Telehealth care system for chronic disease management of middle-aged and older adults in remote areas

Abstract

Background

With the global spread of the coronavirus disease pandemic, governments have become more interested in applying telehealth technology in 2020.

Objective

This study integrates the technology acceptance model and information system success model to explore the influence of satisfaction, attitudes, and continued use of telehealth systems among middle-aged and older people in remote areas.

Methods

The study participants were patients over 40 years (inclusive) who lived in remote townships in eastern Taiwan, were diagnosed with diabetes or hypertension, and used the telehealth care system. In total, 545 questionnaires were returned, resulting in a recovery rate of 99%.

Results

Accepting the hypothesized structural equation model, this study found that information, system, and service quality were influenced by the mediating effect of perceived ease of use and the perceived usefulness of the technology acceptance model. The use of telehealth care systems among chronic patients increased significantly.

Keywords

Coronavirus information systems patient satisfaction technology telemedicine

Introduction

With the global spread of the coronavirus disease 2019 (COVID-19) pandemic in 2020, governments have become more interested in applying telehealth technology. Telehealth can improve access to care, reduce utilization of medical center resources, and reduce infection transmission risk.¹ An important advantage of telemedicine, in addition to its being beneficial for keeping both patients and health workers safe, is that it improves access to healthcare givers, especially in cases of chronic conditions.² Telehealth can also bridge the gap between urban and rural medical resources and can become an essential tool for epidemic prevention during infectious disease outbreaks.

Several countries have applied information and communication technology to long-distance care by combining medical care and technological development. The resulting functions, which are expected to reduce medical and labor costs, include notifications of abnormalities, medication reminders, physiological recording and monitoring, remote video, and medical consultation.^3,4

Telehealth can improve continuity of care, access to care, and health outcomes. The application of telehealth care systems can also support person-centered health care in COVID-19 treatment.⁵ Remote health monitoring can help reduce unanticipated occurrences through timely treatment, prevent the wasting of medical resources, and monitor the compliance of patients with medical prescriptions. Previous studies found that telehealth is an effective tool for treating patients with diabetes and hypertension. It can reduce harm from disease progression and increase chronic patients’ willingness to accept self-care at home.⁶ Telehealth services have the potential to improve access to care, especially in rural or urban areas with scarce health care resources.⁷ Not only can it effectively control and reduce the incidence of hospitalization, but it can also significantly delay readmission.^8–10

Previous research has established the benefits of telehealth care. A recent study using the Internet and a telehealth care system to strengthen interactions between the community, hospital staff, and family members through virtual video, telephone, and visitor management can effectively improve family members’ satisfaction and reduce patient transfer costs and length of hospitalization.¹¹ Several medical institutions in Taiwan have implemented telehealth care systems, allowing older and chronic patients to monitor physiological information at home and within the community to understand their health status and prevent disease. In addition, such systems can aid caregivers or family members in improving the quality of care for older adults. Therefore, telehealth care systems are currently among the most critical medical services. Monitoring the health status and home security of older adults using telehealth devices can help maintain a good quality of life and reduce the burden on their families, as well as the cost of care.

Currently, a considerable amount of research exists on adopting healthcare technology from the perspectives of healthcare professionals as users.¹² However, there is still a lack of an in-depth understanding of the motivations and needs associated with using telecare technology for older adults or those with chronic diseases.¹³ Therefore, when the chronically ill or the older adults use remote care technology, health care workers must explore how to utilize healthcare-related information and communication technology to promote these individuals’ acceptance of remote care technology. To ensure that the telehealth care system achieves the aforementioned effects to aid such patients in the future, this study explored the experiences of and related factors in the acceptance of information technology among middle-aged and older patients who are chronically ill using a telehealth care system. To successfully extend life, it is necessary to improve the quality of life for individuals in their later years.

An information system’s success is determined by its user, whose behavioral attitudes and intentions may be affected by it.¹⁴ According to the technology acceptance model (TAM), the user’s cognitive evaluation of whether the system is conducive to promoting work effectiveness and ease of use will affect his subsequent attitudes and intentions.¹⁵ However, the information system success model (ISSM) focuses on the relationship between system output quality, user satisfaction, and benefits. No additional context for the impact on user attitudes, behaviors, or intentions is available.¹⁶ Therefore, to fill this research gap, this study focuses on middle-aged and older patients and attempts to integrate the concepts of TAM and ISSM with the application of a telehealth care system for chronic disease management in remote areas during the COVID-19 pandemic.

Methods

Study sample

This study’s sample included middle-aged and older chronically ill patients with diabetes and hypertension who lived in six townships in eastern Taiwan (three in Hualien County and three in Taitung County) and used the telehealth care system. The sampling conditions included chronic patients who were: (1) medically diagnosed with diabetes or hypertension; (2) aged 40 years or older and using a telehealth care system; (3) able to speak Chinese, Taiwanese, or Hakka; and (4) willing to participate in this study.

Conceptual framework and hypothesis

Although this study is primarily based on the TAM, influential external variables may include factors such as system characteristics, user characteristics, and organizations. Therefore, some scholars have also proposed the use of “information quality (INQ),” “system quality (SYQ),” and “service quality (SEQ)” as well as the external variables of “perceived usefulness (UF)” and “perceived ease of use (EOU)” for study and discussion.¹⁷

Therefore, this study adopts the ISSM and believes that “information quality,” “system quality,” and “service quality” will directly affect the users’ satisfaction and, ultimately, their “intent to use (INT).”

Before adopting new technology, users self-assess to determine whether their lives will benefit substantially from it.¹⁴ As previous research has demonstrated, the higher the perceived usefulness of a telehealth care system, the higher the intention to use it. Similarly, the higher the perceived ease of use of the telecare system, the higher the intention to use it.¹⁵ Figure 1 illustrates this study’s conceptual framework.

Figure 1.

Conceptual framework.

This study proposes the following research hypotheses:

“Information quality” has a positive relationship with “Perceived usefulness”; that is, the better the “Information quality” of the telehealth care system, the higher its “Perceived usefulness.”

“Information quality” has a positive impact on “Perceived ease of use”; that is, the better the “Information quality” of the telehealth care system, the higher its “Perceived ease of use.”

“System quality” has a positive relationship with “Perceived usefulness”; that is, the better the “System quality” of the telehealth care system, the higher its “Perceived usefulness.”

“System quality” has a positive impact on “Perceived ease of use”; that is, the better the “System quality” of the telehealth care system, the higher its “Perceived ease of use.”

“Service quality” has a positive impact on “Perceived usefulness”; that is, the better the “Service quality” of the telehealth care system, the higher its “Perceived usefulness.”

“Service quality” has a positive impact on “Perceived ease of use”; that is, the better the “Service quality” of the telehealth care system, the higher its “Perceived ease of use.”

“Perceived ease of use” has a positive impact on “Perceived usefulness.”

“Perceived usefulness” has a positive relationship with “Intent to use.”

“Perceived ease of use” has a positive effect on “Intent to use.”

Study tools

In this study, a structured questionnaire was used for data collection. Its content was revised with reference to domestic and foreign literature. The content of the questionnaire included: (1) basic demographic information such as sex, age, marriage, education level, and primary caregiver identification; (2) the ISSM.^14,18–19; and (3) the TAM.¹⁵

The ISSM contains five items in the information quality dimension (for example, the ability to provide what the patients need and timely, correct, or complete information); four items in the system quality dimension (for example, all functions work normally, fast connection speed, system stability, and fast query and archive speed); and four items in the service quality dimension (for example, can help chronic patients solve problems with the system or equipment). A five-point Likert scale was used (1 = strongly disagree, 5 = strongly agree), with higher scores representing higher levels of agreement. The TAM consists of four items of perceived usefulness, four items of perceived ease of use, and three items of intent to use. This measure was also scored using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree), with higher scores representing higher levels of agreement.

Five experts and scholars were invited to conduct a validity appraisal of the questionnaire. Each questionnaire item was scored on the three dimensions of “importance,” “text clarity,” and “appropriateness,” and the title words were modified. After pre-testing the revised questionnaires, formal questionnaires were used to collect the data.

Data collection

This study was reviewed and approved by the Research Ethics Committee. The study trained interviewers who visited the homes of chronic patients using the telehealth care system. Patients were informed regarding the purpose of the research, and the data collection has obtained the respondents' verbal consent and filled in the consent form. Face-to-face interviews were used to collect the questionnaire data.

Results

A total of 545 chronically ill patients used the telehealth care system, and 541 responded to the survey. Four questionnaires contained incomplete or inconsistent answers, so they were excluded from the analysis. In total, 537 valid questionnaires were recovered with a recovery rate of 99%.

Approximately 59% (317) of the respondents were women. A total of 35.8% (192 people) were 61–70 years old, and 33.5% (180 people) were over the age of 71.63.7% (342 people) of the participants were married, and 22.5% (121 people) were widowed. In total, 44.7% (240 people) were primary school educated, and 22.3% (120 people) were high school educated. 43.8% (235 people) of the participants listed their primary caregiver as their husband or wife, and 32.6% (175 people) were primarily cared for by children. Diabetes and hypertension comorbidities were present in 72.1% (387 patients), and 18.4% (99 patients) had diabetes. 30% (161 people) lived in D townships, and 19% (102 people) lived in C townships (Table 1).

Table 1.

Demographic analysis (N = 120).

Variable	n	%
Sex
Male	220	41.0
Female	317	59.0
Age group
41–50	40	7.4
51–60	125	23.3
61–70	192	35.8
71 and above	180	33.5
Marital status
Single	32	6.0
Married	342	63.7
Divorced	42	7.8
Widowed	121	22.5
Education level
Illiterate	48	8.9
Primary school	240	44.7
Secondary school	99	18.4
High school	120	22.3
College and above	30	5.6
Primary caregiver
None	100	18.6
Husband or wife	235	43.8
Child	175	32.6
Brothers or sisters	13	2.4
Parents	9	1.7
Foreign workers	5	0.9
Disease type
Hypertension	51	9.5
Diabetes	99	18.4
Comorbidities	387	72.1
Townships
A	98	18.2
B	79	14.7
C	102	19.0
D	161	30.0
E	78	14.5
F	19	3.5

Demographic variable differences in each dimension

An independent samples t-test was used to test the sex differences in each dimension. No statistically significant differences were found (Table 2).

Table 2.

Independent samples T test results for sex.

Dimension	Sex	n	M	SD	t	p
Information quality	Male	220	4.446	0.6248	−0.501	535
Information quality	Female	317	4.473	0.5754
System quality	Male	220	4.368	0.7049	−0.170	535
System quality	Female	317	4.379	0.6851
Service quality	Male	220	3.622	0.4849	−0.861	432.835
Service quality	Female	317	3.657	0.4288
Perceived usefulness	Male	220	4.438	0.6315	−0.945	431.507
Perceived usefulness	Female	317	4.487	0.5560
Perceived ease of use	Male	220	4.384	0.6323	−0.626	535
Perceived ease of use	Female	317	4.418	0.6059
Intent to use	Male	220	4.357	0.7332	−0.836	535
Intent to use	Female	317	4.408	0.6662

A one-way analysis of variance (ANOVA) was used to explore the differences in each dimension for different demographic variables. If significant differences were found, Scheffe’s post-hoc test was used. Statistically significant differences were only found for variables such as age group, marital status, and township (Table 3).

Table 3.

Differences of demographic variables in each dimension.

Dimension	Variable	F	p	scheffe’s post-hoc test
Information quality	Age group	9.050	0.000	51 and above > 41–50
	Marital status	3.986	0.008	Married > divorced
	Education level	0.797	0.528	—
	Primary caregiver	2.369	0.038	—
	Disease type	0.024	0.976	—
	Townships	20.536	0.000	A > B,C,D,F,D > E,E > F
System quality	Age group	8.896	0.000	61 and above >41–60
	Age group	2.267	0.080	—
	Education level	0.979	0.418	—
	Primary caregiver	1.151	0.332	—
	Disease type	0.019	0.981	—
	Townships	18.541	0.000	1 > B,C,D,E,F,B,C,E > D
Service quality	Age group	6.494	0.000	61 and above > 41–50
	Marital status	3.509	0.015	—
	Education level	0.616	0.652	—
	Primary caregiver	3.115	0.009	—
	Disease type	0.145	0.865	—
	Townships	19.206	0.000	A > C,D,E,F,B > D,F,E > D,F
Perceived usefulness	Age group	8.243	0.000	51以上 > 41–50
	Marital status	4.016	0.008	Married > divorced
	Education level	1.547	0.187	—
	Primary caregiver	1.868	0.098	—
	Disease type	1.137	0.322	—
	Townships	16.243	0.000	A > B,C,D,E,F,C > D
Perceived ease of use	Age group	5.972	0.001	71以上 > 41–50
	Marital status	4.658	0.003	Divorced > married
	Education level	0.983	0.416	—
	Primary caregiver	1.736	0.125	—
	Disease type	0.513	0.599	—
	Townships	18.212	0.000	A > B,C,D,E,F,B > D
Intent to use	Age group	3.480	0.016	71 and above > 41-50
	Marital status	6.277	0.000	Married > divorced
	Education level	0.943	0.438	—
	Primary caregiver	3.110	0.009	—
	Disease type	0.321	0.725	—
	Townships	16.103	0.000	A > B,D,F,C > D,F,E > D,F

Reliability and validity of confirmatory factor analysis (CFA)

Standardized factor loading should ideally be higher than the threshold of 0.7, but values above 0.6 are acceptable.²⁰ The standardized factor loading in this study was between 0.65 and 0.93, indicating that the reliability of each item is acceptable. A square multiple correlation (SMC) greater than 0.5 is ideal, whereas 0.36 is acceptable.²¹ In this study, the SMC ranged from 0.43 to 0.90. The composite reliability (CR) of the study components was between 0.90 and 0.96, all exceeding 0.7, which met the standard recommended by research.²² The scale demonstrated good internal consistency for each facet.

Finally, the average variance extracted (AVE) calculates the variance explanatory power of each measured variable in relation to the latent variables. The higher the coefficient value, the better the discriminative and convergent validity of the results. This study’s AVE was between 0.70 and 0.82, which was greater than 0.5 and in line with empirical standards.²² Each facet of the questionnaire was shown to have good convergent validity.

The above analysis indicates that all index coefficients are in line with the standards suggested by past scholars; therefore, the measurement variables in this study are acceptable, and the research model’s internal consistency is good. (Table 4)

Table 4.

Measurement mode result analysis.

Dimension	Item	Significance of estimated parameters				Item reliability		Construct reliability	Convergence validity
Dimension	Item	Unstd	SE	Unstd/S.E	p-value	Std	SMC	CR	AVE
INQ	INQ01	1.00	—	—	—	0.88	0.77	0.96	0.82
	INQ02	1.03	0.032	32.495	0.000	0.92	0.84	—	—
	INQ03	1.06	0.033	32.552	0.000	0.92	0.85	—	—
	INQ04	1.04	0.033	31.499	0.000	0.92	0.84	—	—
	INQ05	1.03	0.035	29.603	0.000	0.89	0.79	—	—
SYQ	SYQ01	1.00	—	—	—	0.84	0.70	0.936	0.78
	SYQ02	0.99	0.04	28.19	0.000	0.91	0.83	—	—
	SYQ03	0.99	0.04	27.88	0.000	0.91	0.83	—	—
	SYQ04	0.92	0.04	25.77	0.000	0.87	0.76	—	—
SEQ	SEQ01	1.00	—	—	—	0.88	0.77	0.95	0.82
	SEQ02	1.05	0.03	33.62	0.000	0.93	0.87	—	—
	SEQ03	1.09	0.03	32.49	0.000	0.92	0.85	—	—
	SEQ04	1.04	0.04	29.70	0.000	0.89	0.79	—	—
UF	UF01	1.00	—	—	—	0.91	0.82	0.90	0.70
	UF02	1.06	0.03	35.19	0.000	0.92	0.85	—	—
	UF03	0.90	0.03	26.61	0.000	0.82	0.68	—	—
	UF04	1.02	0.06	17.74	0.000	0.66	0.43	—	—
EOU	EOU01	1.00	—	—	—	0.89	0.79	0.91	0.72
	EOU02	0.99	0.03	30.42	0.000	0.89	0.79	—	—
	EOU03	0.98	0.03	33.08	0.000	0.93	0.86	—	—
	EOU04	0.67	0.04	17.21	0.000	0.65	0.42	—	—
INT	INT01	1.00	—	—	—	0.89	0.79	0.93	0.82
	INT02	0.99	0.03	33.04	0.000	0.93	0.87	—	—
	INT03	0.99	0.03	30.28	0.000	0.89	0.90	—	—

Unstd: Unstandardized factor loadings; S.E: Standardized Error; Std: Standardized factor loadings; SMC: Square Multiple Correlations; CR: Composite Reliability; AVE: Average Variance Extracted.

Structural equation model analysis

The fit index of this study refers to the research results of 194 compiled international academic journals (Social Sciences Citation Index) papers and uses the nine most widely used fit indices to report model fitness.²³ This study’s model fit was χ²/df = 2.75, with a root mean square error of approximation (RMSEA) = 0.06, standardized root mean square residual (SRMR) = 0.05, non-normed fit index (NNFI) = 0.94, comparative fit index (CFI) = 0.95, goodness-of-fit index (GFI) = 0.96, and adjusted goodness-of-fit index (AGFI) = 0.95. The structural model fit was consistent with the standard and was considered to be good (Table 5).

Table 5.

Structural model fit.

Model fit	Criteria	Model fit of research model
Satorra-bentler χ²	The small the better	660.04
DF	The large the better	240.00
Normed chi-sqr (χ²/DF)	1<χ²/DF < 3	2.75
RMSEA	<0.08	0.06
SRMR	<0.08	0.05
NNFI	>0.9	0.94
CFI	>0.9	0.95
GFI	>0.9	0.96
AGFI	>0.9	0.95

Validation of structural equation models

The path coefficient results in Table 6 show that information quality (INQ) (b = 0.32, p < .001), system quality (SYQ) (b = 0.12, p = .004), service quality (SEQ) (b = 0.33, p < .001), and perceived ease of use (EOU) (b = 0.21, p < .001) all significantly affected perceived usefulness (UF). Information quality (INQ) (b = 0.14, p = .029), system quality (SYQ) (b = 0.49, p < .001), and service quality (SEQ) (b = 0.33, p < .001) significantly affected perceived ease of use (EOU). Perceived usefulness (UF) (b = 0.57, p < .001) and perceived ease of use (EOU) (b = 0.31, p < .001) significantly affected intention to use (INT).

Table 6.

Regression coefficients.

DV	IV	Unstd	Std. Beta	SE	p-value	Std. RC	R ²
UF	INQ	0.32	0.12	0.04	0.000	0.33	0.87
	SYQ	0.12	0.52	0.04	0.004	0.14	—
	SEQ	0.33	0.27	0.04	0.000	0.31	—
	EOU	0.21	0.25	0.04	0.000	0.25	—
EOU	INQ	0.14	0.33	0.07	0.029	0.12	0.72
	SYQ	0.49	0.14	0.06	0.000	0.52	—
	SEQ	0.33	0.31	0.06	0.000	0.27	—
INT	UF	0.57	0.53	0.07	0.000	0.53	0.70
	EOU	0.31	0.34	0.06	0.000	0.34	—

Unstd: Unstandardized. Coefficients; Std: Standardized Beta Coefficents; S.E: Standardized Error; Std. RC: Standardized regression cofficient.

Thus, the structural pattern hypothesis of this study was established (Figure 2). INQ, SYQ, and SEQ of the telehealth care system had a significant positive mediating effect on the EOU and UF of the TAM and a significant positive impact on chronically ill patients’ INT. EOU also directly affects UF. In short, if chronically ill patients perceive telehealth care systems as simple and easy to use, they will find them even more helpful. The INQ, SYQ, SEQ, and EOU were 87% predictive of UF. The INQ, SYQ, and SEQ were 72% predictive of EOU. UF and EOU were 70% predictive of explained INT.

Figure 2.

Overall structure model. Note: INQ, information quality; SYQ, system quality; SEQ, service quality; EOU, perceived ease of use; UF, perceived usefulness; INT, intention to use.

Discussion

This study found that information, system, and service quality have a significant impact on perceived ease of use and usefulness. In other words, when chronically ill patients believe that a telehealth care system’s information, system, and service quality are good, they will feel that the telehealth care system is easy to operate and can be learned and used without much effort. This may not only help these patients better control their chronic conditions, but it may also improve their quality of life.

Therefore, this study supports the discourse on the ISSM and TAM. If users are willing to use an information technology system, they must feel that these systems have good functions, are easy to use, offer correct information, and have good service quality. Most importantly, users find these systems helpful. In addition, this study supports the statement that ease of use affects the TAM’s proposed usefulness. It is necessary to help users perceive information technology as easy to learn and easy to use because the friendly design is the key point through which information technology systems become increasingly popular. Therefore, the functional interface of the telehealth care system, including the login, search, record, and service features, must use a straightforward design.

Finally, this study accepts the hypothesized structural equation model and finds that information, system, and service quality are influenced by the mediating effects of the TAM’s perceived ease of use and usefulness, thereby significantly increasing chronically ill patients’ use of telehealth care systems. In this study, patients were allowed to transmit physiological data such as blood pressure and blood sugar from their own homes to a remote health care system server. In addition, similar to other studies, telephone consultations and video services were provided.^24,25 These results show that a useful and easy-to-use telehealth care system will increase users' willingness to continue to use it. Evaluations of good quality in all aspects are crucial, particularly following chronically ill patients’ use of the telehealth care system, because these comprehensive evaluations also affect patients’ willingness to continue using the system.

Therefore, the telehealth care system provider, whether a medical institution, health institution, or equipment manufacturer, must pay close attention to the users’ responses and feedback. Moreover, actively dealing with any issues reported by the users' response will promote their continued use of the system.

Conclusion

From the end of 2019 to the end of 2021, this plan trained volunteers in remote areas to become health gatekeepers; that is, semi-professional health care workers who assisted and taught chronically ill patients in remote areas to use the telehealth care information system. Patients were able to measure blood pressure, blood sugar, blood oxygen, heart rate, and other information at home. Then, they used mobile phones, tablets, or gateways to automatically upload information to the hospital cloud platform, which was capable of generating continuous physiological measurement records, analyzing cases, and proactively monitoring for abnormal measurements. If an abnormal situation was present, the platform proactively notified the patient, family members, and care team by message, phone line, email, or customer service contact so the medical staff could keep abreast of patients’ status when they were at home.

Devices used by chronically ill patients at this stage were funded by the Taiwanese government’s demonstration program. In the future, if the coverage of chronic diseases could be expanded and the telehealth system could be offered to more patients with other chronic diseases by taking a comprehensive inventory of the availability of various resources and considering the openness, connectivity, and continuity of back-end data, it will be possible to maximize health management benefits.

The COVID-19 pandemic has accelerated the progress of telehealth care and prompted changes in the healthcare system, making telehealth care play an essential, burden-sharing role. Medical institutions provide continuous care for chronically ill patients in remote areas through a telehealth system, which can solve the long-standing problem of uneven distribution of medical resources.²⁶ It is recommended that medical institutions, long-term care institutions, and community centers use remote health care in the future for public or patient health management. In addition, expanding the use of innovative technologies such as 5G mobile communication, Internet of Things, artificial intelligence, or big data analysis would allow the public to use professional medical diagnostic services through telehealth-related equipment without entering a hospital or clinic. In this way, the risk of cluster infection will be reduced, limited physical medical resources would be reserved for severely and acutely ill patients, and smart technology could be used to provide more comprehensive and dedicated medical care.²⁷

Footnotes

Acknowledgements

We express our gratitude for Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan (R.O.C.) for providing research funds and all participating without whom this study could not have been successfully completed.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taiwan (R.O.C.).

Ethical approval

This study was reviewed and approved by the Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation Research Ethics Committee (IRB 108–209-B).

ORCID iD

Yu Li Lan

References

Chauhan

Galwankar

Arquilla

, et al. Novel coronavirus (COVID-19): leveraging telemedicine to optimize care while minimizing exposures and viral transmission. J Emergencies Trauma Shock 2020; 13(1): 20–24. DOI: 10.4103/JETS.JETS_32_20

Corbett

Opladen

Bisognano

. Telemedicine can revolutionize the treatment of chronic disease. Int J Cardiol Hypertens 2020; 7: 100051. DOI: 10.1016/j.ijchy.2020.100051

De la TD

López-Coronado

Vaca

, et al. Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: a systematic review. Telemed J E Health 2015; 21(2): 81–85. DOI: 10.1089/tmj.2014.0053

Snoswell

Taylor

Comans

, et al. Determining if telehealth can reduce health system costs: scoping review. J Med Internet Res 2020; 22(10): e17298. DOI: 10.2196/17298

Tebeje

Klein

. Applications of e-health to support person-centered health care at the time of covid-19 pandemic. Telemed J E Health 2021;27(2):150–158. DOI: 10.1089/tmj.2020.0201

Wakefield

Holman

Ray

, et al. Effectiveness of home telehealth in comorbid diabetes and hypertension: a randomized, controlled trial. Telemed E-Health 2011; 17(4): 254–261. DOI: 10.1089/tmj.2010.0176

Lin

CCC

Dievler

Robbins

, et al. Telehealth in health centers: Key adoption factors, barriers, and opportunities. Health Aff 2018; 37(12): 1967–1974. DOI: 10.1377/hlthaff.2018.05125

Chou

Yan

Lin

, et al. A pilot study of the telecare medical support system as an intervention in dementia care: the views and experiences of primary caregivers. J Nurs Res 2021; 20(3): 169–180. DOI: 10.1097/jnr.0b013e318263d916

Chen

Liang

Hsieh

, et al. The effect of tele-healthcare intervention on preventable hospitalizations and healthcare utilization in Taiwan. Taiw J Pub Health 2016; 35(5): 524–541. DOI: 10.6288/TJPH201635105059

10.

Peters

Kooij

Lenferink

, et al. The Effect of telehealth on hospital services use: systematic review and meta-analysis. J Med Internet Res 2021; 23(9): e25195. DOI: 10.2196/25195

11.

Perez

Neubauer

Marshall

, et al. Barriers and benefits of information communication technologies used by health care aides. Appl Clin Inform 2022; 13(1): 270–286. DOI: 10.1055/s-0042-1743238

12.

Brewster

Mountain

Wessels

, et al. Factors affecting front line staff acceptance of telehealth technologies: a mixedmethod systematic review. J Adv Nurs 2013; 70(1): 21–33. DOI: 10.1111/jan.12196

13.

Peeters

De Veer

Hoek

LVD

, et al. Factors influencing the adoption of home telecare by elderly or chronically ill people: a national survey. J Clin Nurs 2012; 21(21–22): 3183–3193. DOI: 10.1111/j.1365-2702.2012.04173.x

14.

DeLone

McLean

. The delone and mclean model of information system success: a ten-year update. J Manage Inform Syst 2003; 19(4): 9–30. DOI: 10.1080/07421222.2003.11045748

15.

Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989; 13(3): 319–340. DOI: 10.2307/249008

16.

Seddon

. A Respecification and extension of the delone and mclean model of is success. Inform Syst Res 1997; 8(3): 215–317. DOI: 10.1287/isre.8.3.240

17.

Chen

Tsai

. Determinants of behavioral intention to use the personalized location-based mobile tourism application: an empirical study by integrating TAM with ISSM. Future Gener Comp Sy 2017; 96: 628–638. DOI: 10.1016/j.future.2017.02.028

18.

Zeithaml

Berry

Parasuraman

. The behavioral consequences of service quality. J Marketing 1996; 60(2): 31–46. DOI:10.2307/1251929

19.

Lee

Strong

Kahn

, et al. AIMQ: a methodology for information quality assessment. Inform Manage 2002; 40(2): 133–146. DOI: 10.1016/S0378-7206(02)00043-5

20.

Chin

W.W.

. Commentary: issues and opinion on structural equation modeling. MIS Quartetly 1998; 22(1): 7–16

21.

Hooper

Coughlan

Mullen

. Structural equation modelling: Guidelines for determining model fit. Electron J Bus Res Methods 2008; 6(1): 53–60. DOI: 10.21427/D7CF7

22.

Hair

Black

Babin

, et al. Multivariate Data Analysis: A Global Perspective. 7th ed. New York, NY: Macmillan, 2010.

23.

Jackson

Gillaspy

Jr Purc-Stephenson

. Reporting practices in confirmatory factor analysis: an overview and some recommendations. Psychol Methods 2009; 14(1): 6–23. DOI: 10.1037/a0014694

24.

Czaja

Lee

Arana

, et al. Use of a telehealth system by older adults with hypertension. J Telemed Telecare 2014; 20(4): 184–191. DOI: 10.1177/1357633X14533889

25.

Aberer

Hochfellner

Mader

. Application of telemedicine in diabetes care: The time is now. Diabetes Ther 2021; 12: 629–639. DOI: 10.1007/s13300-020-00996-7

26.

Tseng

Lin

. Hospital-based integrated care model for chronic diseases. Hospita 2019; 52(1): 57–67, https://reurl.cc/44EQLK

27.

Chen

Lan

Yang

, et al. Exploring the impact of a telehealth care system on organizational capabilities and organizational performance from a resource-based perspective. Int J Environ Res Public Health 2019; 16(20): 3988. DOI: 10.3390/ijerph16203988