Health-seeking behavior (HSB) is an important concept associated with a country’s health status and economic development [1]. It also helps uncover the health needs and priorities of a population and informs the development of appropriate policies to address their needs and avert their disease progress [2]. In the literature, HSB has been studied with different scopes. The most commonly used definition is “any action or inaction undertaken by individuals who perceive themselves to have a health problem or to be ill for the purpose of finding an appropriate remedy” [3], and studies adopting this definition always examined the formal health system use or the process of illness responses [2,4-6]. However, one could argue that this definition focuses mainly on illness behaviors [7] and ignores the importance of promoting overall health and well-being, as health is not merely the absence of disease or infirmity but rather a state of complete physical, mental, and social well-being [8]. Thus, HSB could be viewed in a broader sense. A more comprehensive definition proposed by Chinn and Kramer is an individual’s deed to the promotion of maximum well-being, recovery, and rehabilitation; this could happen with or without health concerns and within a range of potential to real health concerns [9]. This definition encompasses practices for preserving optimal wellness, preventing illnesses, and addressing any deviation from good health [1,10], which aligns with the concept of universal health coverage [11].

Technologies have been seamlessly integrated into different aspects of people’s life and reshaped people’s health-seeking activities. For instance, smartwatches and wristbands with various sensors can collect continuous biological, behavioral, and environmental data; deliver health interventions; and measure users’ health outcomes [12]. Mobile apps can keep people connected with their families and friends, disseminate health information [13], store and share health and lifestyle data [14], manage chronic diseases [15], and manage medical appointments [16]. Self-test kits, such as Antigen Rapid Test Kits, allow people to obtain results swiftly and conveniently at any location and time [17]. As another example, telemedicine services such as telephone calls and video consultations have also demonstrated their potential to be a cost-effective and efficient alternative solution to access quality health care during the COVID-19 pandemic, by reducing travel time and protecting users from disease transmission [18].

Singapore has one of the fastest-growing older populations in the world owing to the increasing life expectancy and low fertility rates [19,20]. Statistics showed that the proportion of the silver generation in Singapore has been rising from 3.4% in 1970 to 10.4% in 2011 and 17.6% in 2021 [21], and this number is anticipated to reach approximately 23.8% by 2030 [22]. To address this demographic shift, the Singapore government has been constantly exploring technological solutions and launching health initiatives to proactively meet the older population’s health care needs. Some examples include HealthHub—the 1-stop digital health care companion to manage appointments and access personal medical records [23] and National Steps Challenge—the world’s first population-level and fitness tracker–based physical activity that encourages Singapore residents to track their daily moderate to vigorous physical activities and get rewards [24]. Despite these efforts, there remains a scarcity of studies into the patterns of the older population’s HSB and the potential of technologies in facilitating their health seeking and addressing their health needs.

This study aimed to investigate HSB and associated technology use among older people in the Singapore community. We hoped to gain deep insights into how this population makes decisions when they engage with the health system and use technologies; identify any unmet needs; suggest ways in which technology can be leveraged to address these needs; and ultimately, propose recommendations for practical strategies that ensure they are not excluded from the efforts to build the smart nation. In particular, we would like to answer the following research questions:

We used a phenomenological approach to explore the lived experiences of older adults’ HSB and associated technology use. The reporting of this study was guided by COREQ (Consolidated Criteria for Reporting Qualitative Research), a 32-item checklist [25].

Inclusion criteria were (1) being ≥50 years old, (2) being Singapore citizens or foreigners who are dwelling in the Singapore community in the long term, and (3) being able to read and converse in English or Mandarin.

The study was planned during the stabilization phase [26,27] (with heightened COVID-19 safety management measures [SMMs] to slow down the rate of transmission), and participant recruitment started in January 2022, during the transition phase in Singapore (groups of up to 5 people were allowed in social gatherings) [28]. We aimed to recruit a diverse range of participants in terms of sociodemographic and socioeconomic characteristics such as age groups, ethnicities, education backgrounds, occupations, income levels, and housing types. To achieve this, we adopted convenience sampling with various recruitment strategies, considering the unpredictable SMMs imposed owing to the COVID-19 pandemic. Before data collection, we posted our study posters on social media platforms (Facebook and Instagram) and shared our study information with some chat groups for older adults. We then obtained referrals from our early participants and stopped recruitment until no new themes were identified. Subsequently, we reviewed the profiles of the interviewees and purposively recruited more participants of interest from the community centers and public areas during the COVID-19–resilient nation phase (the social distancing measures were relaxed, and the level of the Disease Outbreak Response System Condition was adjusted to yellow) [29]. We ceased the whole participant recruitment process in July 2022, as no new insights were obtained (thematic data saturation was reached).

This qualitative study used 2 data collection tools—a web-based registration form and a semistructured interview guide.

Data collection occurred from April 2022 to July 2022. Older adults who expressed interest in participating were invited to complete the web-based registration form and screened to check their eligibility for the interview. Eligible participants were then informed about the data collection procedure; recording of the interviews; and how the data would be analyzed, reported, and protected through written informed consent. Only those who gave their consent were scheduled for an interview. To comply with the SMMs, Zoom (Zoom Video Communications Inc) was chosen as the default interview medium, and a manual was provided to the participants whenever needed. Participants who had unresolvable difficulty with Zoom were offered the option of a face-to-face interview. YZ contacted all eligible candidates either via phone calls or WhatsApp to explain the study details again and scheduled the interviews at their convenience. Written informed consent and permission for interview recording were obtained in advance. Each participant was only required to attend 1 interview session. All interviews were conducted on a one-on-one basis and were video recorded with participants’ consent.

At the beginning of each interview, the study details were explained again, and verbal consent was obtained and recorded. YZ conducted all the interviews. She documented her observations through field notes and confirmed important findings with the participants during the interviews to ensure accurate interpretations. The interview consisted of 3 sections: HSB, associated technology use, and how the participants selected technologies. Participants received shopping vouchers worth SG $20 (US $15) upon successful completion of the semistructured interview.

Data analysis was performed after each interview. Each participant was assigned a unique identifier before data analysis, and all study data were deidentified during the transcription process. YZ manually transcribed all interviews verbatim in Word (Microsoft Corp) and transferred all text data to Excel (Microsoft Corp) for thematic analysis. Participants who preferred Mandarin had their transcripts translated by YZ and reviewed by WPT, to ensure the highest level of accuracy. The individual was chosen as the unit of analysis to identify the patterns of HSB and technology use in the older population. Next, YZ conducted inductive thematic analysis, in which a line-by-line coding approach was used while reading through the qualitative data. The codes were then categorized based on the research questions, and the generated pattern codes were used to identify themes. YZ and WPT met regularly to discuss the codes and themes until consensuses were reached, to enhance the validity of the analysis.

This paper presents partial data from a large study, which has been approved by the institutional review board of Nanyang Technological University Singapore (reference IRB-2021-797).

In the period from February 2022 to April 2022, 12 individuals signed up for our study after seeing our study poster on social media or in the chat groups for older adults, but only 2 (17%) met our inclusion criteria. Additional 8 participants were then recruited via the referral of the initial 2 eligible participants until no further new themes were generated. We subsequently visited different community centers and public areas and recruited 5 participants with low socioeconomic status or from other ethnicities before we reached thematic saturation. In total, we conducted 13 Zoom interviews where participants remained in their homes, and 2 face-to-face interviews were conducted at the community centers in close proximity to the participants’ residences. The mean duration of the interviews in the larger study was 39 minutes (SD 15 minutes 26 seconds).

The 15 participants were aged between 55 and 73 (mean 65.8, SD 6) years, among whom 9 (60%) were women, 13 (87%) were Chinese, 6 (40%) were still working, and 8 (53%) had a personal monthly income of <SG $1000 (<US $752). All participants (15/15, 100%) owned a personal smartphone, but 13% (2/15) of them did not have Wi-Fi access at home. Their characteristics are summarized in Table 1, and detailed information can be found in Multimedia Appendix 2.

We explored the use of technologies in each consequence of the older adults’ HSB (Figure 2) and extracted 4 main themes: theme 1—the most widely used digital technologies among the older population are mHealth apps and wearable devices with associated wellness programs launched by government agencies and local companies, and these technologies have the potential to enhance health communication, promote health maintenance, and increase access to health services; theme 2—information communication technologies (ICTs) and social media, although not primarily designed for health purposes, play a substantial role in easing the process of seeking health information and managing symptoms; theme 3—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 theme 4—older adults have different considerations when selecting technologies to facilitate their health seeking and fulfill their health needs.

This study expanded upon the literature by adopting the multidimensional model by Poortaghi et al [30] to explore the HSB of older adults in the Singapore community. Unlike previous studies that only focused on health system use and illness responses, our study explored older adults’ health-seeking practices with a comprehensive approach, including health information seeking, health maintenance, early diagnosis and complication control, responses to symptoms, and access to and interaction with health care services. We also examined older adults’ technology use and considerations for technology selection, and our results offered a different perspective from a previous qualitative study in Singapore, which found that older adults’ HSB was technology independent [38]. Our findings suggested that technologies have been seamlessly integrated into older adults’ health-seeking activities and have a promising capability of encouraging proactive HSB.

We identified a total of 5 major consequences of HSB, namely health information seeking, health maintenance, early diagnosis and complication control, responses to symptoms, and health service access and interaction, which are consistent with the model by Poortaghi et al [30]. Although we made an effort to recruit participants with varying sociodemographic and socioeconomic characteristics and observed relatively consistent patterns, the exploratory nature and limited sample size of qualitative research may restrict the generalizability of our findings. Future studies could expand on our study by incorporating the antecedents in the model by Poortaghi et al [30] and evaluating the impact of social, cultural, economic, and health-related factors on HSB, to uncover the unfulfilled needs of the older population.

A key discovery was the older adults’ wide engagement with technologies and associated health campaigns launched by the government and local companies. Supporting the findings by Low et al [38], most of our participants (10/15, 67%) were motivated to use wearable fitness trackers together with mHealth apps to keep track of their physical activities. These technologies could provide users with real-time feedback and motivate them to achieve the predetermined lifestyle goals [39]. Moreover, the financial incentives offered by these initiatives seemed to be an important motivator for older people to use such technologies and encourage positive changes and discourage negative ones in health behavior [40]. Future studies could further investigate the extent to which technology-driven behavior change techniques such as incentive schemes can alter users’ behavior and improve their health outcomes. It would also be valuable to explore how such techniques can be integrated with policy tools to facilitate sustainable behavior change.

Technologies offer a solution to overcome the challenges in health service use and interaction. Although all our participants (15/15, 100%) were satisfied with their living and surrounding environments, were able to easily access primary care, and could enjoy affordable medical services through various government subsidies, they pointed out 2 challenges during the interaction with the current health systems. Similar to a theme in the findings by Lee et al [5], many of our participants (11/15, 73%) also shared their experience of waiting several hours for just a few-minute consultation, particularly after public holidays and during the COVID-19 pandemic. In addition, they also encountered communication barriers and even felt discouraged from engaging in further communication as they felt pushed away by their health professionals. These might be attributed to a shortage of health professionals in the country, as reported by the Ministry of Health that the physician-to-population ratio was 1:399 and there were only 2.5 physicians per 1000 population in 2019 [41]. In addition to incorporating self-service medical booths [42], the use of digital technologies such as the machine learning–based solution being developed by the Singapore National Eye Center, called Appointment Scheduling Optimizer, could reduce patients’ waiting time [43]. Telehealth, which has the potential to reduce expenses and time spent on traveling and waiting [44], might offer another viable solution, but future studies are needed to further reaffirm the acceptability, effectiveness, and cost benefits of telehealth services.

ICT and social media expand people’s alternatives in searching for health-related information; however, they are not tailored to health intentions and cannot alleviate, diagnose, or cure diseases. In contrast to the findings by Lin et al [45] that only a small portion of the participants searched for health information on the web using their mobile phone, a high percentage of older participants in our study used their smartphones for searching and sharing health information, via web-based resources, social media, mHealth apps, and messaging through social network communication tools. This difference may stem from the improved accessibility and easy sharing of information through mobile phones in recent years. Some of the government initiatives have also begun to harness these platforms, such as HPB’s posting of health-related videos on YouTube for health promotion [46] and the launch of the official COVID-19 channels on Telegram and WhatsApp to broadcast news updates, deliver important announcements, and reduce the spread of misinformation during the COVID-19 pandemic [47]. An unexpected result was that almost all participants (14/15, 93%) made an effort to question and assess the credibility of the health information. They exhibited skepticism toward the health information encountered and used various means to verify its credibility. Our findings suggested that technologies have shaped health information seeking as a collective and collaborative effort—people first gather information; next, verify and circulate it through their social network; and then, apply it in their daily practice and provide feedback. Through this cyclic seeking-verifying-sharing-applying-feedback process, they can reaffirm the credibility and effectiveness of the information through firsthand experience.

Although the COVID-19 pandemic has brought some alterations to older people’s well-being and disruptions to their lifestyle practices, it also catalyzed technological innovation and adoption. For example, telehealth, although not a novel concept [48,49], gained increasing attention amid the COVID-19 pandemic [50]. Our participants shared that different telehealth modalities could enable them to continue their access to health care services and health education. Future studies could further evaluate the role of telehealth in aging care.

This section serves as the last consequence in the HSB model by Poortaghi et al [30]—design and implement needed services.

This study had several strengths. Conceptually, we extended the literature by adopting a multidimensional model and assessed the older adults’ HSB using a more comprehensive approach, and our findings further reiterated this model. Methodologically, we adopted multiple recruitment strategies to reduce selection bias, and we managed to get insights from older adults with varying sociodemographic and socioeconomic characteristics in Singapore. Despite these strengths, we acknowledge that our study also had some limitations. First, most participants (13/15, 87%) were Chinese, which might be because of the inclusion of only English and Mandarin speakers, considering the researchers’ language proficiency. Future studies could be extended to Malay, Tamil, and Chinese dialect speakers. Second, owing to the COVID-19 pandemic restrictions, the main method of participant recruitment and contact was through social media and WhatsApp, which could have resulted in a possible bias toward individuals who were more technologically advanced and had better access to technologies. Future studies should attempt to reach out to individuals who are digitally illiterate or homebound. Finally, this was an exploratory qualitative study with a limited sample size, and the choice of the individual as the unit of analysis to understand group phenomena may overlook certain fine points and variations among individuals. Large-scale quantitative or mixed methods studies could further investigate the impact of social, cultural, economic, and health factors (the antecedents in the model by Poortaghi et al [30]) on older adults’ HSB and how technologies can address these inequalities.

In conclusion, we have extended the literature and investigated older adults’ HSB and associated technology use with a more comprehensive approach. Unlike the commonly held belief that older adults resist technologies and lack technological proficiency, our results 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.