetes, or overweight, as these represent prevalent NCDs in Costa Rica (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Given the exploratory and descriptive nature of the study, a formal sample size calculation was not performed, as the primary goal was to describe rather than infer population-level relationships. While this cross-sectional design enables a detailed snapshot of current behaviors, it does not allow inference of causal relationships.\u003c/p\u003e\n\u003ch3\u003eVariables and Instruments\u003c/h3\u003e\n\u003cp\u003eFour primary variables were analyzed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUse of personal biomedical devices \u0026mdash; specifically digital scales, sphygmomanometers, and glucometers, focusing on usage frequency and context.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePerceived effectiveness \u0026mdash; participants\u0026rsquo; belief in the accuracy, usefulness, and impact of these devices on health monitoring and self-management.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBehavioral determinants \u0026mdash; derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, encompassing \u003cem\u003eeffort expectancy, hedonic motivation, social influence\u003c/em\u003e, and \u003cem\u003efacilitating conditions\u003c/em\u003e (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSelf-care practices \u0026mdash; behaviors related to monitoring, prevention, and adherence to healthy-lifestyle recommendations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eData were collected using a structured, self-administered online questionnaire developed in Google Forms (Google LLC, Mountain View, CA, USA). The instrument consisted of 36 items divided into five sections: [1] sociodemographic information, [2] use of personal biomedical devices, [3] perceived effectiveness, [4] UTAUT2 constructs, and [5] self-care practices.\u003c/p\u003e \u003cp\u003eResponse formats varied by variable: sociodemographic and device-use questions were categorical or multiple-choice, while the scales assessing perceived effectiveness, acceptance constructs, and self-care used a five-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 5 = \u0026ldquo;strongly agree\u0026rdquo;). The questionnaire was developed specifically for this study based on relevant literature and the UTAUT2 framework and has not been previously published elsewhere. An English-language version of the full questionnaire is provided as Supplementary File 1.\u003c/p\u003e \u003cp\u003eBefore accessing the questionnaire, participants were required to read and accept a digital informed-consent statement. Those who selected \u0026ldquo;I do not consent\u0026rdquo; were automatically exited from the form.\u003c/p\u003e\n\u003ch3\u003eInstrument Validation and Reliability\u003c/h3\u003e\n\u003cp\u003eInstrument validation followed two complementary procedures: content validation and internal reliability testing.\u003c/p\u003e \u003cp\u003eContent validity was assessed by at least two professionals with expertise in engineering and research methodology, who evaluated the questionnaire\u0026rsquo;s clarity, relevance, and congruence with its variables. Revisions were made based on expert feedback to refine wording and improve precision.\u003c/p\u003e \u003cp\u003eInternal reliability was examined using Cronbach\u0026rsquo;s alpha for the main scales of the questionnaire, including perceived effectiveness, technology acceptance, and self-care practices. The analysis indicated acceptable to high internal consistency across all constructs, consistent with commonly accepted reliability standards in behavioral research (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe validated questionnaire was distributed between July and August 2025 via Google Forms. Responses were automatically stored in the platform and exported to Microsoft Excel for Windows, Version 2021 (Microsoft Corp., Redmond, WA, USA) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) for cleaning and analysis. Descriptive statistics (frequencies, percentages, and means) were used to summarize participant characteristics and variable distributions. Missing data were coded as \u003cem\u003e\u0026ldquo;Sin responder\u0026rdquo;\u003c/em\u003e (no response).\u003c/p\u003e \u003cp\u003eGiven the descriptive and cross-sectional nature of the research, no inferential statistical tests or power analyses were performed, in accordance with \u003cem\u003eBMC Public Health\u003c/em\u003e recommendations for exploratory observational studies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was reviewed and approved by the ULACIT Research Ethics Committee prior to data collection. All procedures complied with the ethical principles outlined in the World Medical Association\u0026rsquo;s Declaration of Helsinki (2024 revision) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants were informed of the objectives, voluntary nature, and confidentiality of the study through a detailed consent statement embedded in the questionnaire. Those who declined consent were unable to proceed to the survey questions. The study posed minimal risk, as all data were anonymous, non-identifiable, and self-reported. No sensitive or clinical information was collected beyond general self-reports of chronic conditions. All data were stored securely in encrypted devices, with access restricted to the research team.\u003c/p\u003e \u003cp\u003eChatGPT (OpenAI, 2025) was used to assist in refining the wording and structure of sections of this manuscript. All content was reviewed, verified, and approved by the author, who takes full responsibility for the final text.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant flow and response structure\u003c/h2\u003e \u003cp\u003eA total of 237 individuals accessed the questionnaire and responded to the informed-consent item. Three declined consent and were automatically redirected out of the survey, providing no additional data. The final analytic sample therefore consisted of 234 participants who accepted the consent statement and completed the full questionnaire. Because the instrument employed conditional branching, only participants who reported owning a personal biomedical device were shown the device-specific sections. Consequently, denominators vary across subsections: sociodemographic characteristics and self-care practices were assessed using the full analytic sample (N\u0026thinsp;=\u0026thinsp;234), while device use, perceived effectiveness, and UTAUT2 constructs were analyzed exclusively among respondents who owned a device (n\u0026thinsp;=\u0026thinsp;142).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScale reliability\u003c/h2\u003e \u003cp\u003eInternal consistency across the main scales was strong. Cronbach\u0026rsquo;s alpha analysis indicated acceptable to high reliability for perceived effectiveness, technology acceptance (UTAUT2), and self-care practices, supporting the internal consistency of the measurement instrument. Item non-response was minimal in sociodemographic items and increased as expected in sections conditioned on device ownership, reflecting the questionnaire structure rather than respondent dropout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics\u003c/h2\u003e \u003cp\u003eThe analytic sample (N\u0026thinsp;=\u0026thinsp;234) was predominantly young, with a mean age of 20.7 years (range 18\u0026ndash;59) and nearly all respondents (94.8%) between 18 and 25 years. Most participants identified as female (59.5%), followed by male (38.4%), while less than one percent selected another gender identity. Academic representation was diverse. Biomedical Engineering accounted for the largest share (33.8%), followed by Industrial Engineering, Psychology, Computer Engineering, and Graphic Design and Visual Communication, each contributing around 8\u0026ndash;10% of the sample. Smaller proportions were enrolled in Chemical Engineering, Nutrition, Business Administration, and other disciplines, resulting in a heterogeneous sample with a strong concentration in engineering and health-related fields.\u003c/p\u003e \u003cp\u003eHealth status reports indicated that 90.7% of participants did not have a diagnosed noncommunicable disease. Among those reporting a diagnosis, obesity (7.2%) was the most common, and only a single respondent (0.4%) indicated both obesity and hypertension (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings suggest that the sample largely consisted of healthy young adults, consistent with typical university population profiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics (N\u0026thinsp;=\u0026thinsp;234)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7 (18\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (38.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomedical Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79 (33.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraphic Design \u0026amp; Visual Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemical Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness Administration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212 (90.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (7.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOwnership and use of personal biomedical devices\u003c/h2\u003e \u003cp\u003eAmong the 234 participants, 59.9% (n\u0026thinsp;=\u0026thinsp;142) reported owning at least one personal biomedical device, while 38.8% reported not owning any device. Among owners, digital or smart scales were the most frequently reported devices (62.0%), followed by digital blood pressure monitors (29.6%) and glucometers (23.9%). Notably, some respondents who initially indicated device ownership later selected \u0026ldquo;none\u0026rdquo; when identifying the type of device, suggesting inconsistencies in how respondents classified certain tools.\u003c/p\u003e \u003cp\u003ePatterns of use indicated limited integration into daily routines. Most device owners (78.9%) reported using their device rarely, while 19.0% used them occasionally and only 2.1% used them frequently (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Duration of use showed a similar pattern: nearly 60% were not using any device regularly at the time of the survey, while the remaining respondents reported periods of regular use ranging from less than one month to more than six months. Students generally felt comfortable operating their devices; more than half rated their familiarity as medium to very high, although 15.5% rated it low or very low. Despite this familiarity, only 19.0% reported using a connected mobile application or digital platform, whereas most (71.1%) did not and 9.9% were unsure, indicating limited integration with digital health platforms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOwnership and use of personal biomedical devices (n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice ownership (full sample)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142 (59.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91 (38.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital/Smart scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (62.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital blood pressure monitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (29.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlucometer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ldquo;None\u0026rdquo; after indicating ownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely (\u0026le;\u0026thinsp;1/month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112 (78.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally (1\u0026ndash;3/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequently (\u0026ge;\u0026thinsp;4/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of regular use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot currently using\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85 (59.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamiliarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow/Very low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (15.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh/Very high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (52.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of mobile app\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101 (71.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot sure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePerceived effectiveness of personal biomedical devices\u003c/h2\u003e \u003cp\u003eAmong the 142 device owners, perceptions of device usefulness were generally positive. Overall, 66.2% agreed or strongly agreed that their device helped them maintain better control of their health, while only a small proportion (8.5%) disagreed. Confidence in measurement accuracy was similarly strong, with 72.5% expressing trust in the readings produced by their device (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerceived effectiveness and technology acceptance (UTAUT2) among device owners (n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct / Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisagree n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgree n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice helps control my health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (66.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI trust the accuracy of the device\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103 (72.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice is useful for health decisions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101 (71.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice motivates healthier habits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101 (71.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice is part of my self-care routine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice improves my quality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (62.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerformance expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice improves my health monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97 (68.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice makes health management easier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (69.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffort expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning to use the device is easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (76.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing the device requires little effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113 (79.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitating conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI have the resources to use the device\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116 (81.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI know whom to contact if I have issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (52.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeople important to me encourage use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (70.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u0026rsquo; use motivates me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85 (59.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedonic motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI enjoy using the device\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83 (58.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing the device is worthwhile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86 (60.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing the device is part of my routine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (32.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI use the device almost automatically\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (40.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust and anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI trust the data the device provides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91 (64.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI feel nervous or insecure using it\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40 (28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Likert responses were collapsed into three categories (Disagree\u0026thinsp;=\u0026thinsp;1\u0026ndash;2; Neutral\u0026thinsp;=\u0026thinsp;3; Agree\u0026thinsp;=\u0026thinsp;4\u0026ndash;5).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStudents also perceived these devices as supportive tools for decision-making and lifestyle regulation. A majority (71.1%) believed their device helped them make informed health decisions and motivated them to adopt healthier habits. Nevertheless, everyday integration of device use remained low. Only 39.4% viewed device use as part of their self-care routine, while 36.6% reported that it was not. Even so, a clear majority (62.7%) agreed that using the device had a positive effect on their quality of life, indicating that students appreciate the benefits of these technologies even if they do not consistently incorporate them into daily habits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTechnology acceptance (UTAUT2 constructs)\u003c/h2\u003e \u003cp\u003eTechnology-acceptance findings reflected overall favorable attitudes. Performance expectancy was high, with 68.3% of participants agreeing that their device improved their ability to monitor their health and 69.0% noting that it made health management easier. Effort expectancy was even more positive: 76.1% indicated that learning to use the device was easy, and 79.6% felt that using it required little effort.\u003c/p\u003e \u003cp\u003eRegarding facilitating conditions, most students (81.7%) felt they had the resources to use their device effectively, though only 52.8% reported knowing whom to contact for support if they encountered technical issues. Social influence trends showed that 70.4% perceived support from close contacts, and nearly 60% reported feeling more motivated when others used similar devices.\u003c/p\u003e \u003cp\u003eMotivational responses were also strong. Nearly half of all device owners agreed that they enjoyed using their device or found it rewarding. However, habit-formation indicators were weaker: only 32.4% reported that device use was part of their daily routine, and 40.1% said they used it almost automatically, with substantial percentages disagreeing on both items. Trust remained moderately high (64.1%), while anxiety levels were mixed; about one-third (32.4%) reported feeling nervous or insecure using such technology, while nearly half (48.6%) did not (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSelf-care practices\u003c/h2\u003e \u003cp\u003eSelf-care behaviors were assessed among all 234 participants. Overall, nearly half of respondents (50.0%) reported rarely or never measuring physiological parameters such as blood pressure, weight, or glucose, while 26.1% did so occasionally and 23.9% did so frequently or very frequently. In contrast, healthier behaviors such as maintaining a balanced diet and engaging in regular physical activity were more common. 61.1% frequently or always followed a healthy diet, and 59.4% engaged in physical activity at least three times per week. However, roughly one-quarter reported rarely or never meeting these recommendations.\u003c/p\u003e \u003cp\u003eStudents demonstrated variable adherence regarding the avoidance of excessive sugar, fat, or salt. While 45.7% frequently or always avoided these substances, 24.9% rarely or never did so. Among the small number of students with chronic conditions, 65.4% reported frequently or always following medical recommendations. Finally, most participants (67.5%) agreed that they felt responsible for actively monitoring their health, suggesting strong perceived self-responsibility even if monitoring behaviors did not always align with this sense of obligation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelf-care practices (N\u0026thinsp;=\u0026thinsp;234)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-care item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely/Never n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSometimes n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOften/Always n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures BP, weight, or glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56 (23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaintains a healthy diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143 (61.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u0026thinsp;\u0026ge;\u0026thinsp;3\u0026times;/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139 (59.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvoids excessive sugar, fat, or salt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (45.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollows medical recommendations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153 (65.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeels responsible for monitoring own health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158 (67.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Likert responses were collapsed into three categories (Rarely/Never\u0026thinsp;=\u0026thinsp;1\u0026ndash;2; Sometimes\u0026thinsp;=\u0026thinsp;3; Often/Always\u0026thinsp;=\u0026thinsp;4\u0026ndash;5).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e This study examined the perceived effectiveness and use of personal biomedical devices among adult university students in Costa Rica, offering insight into how young adults incorporate digital health tools into their self-care practices. The findings indicate a clear gap between favorable perceptions of device usefulness and limited behavioral adoption. Although nearly 60% of participants owned at least one device, the majority reported using them rarely, and only a small fraction used them regularly. Despite this low frequency, most device owners expressed trust in measurement accuracy and believed their devices helped them monitor their health, make informed decisions, and adopt healthier habits. These results suggest that the factors limiting device use are not related to perceived value or usability but rather to behavioral patterns, contextual conditions, and the absence of established routines for physiological self-monitoring.\u003c/p\u003e \u003cp\u003eThe discrepancy between positive perceptions and limited use mirrors trends observed in digital health research. Several reviews have shown that users often report satisfaction with biomedical devices yet fail to maintain consistent use over time, especially when the perceived urgency for monitoring is low (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The predominantly healthy profile of the sample (over 90% reported no diagnosed noncommunicable disease) may partly explain this pattern, as individuals without immediate clinical needs are less likely to incorporate monitoring into daily routines. Similar observations have been made in interventions targeting blood pressure and glucose self-monitoring, where adherence improves primarily among individuals with clear medical indications or structured follow-up programs (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In this population of mostly young and healthy students, episodic or reactive use may therefore be more common than sustained preventive monitoring.\u003c/p\u003e \u003cp\u003eThe behavioral findings are also consistent with the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). While performance expectancy, effort expectancy, and social influence were generally high, indicators of habit formation were notably weaker. Only one-third of participants reported that device use had become routine, despite reporting that devices were easy to learn and operate. Prior research applying UTAUT2 in health contexts has shown that habit is a central determinant of continuous technology use, often stronger than initial motivation or perceived usefulness (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Students in this study may therefore be \u0026ldquo;accepting\u0026rdquo; the technology at a cognitive level but not translating that acceptance into regular preventive behavior. Limited use of companion mobile applications (only 19% reported using them) may further weaken habit formation, as digital platforms often provide feedback, alerts, and trend visualization that reinforce regular engagement (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlternative explanations should also be considered. Some participants reported device ownership but later selected \u0026ldquo;none\u0026rdquo; when specifying device type, suggesting possible misclassification or uncertainty about what constitutes a biomedical device. This may reflect lower digital health literacy among some students, even within a technologically oriented population. Social desirability bias may also have influenced perceptions of usefulness, especially among students in health or engineering programs who may feel pressure to endorse positive attitudes toward technology. Additionally, devices such as scales and sphygmomanometers are often used situationally rather than habitually; students might find value in having access to a device when needed without feeling the necessity for frequent use. These interpretations highlight the importance of considering contextual and psychological factors beyond device attributes alone.\u003c/p\u003e \u003cp\u003eThe implications of these findings extend to public health, biomedical engineering, and higher-education settings. At a public-health level, young adults represent a critical population for early adoption of preventive practices. Although students in this study infrequently measured physiological parameters, they demonstrated strong feelings of personal responsibility for monitoring their health. Harnessing this sense of responsibility through structured interventions (such as digital-health education, goal-setting programs, or integration of self-monitoring into university wellness initiatives) could promote more consistent engagement. Such efforts align with global recommendations emphasizing the role of digital self-care in the early detection and prevention of noncommunicable diseases (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a biomedical engineering perspective, the findings suggest that device usability and accuracy are not major barriers; instead, greater emphasis may be needed on ecosystem integration, user engagement strategies, and passive monitoring technologies that reduce the need for deliberate action. Wearable devices that automatically track physiological parameters have shown promise in improving adherence and long-term engagement (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), indicating that future innovations should prioritize seamless integration into daily life. Furthermore, improving app-based features (such as personalized insights, automatic alerts, or gamified health challenges) could enhance motivation and strengthen behavioral routines.\u003c/p\u003e \u003cp\u003eIn higher-education environments, universities can play a pivotal role in shaping health behaviors. Given that most students reported healthy dietary and physical-activity habits yet rarely measured physiological parameters, academic institutions may need to incorporate preventive monitoring into broader wellness programs. Educational sessions on interpreting personal health data, practical workshops on device use, and peer-supported health challenges could reinforce the value of routine monitoring. Such initiatives may be particularly impactful in contexts like Costa Rica, where national strategies for NCD prevention emphasize the importance of self-care and digital health literacy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study enriches the existing literature by providing context-specific evidence on the gap between perceived effectiveness and actual use of personal biomedical devices among young adults in a Latin American university setting. While previous studies have primarily focused on clinical populations or populations in high-income countries, where device use is driven by diagnosed conditions or structured follow-up programs (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), the present findings extend current knowledge by demonstrating that positive perceptions, high usability, and technological acceptance do not necessarily translate into habitual self-monitoring behaviors in predominantly healthy student populations. By integrating behavioral constructs from the UTAUT2 framework, this study also adds to prior research by highlighting the central role of habit formation and contextual factors in shaping sustained engagement with digital health technologies (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The cross-sectional design precludes assessment of causal relationships or temporal changes in device use. The convenience sampling limits generalizability, and the analytic subsample for device-specific variables was restricted to respondents who reported ownership. Self-reported data may be subject to recall or desirability bias, and the study did not include objective usage metrics such as app logs or device-record histories. Despite these limitations, the study offers valuable baseline data and highlights areas for targeted intervention and further investigation.\u003c/p\u003e \u003cp\u003eFuture research should incorporate longitudinal designs to examine how device use evolves over time and whether perceived effectiveness predicts sustained engagement. Mixed-methods research could provide richer insights into motivational, emotional, and contextual factors influencing device adoption. Studies incorporating objective usage data or evaluating behavioral interventions (such as app-based nudges, educational programs, or wearable-device integration) may help identify strategies that effectively promote routine self-monitoring in young adult populations. Expanding research to individuals with diagnosed chronic conditions could further illuminate differences in adoption patterns and health outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that while adult university students in Costa Rica generally view personal biomedical devices as useful, accurate, and easy to operate, these positive perceptions do not consistently translate into routine self-monitoring behaviors. Device ownership was relatively high, yet actual use was infrequent and rarely integrated into daily self-care practices. These findings highlight a critical gap between technological acceptance and sustained behavioral adoption, underscoring that usability alone is insufficient to promote regular engagement with digital health tools among young adults.\u003c/p\u003e \u003cp\u003eThe relevance of these results lies in the growing importance of preventive strategies for noncommunicable diseases. Young adulthood represents a formative period for establishing lifelong health behaviors, and strengthening self-monitoring habits could contribute meaningfully to early detection and long-term risk reduction. The study also emphasizes the need for holistic approaches that combine accessible technology with behavioral reinforcement, digital-health literacy, and supportive environments, particularly within higher-education institutions. By clarifying the factors that shape engagement with personal biomedical devices, this research provides evidence that can inform the development of targeted interventions, public-health initiatives, and user-centered innovations aimed at improving preventive self-care in technologically literate populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNCDs: noncommunicable diseases; UTAUT2: Unified Theory of Acceptance and Use of Technology 2; ULACIT: Latin American University of Science and Technology; WHO: World Health Organization; PAHO: Pan American Health Organization; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the ULACIT Research Ethics Committee prior to data collection. All procedures complied with the ethical principles outlined in the World Medical Association\u0026rsquo;s Declaration of Helsinki (2024 revision). Participation was voluntary, and informed consent was obtained electronically from all participants before completion of the questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. No individual-level or identifiable data are included in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to ethical and privacy considerations but are available from the corresponding author on reasonable request, subject to institutional approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author was responsible for the study conception and design, data collection, data analysis, interpretation of results, and drafting and revision of the manuscript. The author read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to thank the participants for their time and willingness to contribute to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuis Diego Salazar Sandoval is a medical doctor and a biomedical engineering student at the Latin American University of Science and Technology (ULACIT), San Jos\u0026eacute;, Costa Rica.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Noncommunicable diseases: key facts [Internet]. Geneva: World Health Organization. 2025. 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JAMA. 2024;332(15):1532\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2024.21972\u003c/span\u003e\u003cspan address=\"10.1001/jama.2024.21972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"personal biomedical devices, digital health, self-care, technology acceptance, noncommunicable diseases, university students, preventive health, Costa Rica","lastPublishedDoi":"10.21203/rs.3.rs-8389948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8389948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePersonal biomedical devices have become increasingly accessible tools for supporting self-monitoring and self-care in the prevention and self-management of noncommunicable diseases. However, evidence suggests that ownership of these devices does not always translate into sustained use, particularly among young and generally healthy populations. This study aimed to examine the perceived effectiveness and use of personal biomedical devices among adult university students in Costa Rica and to explore behavioral factors influencing their integration into self-care practices.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA quantitative, descriptive, cross-sectional study was conducted among adult university students at a private university in Costa Rica between July and August 2025. Data were collected using a structured, self-administered online questionnaire assessing ownership and use of personal biomedical devices, perceived effectiveness, behavioral determinants of technology use, and self-care practices. Descriptive statistics were used to summarize participant characteristics and response distributions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 234 students participated, of whom 59.9% reported owning at least one personal biomedical device. Among device owners, most reported rare use, despite generally positive perceptions of device accuracy, usefulness, and ease of use. Trust in device measurements and perceived benefits for health monitoring were high; however, fewer than half of participants considered device use part of their regular self-care routine. Behavioral determinants related to effort expectancy and performance expectancy were favorable, while habit formation indicators were weak. Most participants reported healthy lifestyle behaviors but infrequent monitoring of physiological parameters.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough personal biomedical devices were widely perceived as effective and easy to use, their integration into routine self-care among adult university students was limited. These findings highlight a gap between technological acceptance and sustained behavioral adoption, emphasizing the need for strategies that promote habit formation, digital health literacy, and contextual support. Universities and public health initiatives may play a key role in fostering consistent preventive self-monitoring practices among young adults.\u003c/p\u003e","manuscriptTitle":"Perceived effectiveness and use of personal biomedical devices for noncommunicable disease self-management among adult university students in Costa Rica: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 22:57:21","doi":"10.21203/rs.3.rs-8389948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-12T14:02:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161254180164147560348251064770582762866","date":"2026-01-28T17:17:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317503037840977315984097305775944892869","date":"2026-01-21T08:47:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T08:04:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T15:08:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-24T08:58:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-23T16:52:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-12-23T16:48:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e90a0707-a126-44a5-8b3b-3ddaacd2ebe9","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T22:57:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 22:57:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8389948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8389948","identity":"rs-8389948","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}