The Universal Health Coverage commitment aligned with the emergence of COVID-19 reinforced the value of telemedicine services and elected these services crucial to coping with the pandemic’s challenges in the health care sector. Since the pandemic reached the western countries, home telemonitoring offered an alternative to control the health status of infected nonsevere patients at their homes to avoid unnecessary visits to the hospital [1].

During the early part of 2020, from a social perspective, the fast-track solution to prevent the spread of COVID-19 focused on social distancing [2]. Governments forced people to stay at home, canceled mass gatherings, imposed teleworking, and closed all educational institutions [3]. From a health care perspective, governments took some extreme measures to increase the capacity to cope with the virus, namely reduction or deferral of nonurgent care and hands-on visits, and postponement of nonurgent surgeries [4]. These measures exposed high-risk groups, such as the elderly at home, people at long-term care facilities, patients with chronic conditions, and hidden diseases [5]. Inevitably, physicians started following-up with their patients through video calls and remote monitoring to continue treatment and avoid long-term complications [6]. In parallel, health care providers launched new telehealth services to assist patients in their homes [7]. Policy makers and practitioners did not have enough information to decide which pilot intervention they should disseminate into real-world settings, considering different financial reimbursement strategies, health care system organizations, and workforce acceptance levels [8].

With technological progression and decreasing equipment costs, remote patient monitoring emerged as a telemedicine application. It comprises interactive and noninteractive technologies to support health care and monitor patients’ health status in their homes [9].

Home telemonitoring is one type of remote patient monitoring, which has shown and is showing potential to improve clinical and patient-reported outcomes and ensure cost reductions for health care practices [10]. In this work, the authors consider the definition given by Paré and colleagues [11] for home telemonitoring. A service based on home telemonitoring consists of health care professionals monitoring the patient's health status at a distance. Patients or caregivers transmit their health-related data to a responsible health care professional through information and telecommunication technologies. Research on home telemonitoring showed benefits concerning health care systems’ capacity constraints [12], patient empowerment, and treatment commitment [13]. It revealed significant improvement in clinical outcomes even in some diseases [11]. Despite the considerable investment in accelerating health information technology [14], there is not enough information on determining whether home telemonitoring is appropriate and feasible for implementation in a real-world context [15]. Scaling up a health intervention requires wise and efficient spending of resources [16]. Therefore, it is crucial to assess the suitability of scaling up home telemonitoring interventions with proven efficacy to provide answers to the following two questions [17]: Does it work in practice? Is it worth it?

To answer these questions and decide which technology-based health intervention can be scaled up for mainstream care delivery, one must assess its scalability (ie, the ability to be expanded to real-world conditions without compromising on effectiveness and access to the eligible population) [18].

Most of the studies focus only on assessing the effectiveness and costs of a health intervention. Nevertheless, these are two of many considerations to address when evaluating the potential of scaling up an intervention [19]. Other domains such as the feasibility and adaptability of the health intervention and the political or strategic contexts are rarely analyzed. As emphasized by Milat and his colleagues [15] in their recently proposed Intervention Scalability Assessment Tool (ISAT), assessing a health intervention’s scalability involves considering multiple domains, such as the political and strategic contexts, workforce, and infrastructure, among others.

There is a need to conduct evidence-based studies that assess pilot interventions’ potential to achieve population-wide benefits [20]. Scalability studies that also consider the intervention’s suitability to the socioeconomic context in question are important to estimate the success of deploying these interventions in different contexts [15].

Owing to the lack of research on scalability analysis, in this paper, the authors present a systematic review, based on Milat and colleagues’ domains [15], to identify and characterize methods used to assess the potential to scale-up home telemonitoring interventions in the context of a growing telehealth service in the industry. This study focuses on peer-reviewed studies conducted to evaluate the scalability of follow-up interventions based on home telemonitoring. The authors aim to provide a comprehensive overview of these studies concerning the domains and methods used and identify gaps for future research to address when evaluating the potential to implement or scale-up home telemonitoring interventions. As the authors are not aware of other systematic reviews focusing on this aspect, they believe that this review will enlighten researchers, practitioners, and policy makers regarding the most used strategies to assess the scalability of home telemonitoring interventions.

The search strategy followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to conduct the review [21]. The population, intervention, comparison, outcome (PICO) framework [21] allowed the identification of key concepts such as “Home Telemonitoring,” “Follow-up,” “Scalability,” and “Assessment” to formulate a well-focused question and facilitate the literature search. To optimize the search through effective queries, the authors used PubMed’s Medical Subject Headings (MeSH) to identify indexed terms [22]. This step was fundamental as this review emerges from the combination of research fields with different terms for the same concept. Textbox 1 presents the rationale used to build the final query used in each database.

Figure 1 illustrates the search performed in PubMed, Scopus, Web of Science, and EconLit covering studies from 2000 to 2020 (Figure 1 - Set #1). The authors chose to explore EconLit owing to the economic evaluation required to assess a health care intervention’s scalability. The authors selected full-text and peer-reviewed papers written in English (Figure 1 - Set #2).

After removing the duplicates and references without abstracts (Figure 1 - Set #3), two authors independently scanned the titles and abstracts identified in the literature search and applied the selection criteria presented in Textbox 2 (Figure 1 - Set #4).

To guarantee that the article’s topic aligned with the research question, the same authors scanned the 49 full-text articles, which reduced the number of studies considered for review to 13 (Set #5).

The authors analyzed 13 full-text articles, corresponding to 13 studies, in detail and registered all the observations in a literature matrix [23]. First, to better explore each intervention and summarize relevant, well-specified, and secure data, the authors conducted an integrative synthesis. The main variables were the country of origin, publication year, sample size, setting, duration of follow-up, comparator arms, type of technology, and study outcomes [24].

Second, the authors assessed the strength of each eligible study’s evidence according to the 9-level classification system proposed by Jovell and Navarro-Rubio [25].

Finally, they conducted narrative and thematic analyses to identify themes and patterns in the eligible articles and outline the findings under specific headings [24] to better examine how each study assessed the potential of scaling up an intervention. When disagreements occurred, the authors reached a consensus via discussion. One author extracted data from the studies and completed quotes, and the second author validated the data according to the definition of each category. The authors conducted this analysis based on the work undertaken by Milat and colleagues [18] in the development of a tool to perform systematic assessments of the suitability of health interventions for scale-up (ISAT). ISAT comprises three parts: setting the scene, planning the intervention implementation, and summarizing the scalability assessment. The first two parts made it possible to classify each study according to the stage of scale-up, context, and focus area. Moreover, Milat and colleagues’ domains enabled the authors to identify the methods and instruments used by the researchers to assess the intervention’s scalability [18].

The research conducted for each domain assessed in the eligible papers was classified as qualitative or quantitative. The research was classified as qualitative if it was based on the description of experiences, emotions, behaviors, events, or actions [26] and quantitative when the respective authors used numerical data to measure, categorize, or identify patterns, relationships, or generalizations through statistical analysis [26].