Telemonitoring (TM) is defined as a method of remotely monitoring vital parameters by individuals outside of a healthcare setting, and electronically transmitting them to the healthcare providers (1–4). It uses audio, video, and other telecommunications and electronic information processing technology to monitor patient health status remotely (5). Telemonitoring systems are increasingly implemented for the follow-up of chronic diseases because they improve the quality of healthcare and allow the capturing of patients’ clinical parameters easily and continuously(6).
Chronic disease is defined by the World Health Organization (WHO) as “being of long duration, generally slow in progression and not passed from person to person”(7). Cardiovascular diseases, Chronic Obstructive Pulmonary Disease (COPD), cancer, diabetes, and hypertension are significant global, national, and personal health problems, where cardiovascular diseases are the leading cause of death globally (8). Cancer is the second leading cause of death globally(9). COPD is the third leading cause of death worldwide(10). Over 422 million people worldwide have diabetes and 1.5 million deaths are directly attributed to diabetes each year in the world(11). About 1.28 billion adults aged 30–79 years worldwide have hypertension(12). The majority, 90% of COPD deaths in those under 70 years of age (10), the majority of diabetic patients (11), and two-thirds of hypertension patients(12) are in low-and middle-income countries.
Chronic illnesses require continuous medical attention as well as patient self-management. However, immediate and continuous follow-up of chronic patients in Ethiopia remained low due to geographical barriers and limited access to health professionals (13, 14). Telemonitoring solutions have been found to overcome these barriers(15).
TM technologies emerge as an effective approach for better control of common conditions, complications, and life-threatening events, mortality, and improving quality of life and the overall standard of living and services(16, 17). Among Digital Health Solutions for Health Workers Establishing Remote Patient Monitoring services/programs at public hospitals is a Strategic Initiatives planned by the Ministry of Health of Ethiopia(18). However, there is limited information on the intention to use telemonitoring among nurses and professionals in Ethiopia.
Various studies indicated that the intention to use telemonitoring technologies among healthcare workers is low globally(17). The problem getting worse in Africa due to high technology resistance from healthcare providers as a result of technology anxiety(19, 20). Similarly, the intention to use health technologies in Ethiopian healthcare settings is low (40%)(21). Another study also supported this, where willingness to use telemedicine is low (46.5%) (22).
Low intention to use telemonitoring technology leads to low adoption of telemonitoring systems to support chronic diseases(23). The consequences of low adoption of telemonitoring systems are high hospitalization rate, increased cost of care, inhibited self-care management, and low health care access which leads to poor management of chronic disease and increased burden of diseases, disability, and premature death(19, 24). Different studies showed that Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), facilitating condition (FC), Hedonic Motivation (HM), and Habit (HA)(23, 25–27) are determined the status of intention to use telemonitoring among nurses and physicians.
As far as my intensive search, a few studies are conducted on the intention to use telemonitoring globally, and not known in Ethiopia and those literature had limitations such as participation bias, small sample size, unable to indicate moderator effect variables, disagreement gap, and are outdated (23, 27). Therefore, the present study aimed to assess nurses’ and physicians’ intention to use telemonitoring and associated factors to support chronic patients among nurses and physicians at Bahir Dar City public hospitals. This study will be important to improve digital health technologies utilization in managing chronic illness and update literature in the area.
Currently, chronic diseases that need follow-up and treatment become among the leading health system challenges in Ethiopia and its districts (28, 29). To manage these and other important diseases, the Ethiopian government considered telemonitoring and related health technologies as one of the primary health system strategies (18, 30).
Prior studies have demonstrated that the use of telemonitoring system implementation depends on the user's intention. Therefore, it is crucial to conduct studies on health professionals’ intention to use telemonitoring systems (27, 31, 32). Based on my literature search, there is limited literature on the intention to use telemonitoring among health professionals to support chronic diseases in Ethiopia and its districts. Therefore, knowing the intention to use telemonitoring and its challenges among health workers will be important to patients with chronic diseases, program owners, policymakers, and researchers to take evidence-based interventions, and conduct further studies to support prevention, diagnosis, and treatment follow-up.
Theoretical background and hypothesis
The Unified Theory of Acceptance and Use of Technology was developed by Venkatesh and Davis in 2003(25) and later modified by Venkatesh in 2012(26). The UTAUT is used to understand better why users accept or reject a given technology, and how user acceptance can be improved through technology design. This model was extracted from eight previous theoretical models that include the theory of reasoned action (TRA), the Social Cognitive Theory (SCT), the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), the Motivational model(MM), the Model of PC utilization (MPCU), Combined TAM and TPB (C-TAM-TPB) and Innovation Diffusion Theory (IDT)(25).
Selecting the appropriate model as a theoretical basis to best explain users’ behavior toward the technology under investigation is vital to providing answers to the research questions. In this study, UTAUT2 model was selected as the primary theoretical framework for this investigation. Information systems research has shown that the UTAUT was extremely successful in assessing factors that influence the intention to use different technologies, with a high explanatory power(33).
In this study, the UTAUT2 model was proposed by removing the experience (moderator), price value (exogenous variable), and actual use (endogenous variable) from the original UTAUT2 model. Since telemonitoring technology has not yet been fully implemented throughout Ethiopia and nurses and physicians have not yet fully utilized the technology, this study will not measure actual use behavior, which was an endogenous variable in the original UTAUT2 mode. Similarly, the TM is not implemented in Ethiopia all nurses and physicians were not familiar with this technology, thus, the experience was not included as a moderator. In the case of resources to adopt TM, which was fulfilled by the government price value is not the concern of nurses and physicians, thus, the price value was not included as an exogenous variable. Therefore, the current proposed UTAUT2 model constructs of intention to use telemonitoring are Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), and Habit (HA), and two moderator variables age and gender(26) [Figure 1].
Performance Expectancy (PE):
Performance expectancy is “the degree to which an individual believes that using the system will help him or her to gain in job performance” (25). The UTAUT models capture the concept of performance expectancy from constructs of five models perceived usefulness (TAM/TAM2 and C-TAM-TPB), extrinsic motivation (MM), job-fit (MPCU), relative advantage (IDT), and outcome expectations (SCT) (25, 34, 35). In Spain, Bilbao Primary Care (23) and Don Ostia University Hospital (27), PE has a positive association with the intention to use telemonitoring. In Bangladesh(36), and Ethiopia(37) PE has a significant effect on the intention to use eHealth. In India(33), PE has a significant influence on behavioral intention to use the proposed mobile-based IT solution. In comparable study conducted in Ethiopia (38) showed that PE has no direct association with intention to use telemedicine. Therefore, to test the effect of PE on Intention to use TM, the following hypothesis is generated.
H1: Performance expectancy will have a positive effect on the intention to use TM.
Effort expectancy (EE)
Effort expectancy is “the degree of ease associated with the use of the system”(25). UTAUT models capture the concept of effort expectancy from perceived ease of use (TAM/TAM2), complexity (MPCU), and ease of use (IDT) (25, 34, 35). A systematic review conducted among twelve studies showed that Effort Expectancy was found to be the strongest predictor of technology acceptance(34). Studies conducted in Spain, Bilbao Primary Care (23) EE has a positive association with intention to use telemonitoring. A study conducted in India(33) showed that EE was found to significantly influence behavioral intention to use the proposed mobile-based IT solution. another study conducted in Ethiopia (38) and Indonesia(39) showed that EE significantly affects the intention to use Telehealth/telemedicine. In a comparable study conducted in Spain, Don Ostia University Hospital (27) EE does not influence the intention to use telemonitoring. Therefore, to test the effect of EE on Intention to use TM, the following hypothesis is generated.
H2: Effort Expectance will have a positive effect on the intention to use TM.
Social Influences (SI)
Social Influence is “the degree to which an individual perceives that important others believe he or she should or should not use the new system” (35, 40). Social influence as a direct determinant of intention to use is represented as the subjective norm in TRA, TAM2, TPB/DTPB and C-TAM-TPB, social factors in MPCU, and image in IDT(25). A studies conducted in Spain, Bilbao Primary Care (23) and Don Ostia University Hospital (27) social influences were not positively associated with the intention to use telemonitoring. Another study conducted in Indonesia(39) and Ethiopia (38) showed that Social Influence is not significantly associated with intention to use telehealth. Comparable studies conducted in the original UTAUT model (25) showed a strong association with the intention to use information technology. Similarly, studies conducted in Taiwan(41) and Senegal (42) showed that social influence has a strong association on intention to telemedicine. Another study conducted in India(33) showed that SI was found to significantly influence on intention to use the proposed mobile-based IT solution. Therefore, to test the effect of SI on Intention to use TM, the following hypothesis is generated.
H3: Social Influences will have a positive effect on the intention to use TM.
Facilitating Conditions (FC)
Facilitating conditions is “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system”(25) (35). This definition captures constructs from three models perceived behavioral control (TPBI DTPB, C-TAM-TPB), facilitating conditions (MPCU), and compatibility (IDT). According to a study done in Spain, Bilbao Primary Care (23) and Don Ostia University Hospital (27) showed that Facilitating conditions are the significant predictor of intention to use Telemonitoring. Similarly, studies conducted in Indonesia(39) and Ethiopia (37) indicate that the facilitating conditions are strongly associated with to use of eHealth/telehealth technologies. Another study conducted in India(33) showed that FC was found to significantly influence on intention to use the proposed mobile-based IT solution. Comparable studies conducted in Ethiopia(38) showed that facilitating conditions did not have a significant influence on the intention to use telemedicine respectively. Therefore, to test the effect of FC on the intention to use TM, the following hypothesis is generated.
H4: Facilitating Conditions will have a positive effect on the intention to use TM.
Hedonic Motivation (HM)
Hedonic motivation is described as the extent to which a person thinks funny or enjoyable due to employing a particular technology (26). The modified UTAUT showed that hedonic motivation has a positive influence on behavioral intention(26). Studies conducted in China(43), and Pakistani(44) showed that hedonic motivation had a significant effect on the intention to use telemedicine. A study conducted in the United States showed that hedonic motivation had a significant effect on mobile-based app acceptance(45). A study conducted in Malaysia showed that HM had a significant effect on intention towards smartwatches for health and fitness monitoring(46). Studies conducted in Italy revealed that hedonic motivation was a significant factor in the use of robot technologies(47). Comparable studies conducted in Ethiopia showed that HM did not have a significant influence on behavioral intention to use EMR(21). Therefore, to test the effect of HM on the intention to use TM, the following hypothesis is generated.
H5: Hedonic motivation will have a positive effect on the intention to use TM.
Habit (HA)
Habit refers to automating behavior from initial learning to regular use of technology(26). A study conducted in the United States showed that habit had a significant effect on mobile-based app acceptance(45). In a comparable study conducted in Ethiopia, the habit did not have a significant influence on behavioral intention to use EMR(21). Therefore, to test the effect of HB on the intention to use TM, the following hypothesis is generated.
H6: habits will have a positive effect on the intention to use TM.
Moderator variables of intention to use TM
Moderator is a variable that can affect the direction and strength of association between exogenous and endogenous variables (21). In the UTAUT2 model of context sex, age, and experience variables affect the direction or strength of the relation between the exogenous and endogenous variables (48).
The moderating effect of age
Study conducted in China, the moderator analysis confirmed that different age groups have specific moderating effects on effort expectancy and behavioral intention to use health technology(49). In another study conducted in the Midwestern U.S. state, the effects of PE, EE, SI, HA, and HM on behavioral intention to use health information technology were all moderated by individual age(26). In another study conducted in Asia PE, EE, and SI were moderated by age to behavioral intention to use smart equipment for health(50). Studies in the original model of UTAUT showed that age had moderating effects on PE, EE, and SI on behavioral intention to use information technology(25). Therefore, to test the moderating effect of age on the Intention to use TM, the following hypothesis is proposed:
H7: The influence of PE on the intention to use TM will be moderated by age.
H8: The influence of EE on the intention to use TM will be moderated by age.
H9: The influence of FC on the intention to use TM will be moderated by age.
H10: The influence of SI on the intention to use TM will be moderated by age.
H11: The influence of HM on the intention to use TM will be moderated by age.
H12: The influence of HA on the intention to use TM will be moderated by age.
The moderating effect of gender
A study conducted in China indicated that gender had moderating effects on EE on behavioral intention to use the health system(49). Studies in the original model of UTAUT showed that gender had moderating effects on PE, EE, SI, and FC on behavioral intention to use information technology(25). Another conducted in India showed that EE had a moderating effect on the Intention to Use Mobile-Based Information Technology(33). Similarly, study conducted in the Midwestern U.S. state, the effects of PE, EE, SI, HA, and HM on behavioral intention to use health information technology were all moderated by individual gender(26). Therefore, to test the moderating effect of gender on the intention to use TM, the following hypothesis is proposed:
H13: The influence of PE on the intention to use TM will be moderated by gender.
H14: The influence of EE on the intention to use TM will be moderated by gender.
H15: The influence of FC on the intention to use TM will be moderated by gender.
H16: The influence of SI on the intention to use TM will be moderated by gender.
H17: The influence of HM on the intention to use TM will be moderated by gender.
H18: The influence of HA on the intention to use TM will be moderated by gender.