Health-Seeking Behavior and Its Associated Technology Use: Interview Study Among Community-Dwelling Older Adults

Abstract

Background Understanding older people’s health-seeking behavior (HSB) is crucial for uncovering their health needs and priorities and developing appropriate policies to address them and avert their disease progression. Technologies play an active role in our daily lives and have been incorporated into health activities to support the older population and facilitate their HSB. However, previous studies of HSB have mainly focused on behaviors during illness, and there are limited studies on how technologies have been used in older people’s health-seeking activities. Objective This study aimed to investigate HSB and the associated technology use among the older population, ultimately proposing implications for practice to address their unmet health needs. Methods This paper presents partial data from a large qualitative study, which has been approved by the institutional review board and used a phenomenological approach. Semistructured interviews were conducted between April 2022 and July 2022, either via Zoom (Zoom Video Communications Inc) or face-to-face sessions. Inclusion criteria were being aged ≥50 years, long-term residence in Singapore, and being able to speak English or Mandarin. The interviews were manually transcribed verbatim, and thematic analysis was performed, with the individual as the unit of analysis to understand the patterns of behaviors. Results In total, 15 interviews were conducted to reach thematic saturation. We identified 5 main consequences of HSB, which were aligned with the original HSB model. Regarding technology use in health seeking, 4 themes were extracted: the most widely used digital technologies are the mobile health apps and wearable devices with the associated wellness programs launched by the government and local companies, and they have the potential to enhance health communication, promote health maintenance, and increase access to health services; information communication technologies and social media, though not primarily designed for health purposes, play a substantial role in easing the process of seeking health information and managing symptoms. Although the outbreak of the COVID-19 pandemic has resulted in some alterations to older adults’ well-being, it has catalyzed the adoption of telehealth as a complement to access health care services, and older adults have different considerations when selecting technologies to facilitate their health seeking and fulfill their health needs. Moreover, 4 archetypes were proposed based on our findings and the insights gained from our participants’ observations in their social networks. These findings led to several implications for practice regarding health communication and promotion, health education, technology design and improvement, telemonitoring service implementation, and solutions to address the needs of each proposed archetype. Conclusions Unlike the commonly held belief that older adults resist technologies and lack technological proficiency, our findings showed that technologies could play a promising role in facilitating older adults’ health seeking. Our findings have implications for the design and implementation of health services and policies.

Sociodigital Determinants of eHealth Literacy and Related Impact on Health Outcomes and eHealth Use in Korean Older Adults: Community-Based Cross-Sectional Survey

“…Integrating all these findings, we can highlight the significant influence of eHealth literacy upon general wellbeing, self-care and opportunity to utilize health and social care services and health promotion through multiple evidence. The findings in other recent publications agreed with our findings [30,31]. The eHealth literacy of community-dwelling older Korean adults is relatively low, and 66.6% need support and facilitation to improve their skills (Table 2).…”

Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models

“…For example, insufficient health information can lead to inaccurate self-diagnosis with subsequent associated health risks [21]. Additionally, comprehensive health information helps lay individuals make informed decisions regarding their health and can help improve communication with health professionals, which culminates in positive health-seeking behavior [22,23]. Consequently, it is important to assess the completeness of health information generated by AI-based tools and to identify the possible gaps in such information [1,24].…”

CLEAR: Pilot Testing of a Tool to Standardize Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models

“…For example, insuf�icient health information can lead to mistaken self-diagnosis with subsequent associated health risks [21]. Additionally, comprehensive health information helps lay individuals to make informed decisions regarding their health and can help to improve communication with health professionals, that culminates in positive health-seeking behavior [22,23]. Consequently, it is important to assess the completeness of health information generated by AI-based tools and to identify the possible gaps in such information [1,24].…”