JMIR Publications

ABSTRACT

Background:

Most studies about the e-Health divide among older people compared users to non-users and found that age, gender, and education were associated with e-Health misuse. Their assumptions were that these attributes are a structural static barrier to e-Health adoption. Furthermore, the e-Health practices were examined in a narrow and incomplete way, and they disagree on the association between health conditions and e-Health usage. We adopted a more dynamic theoretical lens to explore the possible motivations that drove older adults' agential adoption of e-Health practices, despite their older age.

Objective:

1. To obtain a comprehensive and detailed description of e-Health usages among older adults e-Health users; 2. To examine whether demographic characteristics such as age, gender, and education (previously related to e-Health miss-use) continue to be associated with the different e-Health clusters. 3&4. To find whether contextual factors such as changes in the health condition of the older e-Health users, or their loved ones are associated with older adults' e-Health use.

Methods:

We conducted a 30-minute telephone interview with a representative sample of 442 Israeli adults (ages 50 and older) with a sampling error of 2.04%. The interviews were conducted in Hebrew, Arabic, and Russian. Using factor analysis with twenty-one questions about eHealth usages, we have obtained four eHealth clusters: • Instrumental and administrative information • Information sharing • Information from peers like me • Online Self-tracking Besides age, gender, education, and self-rated health, we asked respondents to indicate for whom they obtained health information and how much they have used offline health services due to a health crisis in the past year.

Results:

We found differences in the amount older eHealth use the e-Health different clusters. They utilized the Instrumental and Administrative Information cluster and obtained Information from Peers who share the same health situation the most, about half used online self-tracking related to health issues, and only a few uploaded and shared health information online. When controlling for personal attributes age, gender, and education are no longer predictors of e-Health usages, nor is a chronic ailment. Instead, Internet experience, Internet frequency of use, and perceived eHealth literacy were associated with three eHealth clusters. We also found that looking for health information for family and friends predicted all four e-Health clusters.

Conclusions:

Many older adults can overcome structural barriers such as age, gender, and education. The change in their or their loved ones' circumstances encouraged them to make deliberate efforts to embrace new practices expected from today's patients. Seeking health information for family and friends and experiencing unexpected health crises motivate them to become e-Health users. We suggest that health professionals ignore their tendency to label older people as non-users and encourage them to benefit from using e-Health and overcome stereotypical ways of perceiving these patients. Clinical Trial: NA