Journal Digest: Understanding Factors Influencing the Adoption of mHealth in Elderly

For the requirement of this course, we were tasked to digest an academic journal relating to the adoption or use of an any telehealth system.  I chose this article from the 2017 International Journal of Medical Informatics published by Rakibul Hoque  and Golam Sorwar, “Understanding factors influencing the adoption of mHealth by the elderly: An Extension of the UTAUT model”.

You can access the abstract of the article here.

Unified Theory of Adaption and Utilization of Technology (UTAUT) was first described by Venkatesh et al in 2002 to explain user intentions to use a technology and subsequent use of the technology [1]. UTAUT suggest that there are four main constructs that determines user’s intent to use, and use behavior: Performance Expectancy (PE), Effort Expectancy, (EE) Social Influence (SI), and Facilitating Conditions (FC). PE, EE, SI are direct determinants of behavioral Intention(BI) where BI determines Use Behavior, while facilitating condition is a direct determinant of Use Behavior (Figure 1) [1].

4-Figure1-1

Further, there are four factors that moderates the determinants of the four constructs: Gender, Age, Experience, Voluntariness of Use.

In the paper of Hoque and Sorwar, they explored further the applicability of the model to the elderly population, and hypothesized two new factors that might affect behavior intent: Technology Anxiety and Resistance to Change.

They were testing the following hypothesis.

H1. PE has positive impact on the elderly’s intention to use mHealth
H2. EE has a positive impact on the elderly’s intention to use mHealth.
H3. SI has a positive impact on the elderly’s intention to use mHealth.
H4. FC has a positive impact on the elderly’s inention to use mhealth.
H5. FC has a positive impact on the elderly’s actual use of mHealth.
H6. BI has positive impact on the actual use of mHealth.
H7. (New) TA has negative impact on the elderly’s intention to use mHealth.
H8. (New) RC has a negative impact on the elderly’s intention to use mHealth.

The eight (8) hypothesis can be summarize by the figure below.

Summary.png

The study found out that performance expectancy, effort expectancy, social influence, technology anxiety, and resistance to change had significant impact on the user’s behavioral intention to adopt mHealth services. The facilitating condition, however, showed no significant relation to behavioral intention to adopt mhealth Services. 

This study is significant in the Philippine setting as the mobile penetration rate of the country as of 2016 is at 87%[3]. Also, hospitals in Geographically Isolated Areas and other Rural Health Units are now gearing towards improvisation of health systems by implementing telehealth services such as electronic medical records, telemedicine, etc. This initiatives are supported by private institutions, NGOs, and government offices (DOH, DOST, PhilHealth etc.). some projects are even lead by these government offices themselves. However, although the the intentions of implementing these initiatives are good, there are very few studies conducted in the Philippines on how these technologies be easily adopted by the end users, and thus sustainable also. Adding to the fact, that majority of the implementers of these technologies, especially in Rural Health Units, are tenured individuals and therefore (most but not all) belong to an older age bracket. This recent study emphasizes special consideration to the elderly and adds new factors to test and consider to improve behavioral intent, and use behavior.

References:

1. V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User acceptance ofinformation technology: toward a unified view, MIS Q. 27 (3) (2003).

2. R. Hoque, G. Sorwar, Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model

3. http://wearesocial.com/uk/special-reports/digital-in-2016

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