Health Providers’ Perceptions and Experiences of Using mHealth for Chronic Noncommunicable Diseases: Qualitative Systematic Review and Meta-Synthesis

Introduction

Background

Chronic noncommunicable diseases (CNCDs) are the leading cause of death and disability worldwide and have been recognized as a major challenge for achieving the World Health Organization’s 2030 Agenda for Sustainable Development [1]. The management of CNCDs is a pressing global concern. Simultaneously, the COVID-19 pandemic has greatly affected the provision of care to patients with CNCDs [2]. It has become critically important to find alternative methods to assist people in need of managing their chronic illnesses.

Mobile health (mHealth) refers to a type of health service that applies any mobile device, such as mobile phones, smartphones, PDAs, and devices that work on wireless technology or Bluetooth-compatible devices [3,4]. Owing to the portability of, instantaneous access to, and possibility of direct communication via mHealth, it is increasingly being incorporated into health services, inspiring new models of remote health care delivery and cost-effective solutions for chronic diseases, whose long-term nature and need for continuous monitoring can be positively impacted [5]. Evidence regarding the use of mHealth interventions in improving adherence to treatment; maintaining appointments; collecting data; promoting lifestyle changes [6]; and supporting health providers (HPs) with remote patient care, real-time clinical reference, and digital education is growing [7]. Simultaneously, evidence regarding the efficacy, effectiveness, economics, and clinical preferences of mHealth in the treatment of many chronic diseases is growing [8]. Its performance in extending the reach and capacity of overburdened health care systems attracts immense attention from academics and industries worldwide [9].

Although increasing evidence has shown the potential facilitators of the use of mHealth for CNCDs, the introduction of mobile technology in a medical context is not without challenges [10]. The lack of reimbursement, outcome uncertainty, and data security breaches have undermined the use of mHealth for CNCDs [10-12]. There are still some concerns held by HPs, and many of them have mixed attitudes toward the adoption of mHealth [13,14]. As HPs are the gatekeepers of health services, understanding their perspectives is crucial for the digital transformation of the health care system and the improvement of health care delivery [14].

Several reviews have attempted to investigate the factors impacting HPs’ adoption of mHealth. However, to the best of the authors’ knowledge, no comprehensive qualitative meta-synthesis has been performed to understand HPs’ perceptions and experiences of the use of mHealth for CNCDs. Qualitative meta-synthesis based on the findings of multiple qualitative studies can identify both common and differential factors among studies and generalize their findings to better inform decision-making [15]. Two recent meta-synthesis reviews [16,17] aimed to understand people’s perceptions of mHealth use, but neither covered HPs’ perspective, with one study by Vo et al [16] from the patients’ perspective and the other by Eisapareh et al [17] from the users’ perspective.

Objectives

This study aimed to conduct a qualitative meta-synthesis to identify the factors that HPs perceived or experienced as facilitators of or barriers to mHealth use for people with CNCDs.

Methods

Study Design

The systematic review and meta-synthesis were conducted in accordance with the updated version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) checklist (Multimedia Appendix 1 [18]). The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42022352872).

Search Strategy

A systematic literature search was conducted in the MEDLINE (via Ovid), Embase, Web of Science, Google Scholar, and Cochrane Library (via Ovid) databases. The search terms included 3 categories of keywords: HPs, mHealth, and adoption. All databases were searched from their inception to July 23, 2022, and an updated search was performed on July 19, 2023. The full search strategy for each database is presented in Multimedia Appendix 2. In addition, the reference lists of the included studies and studies cited in previous reviews were screened to identify additional studies.

Studies were required to meet a set of inclusion criteria. Textbox 1 summarizes the inclusion and exclusion criteria.

Identification of Studies

All retrieved records were exported to EndNote (version X9; Clarivate). After excluding duplicates, titles and abstracts were independently screened by 2 reviewers according to the inclusion and exclusion criteria. All abstracts that could potentially meet the inclusion criteria were forwarded to full-text review. Any disagreements between the 2 reviewers over eligibility were reconciled through discussion with a third researcher. Reasons for exclusion were recorded for all excluded studies.

Data Extraction

Data extraction pro forma was developed based on the Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI) data extraction tool [19], and the following data were extracted for each of the included studies: title, authors, date, geographical or cultural setting, participant characteristics, type of mHealth technology, data collection and analysis approaches, and relevant primary qualitative data (themes and quotations). Data extraction was performed by 1 reviewer and checked by a second reviewer.

Quality Assessment

Studies that met the inclusion criteria were appraised for quality by 2 independent reviewers using the standardized critical appraisal instrument from the JBI-QARI [19]. The JBI-QARI comprises 10 questions, each of which is answered with no, yes, or unclear. The final scores were computed according to the number of yes items and rated as follows: low (0-4 items), moderate (5-7 items), and high (8-10 items) [20]. Studies rated as low were excluded. Any disagreement between the reviewers was resolved through a discussion with a third reviewer.

Data Synthesis

This study applied a thematic approach to the qualitative meta-synthesis described by Thomas and Harden [21]. The process has 3 stages. First, the free codes were identified line by line according to the results or findings part of the primary studies. Second, these codes were grouped by comparing their similarities to create descriptive themes. Finally, the descriptive themes were repeatedly checked, and new conceptions, understandings, or assumptions were identified. In this stage, analytic findings (themes and subthemes) were generated, which presented the key findings of the primary studies and provided new views of this field. All 3 stages were completed by 2 reviewers. The data analysis process was subsequently checked to ensure the congruence of the interpretations and the adequacy of the analytic themes.

Results

Study Identified

The database search and hand searches identified 18,242 articles, from which 7609 (41.71%) duplicates were removed. After screening the titles and abstracts, 142 (1.87%) full-text records were reviewed, of which 24 (16.9%) were included in the review. The study selection process is illustrated in Figure 1.

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Characteristics of the Included Studies

The characteristics of the 24 studies included in this review are described in Table 1. mHealth technologies were applied to assist the management of the following CNCDs: mental diseases (depression, anxiety, epilepsy, or multiple sclerosis; n=9, 38%) [22-30], cancer (n=2, 8%) [31,32], heart failure (n=4, 17%) [33-36], asthma (n=1, 4%) [37], diabetes (n=2, 8%) [38,39], chronic pain (n=1, 4%) [40], chronic obstructive pulmonary disease (n=1, 4%) [41], and other chronic diseases (n=4, 17%) [42-45]. The participant sample sizes ranged from 8 to 43. Studies were conducted in 13 countries, namely Ghana [38], the United Kingdom [22,29], Canada [33,36,41], Australia [28,31,34], Ireland [23], Nepal [24], Germany [25], the United States [26,32,35,37,39,40], France [27,42], the Netherlands [43], Sri Lanka [44], Spain [30], and China [45]. The studies were conducted in public or private hospitals, primary care institutions, and home care facilities in urban or rural settings. Data were collected in these studies through semistructured interviews (18/24, 75%) [23-26,28,29,31-36,38,41-45], focus group discussions (8/24, 33%) [22,24,27,28,30,37,40,42], or in-depth interviews (2/24, 8%) [37,39]. The analytic approaches followed in these qualitative studies included thematic analysis (11/24, 46%) [22-24,28,29,31,32,34,35,38,43], content analysis (7/24, 29%) [26,27,33,36,40,41,45], constant comparative method (4/24, 17%) [37,39,42,44], generic inductive approach (1/24, 4%) [30], and no specific method (1/24, 4%) [25].

Assessment of Study Quality

The included papers were critically appraised and found to be of moderate to high methodological quality, with scores of 7 and 10 based on the 10 questions of the JBI-QARI (Multimedia Appendix 3 [22-45]). No study was excluded based on the quality critical appraisal tool. All studies showed congruity between the stated philosophical perspective and the research methodology (question 1). They were rated positively in terms of research methodology (questions 2, 3, 4, and 5). Among the 24 studies, the cultural or theoretical backgrounds of the researchers of 17 (71%) studies were inconsistently reported (question 6). Moreover, 17 (71%) papers rarely stated the influence of the researcher on the research (question 7), and 17 (71%) studies were granted formal ethics approval (question 9). All studies demonstrated adequate representation of participants’ voices (question 8) and concluded rationally (question 10). Overall, the methodological quality of the included studies was verified.

Data Analysis and Meta-Synthesis

The results of the meta-synthesis are presented in this section. Data are presented as a synthesized finding with supporting themes and component subthemes. Summaries of themes and subthemes related to the perceived facilitators of and barriers to the adoption of mHealth by HPs are shown in Tables 2 and 3, respectively.

Perceived Facilitators

The facilitators perceived by HPs were addressed in 22 (92%) of the 24 studies on the use of mHealth technology and included the themes of empowering patient self-management, increasing efficiency, improving access to care, increasing the quality of care, improving satisfaction, and improving the usability of the internet and mobile software (see Table 2).

Empowering Patient Self-Management

Helping Patients Gain Self-Management Knowledge

HPs felt that mHealth increased the accessibility of reliable self-management knowledge for patients to manage their chronic illnesses [33,42].

Making All-Time Remote Monitoring Possible and Better

HPs believed that mHealth provided them with a chance to assist patients with developing self-management skills to make symptom tracking and remote supervision more intensive and real time [22,27,32,33,35,42,43]. Furthermore, patient-collected information received via mobile phones may be more accurate than information collected during a clinic visit because monitoring at more frequent intervals can overcome the memory bias encountered in in-person follow-ups [27,39].

Increasing Adherence

HPs deemed that personalized feedback and automated messages contributed to improving patients’ motivation to manage their condition [34,38], and mHealth enhanced engagement by empowering patients to take a leading role in their care by increasing their sense of responsibility and self-efficacy to improve follow-up and therapy adherence [25,29,30,37,39,41,42].

Increasing Efficiency

Reducing Workload and Stress

Some HPs perceived mHealth as a tool that reduces workload and stress by reducing repetitive actions, expediting the transfer of information, and providing buffer time to properly consider how to handle and respond to a request [22,36,39,41-43]. In addition, HPs regarded mHealth as a useful reminder for HPs and patients on a day-to-day basis that provides convenient recordings to prevent forgetfulness [22,24,33,35,41,45].

Optimizing Work Procedures

HPs indicated that mHealth helped reduce unnecessary face-to-face visits and simplified in-person visits by allowing them to review medical recordings before seeing patients [35,39,41,45].

Facilitating Clinical Practice

HPs perceived that the reliability and objectivity of measurements and the traceability of measurement history of mHealth facilitated the provision of continuous and holistic care to patients [24,27,29,30,42,45]. In addition, HPs indicated that mHealth served as an instant, easily accessible resource for updating their skills and knowledge [23,24].

Improving Access to Care

Providing Flexible Care and Promoting the Continuity of Care

mHealth was perceived by HPs as helping patients obtain more flexible access to care independent of a practice’s opening hours and therapy location, especially in crises or for people in rural areas [23-25,33,35,41,45].

Supporting Family Caregivers to Participate in Caring for Patients

mHealth technologies were perceived by HPs as helping family caregivers deal with the chronic illnesses of patients by providing a method that facilitates timely and accurate communication of clinical information, which could avoid unnecessary medical visits [45].

Increasing the Quality of Care

Conveying Accurate and Credible Information

HPs felt that mHealth promoted accurate communication in chronic disease care, with a greater likelihood of improved outcomes [24,26,30,32,36,37,39,42,45]. For example, HPs perceived that mHealth provided patients with a means to share their symptoms and conditions via a photo and video transfer to prevent subjective judgment or differential perceptions, which might lead to communication errors [33,45].

Allowing Deeper and More Timely Data Analysis

HPs thought that mHealth could help them obtain more actual and direct information about the conditions of their patients via open-ended web-based chat, particularly from those who find direct social interaction to be challenging [39], and obtain patients’ data anytime and anywhere, which promote their insights into treatment and prognosis [23,24,27,30,34,35,38,45].

Improving Satisfaction

Promoting the Physician-Patient Relationship

HPs believed that mHealth could benefit the physician-patient relationship by improving effective communication between them and has the potential to improve patients’ and physicians’ satisfaction [22,33,38,39,41,43-45].

Saving Time and Money

HPs indicated that mHealth could save time and money by avoiding unnecessary hospital and home visitation services and thereby might improve patients’ and physicians’ satisfaction by reducing financial and geographic barriers [26,30,37,41].

Improving the Usability of the Internet and Mobile Software

Promoting the Availability of Reliable Internet Connections in the Health Facility

HPs believed that network infrastructure, such as Wi-Fi, can be a motivator for them to use mobile apps to manage their patients [24,38].

Making the mHealth Software Easy to Use

HPs deemed that mHealth software needs to be simple for them to use; for example, the language should be basic, the software should be visually appealing, and automated self-monitoring through connected wireless devices should be enabled instead of manual input of measures [34,41].

Perceived Barriers

Despite HPs perceiving some facilitators of the use of mHealth technology, they also perceived some barriers to mHealth use. Barriers were discussed in all the 24 included studies and emanated from 8 themes (see Table 3).

Limitation Due to Digital Literacy, Personal Habits, or Health Problems

Limited Digital Literacy

HPs thought that physicians, particularly primary care physicians in rural areas and older physicians, had insufficient knowledge about and experience in using digital health technologies [24,25,28,30,31,37,41,43]. In addition, HPs also believed that the lack of technical literacy was a barrier for patients to manage their disease using mHealth, especially for older patients or those with a low level of education [26,28,29,31,33,40,41].

Personal Habits

HPs felt that many of them would be reluctant to change their habits in general practices, especially older HPs [40,41,43]. Moreover, they also thought that some older patients would prefer face-to-face consultation [31,43].

Health Problems

In addition, HPs considered that some patients with severe physical or mental health problems would have difficulty in using mHealth devices [28,29,40].

Concern About Additional Burden

Information Overload

HPs believed that patient-reported or automatically generated data by apps would result in information overload, which would create extra work [25,29,37,40-43].

Excessive Schedule Pressure

HPs stated that they did not have further capacity to take on duties that mHealth use would add to their busy schedules, such as excessive demand for immediate processing, interpretation, and responses [25,29,32,36].

Disturbing Personal Life

HPs also expressed feeling a little conflicted about receiving patients’ messages or managing alerts during off-work hours (during nights, weekends, and vacations) [36,42,45].

Uncertainty Around the Value of mHealth Technology

Lack of Evidence of the Value of mHealth Intervention

HPs thought that some of their peers were skeptical about mHealth technology and uncertain whether mHealth solutions could meet the expectations of saving time and maintaining high-quality care [25,27,31,32,37,43]. They lacked the time and resources to fully explore and assess the available apps [43]. Simultaneously, they expressed that evidence regarding the validity, reliability, utility, effectiveness, and risk-benefit of mHealth from research or other health care workers was insufficient [25,29,40,41,43]. Therefore, it was difficult for them to differentiate bad apps from good ones [29].

Dubious About the Value of Patient-Gathered Health Data

HPs believed that it was difficult to guarantee the accuracy of patient-gathered health data [24,29,43]. Therefore, they considered the value and usefulness of the vast amount of patient-reported data to be doubtful [35,41,42]. They were inclined to see patients in person to conduct the tests themselves.

Concern About Undermining Traditional Face-To-Face Services

Moreover, HPs stressed that they were concerned about mHealth technology undermining and even replacing traditional services [23,27]. They also feared the risk of transferring their role to the tools [23,29,31].

Fear of Medicolegal Risk

The Potential for the Misinterpretation of mHealth Data

HPs believed that the significant amount of data that patients recorded in mHealth tools might lead to missing critical information [42] and that web-based communication might cause difficulties in determining whether the patient obtained the information correctly [27,38,41,43,44]. All these conditions increase the risk of the misinterpretation of mHealth data and unintended outcomes [32,42-44].

Legal Liability for Inability to Immediately Respond to an Alert

HPs expressed that the opinions on the time taken to respond to patient data and the modalities and frequency of app use were numerous and strongly diverged among end users [27,29]. They might even hold different perceptions of the same incident [27,45]. All these conditions could lead to unintended medical liability [36].

Inappropriate Automated Instruction

HPs indicated that mHealth might send irrelevant or non–evidence-based information [42] and even inappropriate automated instructions via an incomplete alerting algorithm, which would mislead the patient [30,36].

Data Privacy and Security

HPs stressed that data privacy and security were regarded as important issues. They feared the risk of patient data being exploited and commodified [23-25,27-30,36,37,39,41,42,45].

Lack of Comfortable Design and Experience

Non–User-Friendly Design

HPs expressed that the end users were unsatisfied with the design of some mHealth tools for reasons such as a boring appearance, outdated content, heavy text, language deficits, the lack of preset options, the lack of an option to provide voice input, the lack of symptom trend charts, scattered information, and the lack of compatibility with different mobile operating systems [22,24,27,28,30,38,42]. They felt that the app design was less personalized and could not adapt to user characteristics and preferences [27,30,35].

Lack of Interoperability and Integration

HPs believed that a key barrier was the lack of interoperability between mHealth devices and existing health care information systems or among mHealth apps, and they were concerned that using mHealth would increase their workload [22,28,29,31,43,44].

Technical Glitches

HPs expressed that potential technical glitches, such as freezing, crashing, equipment malfunction, password issues, and unstable internet connection, would make the end user lose interest in using mHealth [24,28,33,35,41].

Insufficient Development Support

HPs thought that the lack of motivation among information system developers to optimize the systems limited the speed of information exchange between mHealth technology and other information systems [25,43].

Lack of Resource and Incentives

Lack of Financial Investment

HPs deemed that in some developing countries, there was not enough financial investment to cover the costs of data storage, infrastructure establishment, the assessment of the impact of mHealth tools, the maintenance of mHealth tools, and the replacement of outdated mHealth tools [25,31,38,40,41,43,44].

Lack of Workforce

HPs indicated that they lacked time, and there was insufficient workforce available to respond to every message from patients using mHealth apps [29,31-33,36,41,44].

Lack of Extra Payment for HPs

HPs expressed that they had limited readiness to work outside their routine responsibilities without overtime compensation [25,33,37,41,43,45].

Inadequate Medical Insurance Coverage

HPs believed that some patients with lower socioeconomic status could not afford mHealth services without adequate insurance coverage [22,25,41,43].

Lack of Policy Guidance and Regulation

Absence of Policy on mHealth Development

HPs expressed that health authorities had little interest in making users aware of the possibilities of mHealth and in making policies to support the implementation of mHealth [23,28,30,36,43,45]. In addition, hospitals typically did not request them to provide mHealth-related services. Therefore, mHealth service provision was based on personal willingness [45]. HPs lacked a sense of urgency about the provision of mHealth service without explicit managerial instruction and a policy on the adoption of the technology.

Absence of Authorized Certification for mHealth Apps

HPs indicated that there was no requirement to obtain validation from an authorized organization before putting mHealth apps into use; therefore, it was difficult to prevent false claims on the effectiveness of certain mHealth products or services [22,23,30,41,42].

Absence of Regulations on Related Legal Responsibility

HPs were concerned that a lack of regulations or specifications clarifying definitions, including those of immediacy, emergency, standard operating procedures, optimal care pathways, and data transmission conditions, would create disputes and even medicolegal implications [27,31,35,37,40,45]. Furthermore, there was a lack of overarching organizational systems for accountability management [31].

Absence of Regulations on mHealth Data Protection

HPs indicated that mHealth lacked standards and specifications about who could be allowed access to patients’ health information and in which situations the data could be used, which would threaten patients’ privacy and data security [27,41,43].

Worrisome Side Effects Resulting From the Use of mHealth

Worry That Patients Would Become Obsessed With Their Smartphone

Some HPs were concerned that a number of patients would become obsessed with mHealth, and the overuse of mHealth apps might cause anxiety, suspicious feelings, or a false sense of security, which would create or worsen some symptoms and thus increase drug prescriptions and health care expenses [22-24,27,29,41,42].

Worry That Close Contact Between the Patient and Physician Would Be Affected

Some HPs were concerned that mHealth would make them become technicians and affect the humanistic care of patients [24,40-43].

Worry About the Deepening of the Social Inequalities of Health Care

HPs worried that mHealth could be used only by certain patients, depending on their income level, level of digital literacy, and their linguistic ability [32,42,44].

Discussion

Principal Findings

This meta-synthesis extends our understanding of HPs’ perceptions and experiences of adopting mHealth for CNCDs and highlights specific facilitators of and barriers to mHealth use. The main perceived adoption facilitators were categorized into 6 themes: empowering patient self-management, increasing efficiency, improving access to care, increasing the quality of care, improving satisfaction, and improving the usability of the internet and mobile software. The perceived barriers could be categorized into 3 major groups: individual factors (limitation due to digital literacy, personal habits, or health problems; concern about additional burden; uncertainty around the value of mHealth technology; and fear of medicolegal risks), technological factors (lack of comfortable design and experience), and social and economic factors (lack of resources and incentives, lack of policy guidance and regulation, and worrisome side effects resulting from the use of mHealth).

A previous review [46] suggested that 2 technical acceptance model factors [47], usefulness and ease of use of the technology, were seen as 2 of the most important factors with respect to the adoption of mHealth. In addition to the facilitator of the usability of mobile technology, our findings also showed that empowering patient self-management was a main perceived adoption facilitator for mHealth. The previous review [46] examined the factors influencing health care professionals’ adoption of mHealth tools that were not specifically designed for chronic diseases; however, this study focused specifically on mHealth tools for chronic disease management. In most of the included studies (16/24, 67%), HPs expressed that they were more likely to adopt mHealth when it could improve self-management in chronic care [42,47,48]. Moreover, mHealth could potentially improve patient health and minimize the need for office visits for the routine management of some of the most common acute and chronic issues because of greater patient self-management [49]. In addition, mHealth can increase the accessibility of care and enhance the monitoring, tracking, and communication of various biometric information [50]. HPs also indicated that mHealth could eliminate the time demands on physicians, which was associated with greater physician satisfaction.

Despite HPs perceiving some facilitators to the use of mHealth technology, they also perceived some barriers to mHealth use. In addition to the barriers to mHealth use at the individual and technological levels, which had been emphasized in previous studies [46,51], this study also highlighted the social and economic factors that were perceived by HPs as important barriers to mHealth use.

Privacy and security issues had been regarded as significant barriers to HPs’ adoption of mHealth. mHealth devices generate substantive amounts of personal data. HPs feared the risk of patient data being exploited and commodified [23-25]. Although many countries are developing legislations or regulations to deal with this concern [52], few mHealth apps received a comprehensive risk analysis before the trial to ensure data privacy and security [53]. For HPs, the assessment of individual apps and literature searches on app evidence are highly time consuming and challenging to perform on their own [54]. Given the importance of secure and private channels of communication, it was also found that some clinicians may be challenged in finding mHealth technology partners willing to sign business associate agreements for security and privacy [54]. These findings suggest that a more nuanced approach to privacy and security may be needed to support mHealth expansion.

Moreover, the reimbursement of tasks related to mHealth was also perceived as a potential barrier to mHealth adoption. This finding was consistent with a prior analysis of factors related to mHealth use [46]. Without payment, it would be difficult for HPs to afford to provide services to patients with chronic illnesses using mHealth [53]. However, it has been reported that many countries have not yet incorporated reimbursement approval [52]. These findings suggest that adequate reimbursement for mHealth may be a critical incentive for maintaining the broad adoption of mHealth.

Notably, HPs felt that mHealth could reduce their workload and stress by reducing repetitive actions. HPs also believed that patient-reported data collected using mHealth would result in information overload, which would create extra work [29,41,43]. HPs were also concerned about how managing the alerts could disturb their personal lives [36,42,45]. These findings suggest that specific regulations for the use of mHealth should be implemented and indicate the need for increased incentives for mHealth use.

Although HPs perceived that mHealth offers a way to address the barriers to care, they were also concerned about unequal access to mHealth for some patients due to low income and the lack of digital literacy, which may deepen the social inequalities of health care [7,55]. This potential side effect of mHealth has been less frequently reported in previous studies. Previous studies have also shown little evidence of the widespread use of mHealth in resource-poor settings [55,56]. Therefore, improving the implementation of mHealth in low- and middle-income countries is critical to meet unmet health care needs, especially among susceptible people, such as older patients and patients with poor digital literacy skills.

Strengths and Limitations

To our knowledge, this is the first meta-synthesis to examine HPs’ perceptions and experiences regarding the barriers to and facilitators of mHealth use for CNCDs. The review was based on an extensive literature search and adhered to best practice processes to ensure rigor and quality, and bias was minimized in terms of the literature search, appraisal, and synthesis. Overall, the methodological quality of the included studies was verified based on the JBI-QARI critical appraisal tool.

This study has several limitations. First, all the studies included in this review were published in English, thus eliminating any important papers in other languages. Second, most of the included studies were from high-income countries. Therefore, the findings may not be representative of countries with different cultures and income levels. Third, the results of this study were based on a synthesis of qualitative studies, which is inherently subjective; however, the involvement of a review team improved the robustness of the findings.

Conclusions

This synthesis provides an overview of the qualitative literature on HPs’ experiences and perceptions regarding the adoption of mHealth for CNCDs. The facilitators of the use of mHealth technology as perceived by HPs fell under the themes of empowering patient self-management, increasing efficiency, improving access to care, increasing the quality of care, improving satisfaction, and improving the usability of the internet and mobile software. The perceived barriers included individual, technological, social, and economic factors. On the basis of these findings, interventions are needed to address the identified obstacles to foster HPs’ adoption of mHealth for CNCDs. The findings of this study may provide significant insights for health care workers and policy makers who seek ways to improve HPs’ adoption of mHealth.