With the advancement of medical science, the population of aging people is increasing more than before worldwide, especially in developing countries 1,2. According to WHO 3, about 10 percent of the world population is aged 60 years or more, accounting for 840 million people, and it is predicted to be more than one billion or about 12 percent by 2030 4,5. It is also anticipated that five developing countries: Bangladesh, India, Pakistan, China, and Indonesia, would cover around 50 percent of the world’s elderly population by 2025 6. Due to a poor socio-economic condition in the developing nations of Asia, the elderly reaching their sixties or beyond struggle with inadequate diet, no or limited access to quality healthcare, and poverty 7. On the other hand, traditional sources of security and healthcare for an aging population have already started to disappear with the changing concept of family and responsibility towards aged ones due to globalization 8.
Elderlies are comparatively more prone to physical disability, mental incapability, and chronic diseases than others 9. They need frequent visits to the doctor and/or hospital, which is inevitable when they become older 10. Being more vulnerable than younger people, they need to rely on social connections and family members for monitoring and ensure compliance with medication and a healthy lifestyle 11. However, due to their physical inability, frequent movements are difficult for them 12. Due to a higher hospitalization cost, such monitoring comes out as best if it could be arranged at home 13. Family members and relatives need to make room in their busy schedules to take care of elderlies' health in manifold ways, e.g., doctors' appointments, routine medication, exercise, specialized food, and so on 14.
Due to the significant financial burden, very few older couples or single elderlies can avail of external caregivers; hence long-term care for them is most challenging 15. They often need help with comprehending and administering drugs, interpreting diagnostic test reports, finding emergency contacts of hospitals, etc. Besides, in cases of chronic diseases or emergencies, they require intense monitoring round the clock. Moreover, the recent worldwide outbreak of COVID-19 manifests how older people might become the most vulnerable and subjected to be in quarantine and follow-up care from home 16. In such a scenario, Mobile Health (mHealth) services play a role in minimizing costs, time, stress, and discomfort 17 by overcoming the stated inconveniencies and enabling healthcare access from home 18. Other than complex tests and operative measures, routine consultation and medication from doctors can easily be availed through mHealth 19,20.
As mHealth can facilitate the registration for doctors’ appointment, receiving medical prescriptions, test results and treatments after diagnostic 21, it is found as a significant and efficient way of access to healthcare by elderlies 22,23 as well as improving the health condition of elderlies 24. Recent studies have reported multiple successful applications of mHealth for elderlies, such as self-management chronic diseases 25, self-care of asthma 26, medication adherence 27, medication safety 28, and healthcare monitoring at home 29. Other diverse uses of mHealth for elderly healthcare include empowerment and patient-centered healthcare 30, mHealth for wheelchair users 31 and fall detection 32, and supporting elderlies in outdoor risk circumstances 33. Overall, parallel to regular face to face healthcare, mHealth helps as an additional but significant help by playing digital various roles 34.
Despite potential advantages with mHealth applications, only a handful of the elderly population embraces mHealth in their real-life needs, while most still depend on traditional health services 35. Recent studies have identified motivation, perception 36, low literacy, user interface, cost, and income 37 as the significant barriers to adopting mHealth applications. Low mHealth uptake due to these barriers is commonly reported in developing countries like India, Pakistan, Sri Lanka, and Bangladesh 38–40 that have similar socio-economic status. On the other hand, Technology anxiety, resistance to change, and effort in using were also found as potential barriers in mHealth adoption by the elderly in Bangladesh 41. In contrast, Kaium, Bao, Alam, and Hoque (2020) 42 showed that performance expectancy, facilitating conditions, and social influence are significant influencing factors for the rural older people's adoption of mHealth services in Bangladesh 42. Another study found that social influence, facilitating condition, performance, and reliability influence the general population's behavioral intention to use mHealth services in Bangladesh 43.
Although considerable numbers of studies have been conducted on the adoption of mHealth services in Bangladesh, the number of systematic and theory-based empirical studies focusing on elderlies is seemingly absent. To address these gaps, Hoque & Sorwar 41 used the Unified Theory of Acceptance and Use of Technology (UTAUT) to study the factors influencing adoption and usage of mHealth from older population perspective in the Bangladesh context. They extended the UTAUT model with two additional variables, i.e., technology anxiety and resistance to change for understanding the users’ behavioral intention to adopt mHealth services.
However, unlike in the past, the change in lifestyle and pandemic impact in Bangladesh has recently created an enormous demand for mHealth apps or websites 44. Although not in a mature stage 45, the availability of mHealth services and their adoption are gradually increasing. Subsequently, the factors related to lifestyle (e.g., quality of life) and service (e.g., service quality) have become important for the broader adoption of these technologies.
Quality of Life (QL) is extensively studied as a significant outcome variable in the existing literature on health, information systems, and marketing. In contrast, few studies highlighted QL as a significant determinant of the intention to continue using Assistive technologies by older people 46, intention to use mHealth 47,48, and technology acceptance for telecare program by older people 49. A study by Shen 50 indicates that QL theory-related constructs 'Loneliness in Real Life' and 'Life Dissatisfaction' are significant antecedents of seeking support via social networks. Chen and Chan 51 indicate that constructs such as 'cognitive ability', 'social relationships', 'attitude to life and satisfaction', and 'physician functioning’ are related to QL and those have an impact on the acceptance of gerontechnology by elderlies in Hong Kong. Another study by Vululleh 52 found that QL is a strong determinant of behavioral intention to use eLearning technology. Although QL influences users’ acceptance of technology, few studies are available evaluating the predictive power of QL in the acceptance of mHealth technology, particularly by developing nations with low Wellness Index (WI) such as Bangladesh (WI= 43) 53.
Similarly, Service quality (SQ) is a significant factor for healthcare, including mHealth adoption 54–57. Inferior service quality is a major reason for discontinuing using mHealth services 58. Existing studies confirm that service quality is multi-dimensional 59 and context-specific 60. Most studies focus on system and information quality dimensions such as the accessibility and reliability of service delivery infrastructure, information privacy, and trust. For example, Nisha et al. (2016) 61 proposed a conceptual model to examine the factors primarily related to system quality and information quality to understand users’ intention to use mHealth services in Bangladesh. Alam et al. (2018) 43 introduced perceived reliability of service delivery infrastructure and data privacy as an additional construct with the base UTAUT constructs to identify key factors affecting the mHealth adoption by the highly educated (100% graduate or above), whereas about 65% of the older population in Bangladesh have only primary education 62 and younger (majority, 73% aged 55 years or less) users (patients) in Bangladesh. Similarly, Kaium et al. (2020) 42 investigated the reliability of the technical aspects of service provision infrastructure and found no significant impact on mHealth adoption by the rural population in Bangladesh.
On the other hand, Jandavath & Bryan (2016) 63 argued that among other dimensions, empathy has significant effects on behavioral intention to adopt healthcare services. A recent study by Zobair et al. (2020) 64 indicates that the health staff motivation in the caring and individualized attention has substantial effects on the quality of care and patient satisfaction in telemedicine service in Bangladesh. In another qualitative study by Khatun et al. (2016) 65 found that general people (aged between 18-63yrs) are concerned about the quality of healthcare providers in mHealth services in Bangladesh. As per the above discussion, there are no studies available investigating the factors influencing the adoption of mHealth services by the elderly from a human-centric perspective: the influence of health care providers' e. g., general practitioner involved, motivation, medical advice (service) quality, users trust in the service providers, the competence of provider to deliver service over mobile technology and their empathy.
Due to COVID-19, there is a surge in mHealth use in developing countries like Bangladesh. However, no previous studies have systematically reported whether SQ and QL are significant factors for mHealth adoption by elderlies in Bangladesh. Therefore, it is imperative to investigate the impact of SQ and QL to uncover a more generalized and widely applicable model for understanding the users' behavioral intention of mHealth by elderlies in the context of developing countries like Bangladesh.
Therefore, the study aimed to extend the UTAUT2 further with service quality (SQ) and the quality of life (QL) to empirically find the key factors that influence elderlies’ intention to adopt and use mHealth services. As a theoretical framework, UTAUT2 has been used in this study to investigate a broader range of factors that might influence the adoption and usage of mHealth services by elderlies.