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.

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.

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).

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.

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 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.

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.

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.

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.

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].

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.

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.

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).

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).

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.

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.

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.