mHealth Adoption Among Older Chinese Adults: A Conceptual Model With Design Suggestions

Figures & data

Table 1. Constructs extracted from existing models.

Construct	Origin		Definition
Characteristics of Users	Demographic variables
	Age	HBM	The demographic variables of users.
	Gender	UTAUT
	Class	UTAUT2
	Psychological characteristics
	Personality	HBM	The psychological characteristics of users.
	Computer Self-efficacy	TAM3
	Self-efficacy	PMT
	Perceived health condition
	(Perceived) Susceptibility	HBM	An individual’s perception of the risk of acquiring an illness or disease (Rogers, 1975) and the seriousness of contracting an illness or disease (Becker, 1974).
	(Perceived) Severity	MPT
	Health Motivation
	Experience and habit
	Experience	TAM3	The extent to which an individual tends to perform behaviors automatically because of learning (Limayem et al., 2007).
		UTAUT
	Habit	UTAUT2	Habit is a perceptual construct that reflects the results of prior experiences (Venkatesh et al., 2012).
Characteristics of mHealth	Output Quality	TAM3	The characteristics of mHealth itself.
	Result Demonstrability
	Objective Usability
	Computer Playfulness
	Price Value	UTAUT2
Social Influence	Social Influence	UTAUT	An individual’s perception of the degree to which most people who are important to him or her approve or disapprove of the target behavior (Fishbein & Ajzen, 1977).
		UTAUT2
	Peer group pressure	HBM
	Subjective Norm	TAM2
	Image	TAM3
Perceived Usefulness	Perceived Usefulness	TAM	An individual’s perception that using a particular system would enhance his or her job performance (Davis, 1989).
		TAM2
		TAM3
	Performance Expectation	UTAUT
		UTAUT2
	Perceived Benefits	HBM
	Rewards	PMT
Perceived Ease of Use	Perceived Ease of Use	TAM	An individual’s perception that using a particular system would be free of effort (Davis, 1989).
		TAM2
		TAM3
	Effort Expectancy	UTAUT
		UTAUT2
Perceived Behavior Control	Perceptions of External Control	TAM3	An individual’s perceptions of internal and external constraints on behavior (Venkatesh et al., 2003).
	Facilitating Conditions	UTAUT
		UTAUT2
	Perceived Barriers	HBM
	Response Cost	PMT
Emotional Factors	Computer Anxiety	TAM3	An individual’s perception of the pleasure and anxiety derived from using a technology (Venkatesh et al., 2012).
	Perceived Enjoyment
	Hedonic Motivation	UTAUT2
Behavioral Intention	Behavioral Intention	UTAUT	An individual’s motivation or willingness to exert effort to use mHealth.
		UTAUT2
		TAM3
	Intention to Use	TAM TAM2
Actual Usage	Actual Usage	TAM	Actual use of mHealth.
		TAM2
	Use Behaviors	TAM3
		UTAUT
		UTAUT2
	Action	HBM

Figure 1. The preliminary model of mHealth adoption among older adults.

Table 2. Characteristics of the participants.

Characteristics	Number (n = 30)
Age
50–54	4
55–59	12
60–64	7
65–70	7
Gender
Male	14
Female	16
Living Arrangement
Alone	6
With the partner only	11
With the child only	2
With the partner and child	11
Other	0
Education Level
Master’s or higher degree	2
Bachelor’s degree	10
Junior college	8
High school or equivalent	7
Junior High school	3
Primary school or lower degree	0
Employment Status
Retired	15
Employed part time	0
Employed full time	11
Unemployed (no formal job)	4

Table 3. Details of the interview: step 1.

Research contents	Details
Perceived ease of use	What is ‘ease of use’ of an app to you?
	Which of these two apps is easier to you, and why?
Perceived usefulness	Why is this function important to you?
	Why is this function useless to you?
Information Channel	Where do you get health information?
	Where do you get information about mobile apps?

Table 4. mHealth apps used by the participants.

Name of App	Manufacturer	Participant code	Number (n = 15)
Huawei Sports (华为运动)	HuaWei Technologies Co., Ltd	P2, P23	2
Apple health (苹果健康)	Apple Inc.	P11, P18	2
WeChat Sports (微信运动)	Tencent Holdings Ltd	P4, P21	2
Hospital Exclusive Apps	Nanjing Gulou Hospital	P4	2
	Jiangsu Province Hospital of Chinese Medicine	P19
Lifesense Movement (乐心运动)	Shenzhen Lifesense Medical Electronics Co., Ltd	P5, P20	2
Mi Sports (小米运动)	Anhui Huami Information Technology Co. Ltd	P16, P19	2
Joyrun (悦跑圈)	Guangzhou Joyrun Information Technology Co. Ltd	P7	1
Doctor Spring Rain (春雨医生)	Spring Rain Health Co. Ltd	P28	1
Doctor Lilac (丁香医生)	LinkMed Co. Ltd	P6	1
Codoon (咕咚)	Chengdu Cudoon Technologies Co. Ltd	P6	1
Ping An Good Doctor (平安好医生)	Ping An Health Cloud Co. Ltd	P3	1
Ping An Golden Butler (平安金管家)	Ping An Insurance（Group）Company of China, Ltd	P10	1

Table 5. Evaluation of mHealth functions.

Functions	Min	Max	Mean	SD
Health monitoring (e.g., body temperature, blood pressure, blood glucose, and heartbeat)	1	5	4.1	1.2
For emergency (e.g., calling for help automatically, providing vital medical information in an emergency like allergies and medical conditions)	1	5	3.9	1.2
Self-assessment or self-diagnose (e.g., check health statues with apps or websites by yourself)	1	5	3.8	1.2
Finding suitable doctors and hospitals and making an appointment	1	5	3.7	1.3
Knowledge about health and health preservation information	1	5	3.7	1.4
Access to health record/history and examination report	1	5	3.6	1.1
Helping with healthy diet (e.g., healthy recipes, calories calculator, food diary)	1	5	3.5	1.2
Fitness and exercises (step counter and exercise guide)	1	5	3.5	1.2
Information of medicine	1	5	3.4	1.2
Long-term situation management	1	5	3.3	1.1
Reminder for taking medicine or meeting a doctor	1	5	3.3	1.2
Communicating with a doctor online	1	5	3.2	1.3
Communicating with people who have the same health issue	1	5	2.8	1.0
Total valid sample	30

Note. 1 = not useful at all; 5 = very useful.

Table 6. How aging factors influence older people’s adoption of mobile apps.

Factors	Min	Max	Mean	SD
Generation gap (having difficulty to understand the new terms generated by the younger generation)	1	5	3.0	1.0
Visual impairment	1	5	2.0	0.8
Decline in memory	1	5	2.0	0.9
Decline in movement control (e.g., typing, clicking)	1	5	1.9	0.9
Hearing loss	1	5	1.8	1.0
Decline in the ability to focus attention	1	5	1.7	0.8
Decline in the ability to understand written and spoken languages	1	5	1.7	0.7
Total valid sample	30

*Note. 1—no influence; 5—great influence.

Table 7. Information channel of health information.

Information Channel	Number (n = 30)	%
Word of mouth by friends and family	25	83.3
Doctors, hospitals/clinics	21	70.0
Social media (such as WeChat, QQ, Weibo)	18	60.0
TV or radio	13	43.4
Relevant websites	10	33.3
Advertisement in public place (in the underground, or on the street)	2	6.6

Table 8. Information channel of mobile apps.

Information Channel	Number (n = 30)	%
Word of mouth by friends and family	21	70.0
App stores in smartphones	16	53.3
Social media (such as WeChat, QQ, Weibo)	13	43.3
Relevant website	9	30.0
Advertisement in mobile games and website	6	20.0
Advertisement in public place (in hospital, in the underground, or on the street)	2	6.6

Figure 2. The pine tree model.

Table 9. Design implications.

Category		Suggestions
1	Suitable Information Channel	Delivered through interpersonal relationship Delivered through social network Delivered through health authorities
2	Various Behavior Triggers	Provide low-threshold trial opportunities Use different means to trigger the adoption behavior
3	Obvious Benefit	Increase publicity and increase social visibility Show the advantages of the services over existing ones Increase information reliability Respect older adults’ lifestyle Provide spiritual and economical rewards Expand the contents and scope of services Improve the effectiveness and make the outcome visible
4	Low Cost	Make the apps and devices easy to learn Proved accessible help Make the user interface more readable and understandable Reduce information interference (such as pop-up ads) Make it clear which services are free and which require additional expenses Provide free trial or open some free features for starters

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