The protocol of this systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42024524634). The review was performed following the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [15].

We undertook a 25-year systematic review of mHealth interventions used to facilitate the self-management of CMP and adherence to treatment and management regimens. For this review, CMP included all types of chronic primary and secondary musculoskeletal pain based on the complete list of CMP conditions in the International Classification of Diseases, 11th edition (ICD-11) foundation layer supplement [16]. Our definition of mHealth was adopted from the Global Observatory for eHealth definition: “medical and public health practice supported by mobile devices” [17]. Given the comprehensive nature of CMP, this review goes beyond defining adherence as compliance with a treatment regimen by including a wide range of interventions such as medication reminders, symptom monitoring, educational tools, and facilitated patient-provider communication [18].

The search strategy was based on CMP diseases according to the ICD-11 [19]. The search was conducted from January 1999 through December 2023. Using Boolean phrases, we searched the PubMed, MEDLINE, Cochrane (CENTRAL), EMBASE, and Web of Science databases for studies that assessed the role of mHealth interventions in CMP management. The search strategy was first developed for the PubMed database using Medical Subject Headings (MeSH) and was adapted for other databases. The search was filtered either by English language or by date of publication or publication type as an article. The detailed search strategy for each database is provided in Multimedia Appendix 1.

We included original research published in peer-reviewed journals that evaluated the effects of mHealth tools on CMP-related clinical outcomes; adherence to management; and usability, feasibility, and acceptability features. All available study designs (randomized controlled trials [RCTs], cohort studies, cross-sectional studies, case series, case reports, questionnaires, mixed methods studies, and qualitative studies) were eligible for inclusion. Allowing for flexibility in the outcomes measured and in study design was necessary for an inclusive view of mHealth interventions at all stages of design, development, and evaluation. The detailed inclusion criteria for study selection and mHealth tools are provided in Multimedia Appendix 2.

Only articles reporting mHealth interventions designed for CMP were included. We excluded articles regarding interventions that were not tested in a sample population with clearly described methods and results. In addition, review articles, editorials, commentaries, dissertations, poster presentations, abstracts, proposals for future studies, study protocols, and descriptive articles describing new tools but not testing them in a sample population were excluded. The publication language was restricted to English.

Publications were initially screened for potential inclusion based on a simultaneous review of titles and abstracts by 2 independent reviewers. Any discrepancies were resolved by consensus among the researchers. Information, including publication year, location, study sample characteristics, types of mobile technology used, intervention and control details (if available for the given study design), outcomes measured, and summary results reported, was extracted and compiled using a Microsoft Excel spreadsheet.

We performed descriptive analyses of the data and summarized the findings from the included studies with an emphasis on statistical results reported in RCTs and cohort studies. We highlighted differences between groups when these results were available. Studies were organized for analysis based on the primary objective of the study and the key outcomes measured. Outcomes were organized into the following categories: (1) usability, feasibility, and acceptability of the mHealth tool; (2) effect of the mHealth intervention on adherence to CMP management; and (3) effect of the mHealth intervention on pain outcomes.

In all, 866 articles were retrieved in full text and assessed for eligibility, among which 383 articles were excluded due to duplication. Based on the search criteria, 337 articles were excluded due to not meeting the study design criteria or not aligning with the definition of mHealth used in our study. A total of 142 articles were included in the full-text retrieval process, of which 53 were excluded due to an inappropriate study design, intervention, or population or no access to the full text. Finally, a total of 89 articles were eligible and met all inclusion criteria; the details of these included studies are provided in Multimedia Appendix 3 [20-107]. Multimedia Appendix 4 illustrates the PRISMA flowchart for the study selection process and Multimedia Appendix 5 provides a list of excluded studies with reasons for exclusion.

The publication time period for this 25-year systematic review spanned from 1999 to 2023, with an overall increase in articles published after 2017 and a significant increase during the COVID-19 pandemic from 2019 to 2022 (Figure 1). A total of 26 countries published eligible studies across 5 continents (Asia, Europe, Africa, America, and Oceania) from 1999 to 2023. Most of the eligible studies were published in Europe (Figure 2). The top-3 ranked countries publishing in this field were the United States (22/89, 25%), Germany (11/89, 12%), and Australia (7/89, 8%) (Figure 3). The vast majority of technology development for mHealth has been based in the United States, especially in the earlier days of smartphone introduction.

RCTs that assessed the differences between different mHealth tools or between an mHealth tool and standard or control care were the most common study designs, accounting for 35% (31/89) of the included studies, followed by cohort (23/89, 26%), pilot (14/89, 16%), cross-sectional (8/89, 8%), qualitative (7/89, 8%), and questionnaire (4/89, 4%) study designs; there was only 1 (1%) case series, case report, and cost-effectiveness study each. Study durations ranged from only a few hours to 12 months depending on the study design.

The CMP populations considered in this review included adult and young patients with nonspecific CMP, specific OA without or with surgery, CLBP, and neck pain. Patients with OA and CLBP were the most commonly investigated groups among the included studies. The characteristics of the target user group were often the key impetus for the development of the mHealth tool. For example, some researchers noted that a certain percentage of patients report suboptimal satisfaction for various reasons after total knee arthroplasty, such as continued pain or stiffness [108-111]. For patients with knee pain 24 months post triple arthrodesis following a crush injury, a specific mobile camera could immediately provide corrective techniques when applying in-clinic equipment to the patient’s shoe [102]. We identified 10 studies addressing the perceptions or experiences about mHealth tools in adult CMP populations with and without surgery [26,28,30,55,62,85,87,95,112] and their practitioners [30,35,87], as well as in young people [90]; the authors of these studies agreed that mHealth interventions were acceptable, useful, and feasible. However, there is still a gap between patients and physicians in understanding and communication about the treatment and management of knee OA [30]. It is expected that multiple stages of user experience testing could be a template for future mHealth tools aimed at chronic disease management [113]. However, only 1 eligible article [87] mentioned an mHealth app prototype that was codeveloped with patients and health care providers (HCPs) in 2021. Ultimately, it appears that diverse individuals can use mHealth tools as long as the tools are tailored to the needs of the population and sufficient training and support are provided.

In most of the reported mHealth interventions, mobile phones or other devices were either provided to users or considered a required intervention for study participation. We classified the studied mHealth tools into 4 main categories: mobile app, mobile app plus monitor, mobile app plus wearable sensor, and web-based mobile app plus monitor. The details of each type of mHealth tool are provided in Multimedia Appendix 6. Mobile app (78/89, 88%) was the most commonly used tool and the primary platform for patients with CMP. For example, mHealth apps could be installed on the patient’s mobile phone at any time to help remember to check pain symptoms, maintain physical therapy, connect to coaches, or communicate virtually with HCPs in real time. The next most common mHealth tool reported was a mobile app plus monitor, which was used in 6% (5/89) of the included studies, followed by a mobile app plus wearable sensor (4/89, 4%) and web-based mobile app plus monitor (1/89, 1%). One study reported multiple digital technologies. These mHealth programs focused mainly on a combination of mobile and internal or external monitors or an external sensor, thus facilitating the transfer of data automatically without requiring the patient to manually submit the data and providing faster delivery of self-management therapy.

Among the 89 included studies, 23 (26%) assessed or reported usability, feasibility, and acceptability using qualitative methods and compiled usage data, including 1 case series, 9 cohort studies, 4 cross-sectional studies, 6 pilot studies, 1 qualitative study, 1 questionnaire study, and 1 RCT. These data ranged from patient satisfaction to cost-effectiveness estimations as well as the timing and frequency of engagement with mobile apps and platforms. Multimedia Appendix 9 provides an overview of these studies [22,24,29,33,38,43,45,49,52,57, 61,66,71,72,74,77,80,92,93,95,101,107].

In general, these studies found mHealth tools and platforms to be usable, feasible, acceptable, and appreciated among users compared with traditional measures. For example, both older and younger patients with CMP and those who underwent joint surgery perceived that using an mHealth tool increased their independence and confidence in pain management [22,38,80]. One study reported that the prescription of therapeutic exercises via a smartphone app is feasible and well-accepted among patients of all ages [112]. Seven studies showed the long-term (>3-month follow-up) feasibility and acceptability of mHealth tools [24,33,38,49,52,72,101]. Specifically, with long-term follow-up, patients with knee or hip OA seemed to have preferences for goals related to physical activity and nutrition rather than for goals related to vitality and education [71]. Patients with OA undergoing primary hip or knee arthroplasty particularly appreciated the mHealth tools empowering patients, facilitating transitions from specialized hospital care to primary care, reducing unplanned contacts with the health system, and reducing overall health costs, proving to be cost-effective [38]. Another study performed in a real-world setting with a large (N=10,264) and diverse population experiencing CMP found that mHealth was accepted and considered especially useful for pain reduction [62]. The majority of studies included in this review focused on the patient as the end user of mHealth tools, although some also evaluated acceptability and perceptions from the perspective of HCPs. Features of mHealth tools such as automated reminders, messages with educational and motivational content, healthy living challenges, and wireless transmission of data contributed to increased self-care awareness and knowledge about CMP.

This review showed that interventions based on mHealth systems have beneficial effects on adherence and clinical outcomes for individuals with CMP. Thus, this scientific evidence suggests that these mHealth systems could be promising alternatives for CMP self-management through multimodal approaches. The evidence presented here indicates that while the potential of mHealth tools is high, their results during implementation and execution are nevertheless mixed.

Mobile apps are the most widely reported mHealth tool for interventions, which have been successfully used to facilitate adherence to CMP management and improve clinical outcomes [114,115]. The freedom and portability of mobile devices combined with the advanced capacity to facilitate 2-way communication and collect and analyze data for a real-time response offer enormous potential to both patients and providers [116,117]. Owing to their abilities for automation, personalization, and easy integration into existing health systems, mobile apps are less operator-dependent and are less reliant on processes to facilitate the active and time-consuming exchange of information compared to traditional tools. However, apps may have a limitation in terms of difficulty of use for the older population with minimal technology experience or familiarity, and there is clearly room for improvement. Moreover, there is a lack of scientific and health professional support in many available mHealth systems, highlighting the need for developing appropriate apps based on the well-recognized guidelines in the management of CMP [113].

There is a growing recognition of the need for digital technologies to improve access to age-appropriate resources and personalized support for cocare [118-120]. Unlike previous reviews in this field that only included RCTs or cohort studies, this systematic review also included 13 qualitative studies (including questionnaire, interview, and discussion studies). These informative studies used qualitative methods that yielded rich data that can be used to better understand how and why mHealth tools impact adherence behaviors and clinical outcomes. Qualitative data can also enable patient-physician discussions regarding modifiable self-management options based on the perspectives and needs of patients, HCPs, or both groups. Moreover, user feedback can inform hypotheses that can then be tested. Research that seeks to understand how and why mHealth works will deliver on the broader promise of mHealth. Future mHealth tools will be able to draw on the knowledge generated when discrete hypotheses around the relative importance of, for example, patient-provider communication, optimal user interfaces, or targeted motivational messages are tested. These informative studies could lead to better mHealth tools that deliver improved health outcomes.

This review found that the usability, feasibility, and acceptability of mHealth tools for CMP management and adherence to different programs were generally high among both patients and providers. mHealth offers a way to address different barriers to care and to reduce health disparities from both patient and HCP perspectives. However, only one article published in this field over the last 25 years mentioned that the mHealth app prototype was codeveloped by patients and HCPs [87]. There will be more opportunities to codesign mHealth tools in the future. Undoubtedly, innovative mHealth tools could unintentionally increase health disparities due to unequal access to technology. There is also recognition that unequal access to, use of, and knowledge of information can influence the uptake and use of mHealth tools. These inequalities and the needs from target user groups should be taken into consideration early in the design and development of future mHealth tools. However, none of the studies included in this review addressed systematic differences in usability between diverse patient groups and geographical areas. Future research can be designed to better understand these differences and to encourage the development of mHealth tools that address the needs of diverse patient groups and populations living in regions with different levels of economic development.

The high prevalence of CMP globally coupled with the advantages of providing help through apps offers opportunities to help countless people who may be looking to the potential of mHealth to lessen the burden of their pain. One key aspect of this potential involves an increase in cost-effectiveness and expanded outreach of pain management. Of note, only one study included in the review specifically focused on the issue of cost-effectiveness [60]. Furthermore, only one study presented the postmarketing observational data after a follow-up duration of 9 months [96]. Rigorous cost-effectiveness analyses will be necessary to demonstrate not only the health impact but also the value of investing in these innovations. Future studies evaluating the cost-effectiveness of mHealth tools are indeed needed, since we can see that pockets of mHealth innovations are expanding around the globe (Figure 3). Besides cost, language, and literacy barriers, availability and connectivity issues are also potential barriers to consider when developing these types of mHealth tools. Nevertheless, the strong attachment people have to mobile phones and the tendency to carry them everywhere open up opportunities for continuous symptom monitoring and connecting patients with providers outside of health care facilities.

There are some limitations of this study. First, we did not perform a meta-analysis and we did not weigh the quality of evidence or study design against reported results. Second, only the English literature was included, and the sample size of the included studies varied substantially from 1 in a case report to 10,264 in a cohort study. Third, the diversity of study designs, objectives, and outcome measures made clear comparisons among studies difficult, and the quality of evidence was deemed to be variable. However, this is the first 25-year systematic review focusing on evidence collected from January 1999 to December 2023 regarding the impact of mHealth on CMP management adherence. The main strengths of this review are that we included a diverse array of study designs; assessed both self-management and clinical outcomes; and incorporated the nascent literature regarding mHealth feasibility, usability, and acceptability.

mHealth is a potential high-impact tool to improve health outcomes among those with CMP through supporting adherence to personalized or tailored self-management programs for pain. Further evaluation of mHealth tools is needed, especially research that informs the cost-effectiveness of these tools. More innovation, optimization, and high-quality research in mHealth has the potential to transform the promise of mHealth technology into the reality of improved health care delivery and outcomes.