Factors Influencing the Digital Health Technology Use Behavior among the Older Adults in Phetchaburi Province​

Abstract

Introduction Currently, Thailand has experienced a continuous increase in the proportion of its older population, having fully transitioned into an aged society and projected to become a super-aged society in the near future. Consequently, health and quality of life in older adults have emerged as a matter of urgent priority, particularly regarding the widely applied role of digital technologies in healthcare. Nevertheless, the acceptance and use of digital technologies among older adults remain constrained by various factors, including age, education, income, prior technological experience, and attitudes and social influences. Therefore, it is necessary to investigate factors influencing older adults’ acceptance and adoption of digital healthcare technologies. Such research is essential for designing approaches that genuinely address their specific needs. This understanding will facilitate the development of user-friendly and appropriate health technologies that are congruent with the socio-cultural context of Thai older adults. Ultimately, this ensures that health technology serves as a pivotal mechanism that empowers older adults to engage in self-care effectively, thereby enhancing their overall quality of life in an aging society.

Objective: The objectives of this study were 1) to investigate the digital health technology use behaviors in older adults, and 2) to examine factors predicting digital health technology use behaviors, including personal factors and digital technology acceptance factors among older adults.

Design This study employed a predictive descriptive design, applying the conceptual framework of the Technology Acceptance Model 2 (TAM2) to investigate the factors influencing the digital health technology use behaviors of older adults. The model suggests that digital health technology use behavior stems from cognitive appraisal and social environmental influences. Building upon the original TAM, it incorporates additional components to provide a more comprehensive explanation of real-world behaviors. Specifically, TAM2 categorizes its core components into two primary domains: social influence processes and cognitive instrumental processes.

Methodology The participants comprised 250 older adults, aged 60 years and older, who were members of senior clubs in Phetchaburi Province. Data collection was conducted between May and June 2025. The participants were recruited using a multistage random sampling, with the sample size determined according to the guidelines proposed by Hair et al. The research instruments consisted of five questionnaires: 1) a demographic information questionnaire; 2) a digital technology perception questionnaire, including perceived usefulness and perceived ease of use; 3) a social influence on digital technology use questionnaire; 4) an attitude toward digital technology use questionnaire; and 5) a digital healthcare technology utilization behavior questionnaire. The instruments were validated for content validity by a panel of six experts, yielding Content Validity Indices (CVI) for the second through the fifth instruments of .92, .86, 1.00, .98, and .95, respectively. Reliability testing resulted in Cronbach’s alpha coefficients of .91, .91, .96, .94, and .96, respectively. Data were collected via self-administered questionnaires and analyzed using both descriptive and inferential statistics, including Pearson’s correlation coefficient and Stepwise Multiple Regression analysis.

Results The sample consisted of 250 older adults aged between 60 and 92 years (M = 69.01, SD = 7.22). More than half were classified as young-old (56.40%). The most prevalent educational attainment was primary education (50.80%), and the most common occupation was housewife (24.60%). The majority reported an income between 500 and 10,000 THB (81.20%). Most participants had over 6 years of experience with technology (68.80%), with the mobile phone as the most frequently used digital device (94.40%). Furthermore, a significant majority had underlying health conditions (77.20%), with the three most common conditions being hypertension (53.60%), hyperlipidemia (38.40%), and diabetes mellitus (26.80%). Regarding digital health technology use behaviors, the overall use of digital technologies for healthcare among older adults was moderate (M = 2.76, SD = 1.12). When examined by specific domains, health communication was the most frequently used function, followed by health information seeking and health monitoring and management, in which older adults periodically used technology to check their basic health data. Conversely, health promotion and online health transactions, such as playing brain-training games, purchasing health products, and scheduling medical appointments or medication pick-ups, were the least utilized domains. Finally, a Stepwise Multiple Regression analysis revealed that the factors significantly predicting the digital healthcare technology use behaviors among older adults included social influence (β = .288, p < .001), attitude toward digital technology use (β = .253, p < .001), perceived ease of use (β = .175, p = .002), age (β = -.143, p = .002), experience using digital technology (β = .120, p = .009), and education (β = .110, p = .015). Together, these variables accounted for 52.9% of the variance in digital healthcare technology utilization behaviors among older adults (Adjusted R² = .529, p = .015).

Recommendations Community nurse practitioners can utilize these research findings to design and implement interventions aimed at promoting digital health technology use behaviors among older adults. This can be achieved by fostering social support networks involving family members, peers, and community health volunteers. Furthermore, practitioners should enhance positive attitudes and self-efficacy regarding technology adoption through constructive health communication. It is also essential to organize accessible training programs on health technologies that are comprehensible and explicitly tailored to the cognitive and functional capacities of older adults.