First, a scoping search was undertaken to explore the context of palliative care needs at home, what is important about supporting such needs, how end users might engage with mHealth interventions, and the mechanisms underpinning home-based care. Nine electronic databases were systematically searched: MEDLINE, Embase, PsycINFO, and Global Health via Ovid Technologies, CINAHL via EBSCO Information Services, Web of Science via the Clarivate Web of Science platform, the Cochrane Database of Systematic Reviews via Cochrane Library, Scopus via the Elsevier Scopus, and Global Index Medicus via the WHO platform. The search applied a broad structure: [mHealth] AND [palliative care] AND [home-based care], combining subject headings and free-text searches of titles and abstracts. Search results were uploaded to an online data management software, Covidence [30]. The first author (NTG) screened all records for relevance to the topic of mHealth intervention in home-based palliative care, while the second and third reviewers (OA and NG) independently screened 50% of the identified articles, and any disagreement was resolved by team discussion. After title, abstract, and full-text screening, the authors familiarized themselves with the included studies and identified common themes. Preliminary “if...then” statements were developed to articulate how, why, and under what circumstances mHealth interventions influence home-based palliative care. These statements informed the initial context-mechanism-outcome hypotheses [29].

The preliminary hypotheses generated from the scoping search were discussed with 14 global palliative care researchers and health care professionals through individual face-to-face and online meetings [31]. These discussions emphasized the importance of understanding the complex systems in which mHealth interventions are implemented, including their impact on technology engagement and subsequent outcomes.

To explore these contextual factors, we applied the Framework of Complexity in Palliative Care Contexts [32] adopted from Bronfenbrenner’s Ecological Systems Theory [25,26]. This framework highlights interconnected environmental levels and provides a systemic perspective to analyze variations in context, multisectoral actors, and environments across different levels of the health care system (Figure 1). Insights from stakeholder consultations guided the refinement of the review scope and informed the subsequent iterative search strategy.

Following stakeholder consultation, an iterative evidence database search was conducted to identify studies relevant to the refined scope. The aim of this supplementary searching was to reach a point of theoretical saturation, where no new relevant evidence was generated for the particular theory building or testing within the synthesis [29].

The iterative search applied refined search terms informed by the scoping search and reassessment of the retained studies. In addition to the broader terms, specific functions of mHealth interventions commonly used in home settings, such as telehealth, telemedicine, and videoconferencing, were included. Searches were conducted in the same 9 electronic databases, with no restriction by year in March 2024 (full search strategy reproduced in Multimedia Appendix 1). The search results were uploaded and managed in Covidence. Only papers published in English were considered due to the practical constraints of engaging reviewers fluent in other languages.

In March 2026, the search was updated with forward citation searching of the final included studies using Google Scholar to identify more recent publications. Screening was conducted following predefined selection criteria; the first author (NTG) screened all titles and abstracts, while OA and NG independently reviewed 50% of records. Discrepancies were resolved through team discussions.

The relevance of studies in this review was assessed by considering whether they contained sufficient detail and/or theoretical discussion to inform how and in what context mHealth interventions support home-based palliative care, that is, their contribution to generating program theory [29,38]. In accordance with previous research, the relevance of contributions was defined as high (well aligned to the review questions, with rich descriptions contributing to theory), medium (with some relevance to the review question and some insights to CMOCs), or low (meeting broad inclusion criteria and providing at least one insight) [29].

The Mixed Methods Appraisal Tool (MMAT) [39] was used to assess the methodological quality and rigor of the included studies, as it is useful to assess the quality of qualitative, quantitative, and mixed methods studies. The MMAT comprises 5 quality criteria, with 3 responses for each item (ie, positive [=1] and negative or unavailable information [both=0]). The 5 items are added with a score of 5 indicating high quality. The Joanna Briggs Institute quality assessment tool was used to assess the methodological rigor of the included systematic reviews, with scores allocated on a scale from 0 to 11. Using this tool, a score of 8 or more indicates high quality, a score of 6 to 8 indicates moderate quality, and a score less than 6 suggests low quality. Three authors (NTG, NG, and OA) independently assessed the methodological quality of the studies and met to discuss their appraisal. Differences were resolved through discussion with the entire research team [40].

As a realist review approach may use only relevant data from part of an article, no studies were excluded based on methodological quality. However, studies with high relevance and rigor were given priority during theory development [27-29,31].

Bespoke data extraction forms were developed to organize information obtained from the included studies. A careful review of the introduction, methods, results, and discussion sections assisted in identifying relevant information. Descriptive characteristics, such as author, year, study aims, design, population, and country, were recorded. Details were also extracted on the intervention components, contextual factors, implied or actual mechanisms attributed to the outcome of interest, and the intervention outcomes. Data extraction was undertaken by 2 of the authors (NTG and NG) working independently. Areas of discrepancy were resolved through discussion to reach consensus.

Evidence sources were synthesized narratively, prioritizing included studies by relevance and rigor [41] and shown as a set of CMOCs. Taking an iterative approach, the first author read and reread different sources of data and coded the evidence to identify prominent recurrent concepts concerning contexts, mechanisms, and outcomes [29]. Coding was done deductively to the concepts in the Framework of Complexity in Palliative Care Context and using the priority “if...then” statements, which informed the preliminary context-mechanism-outcome hypotheses. In this process, we drew logical conclusions from the observed patterns, particularly the contextual factors influencing mHealth interventions [32] and inductively (proposing insights from recurring observations), mainly regarding mechanisms, and retroductive coding (exploring new patterns using theory to propose causal pathways) [42]. This process allowed the development of mHealth program theory in palliative care and conceptualization of the causal pathways using contexts, mechanisms, and outcomes [43,44].

In total, 4134 studies were identified via database searching (n=1895, 45.8%) and forward citation searching of the final included studies in March 2026 (n=2239, 54.2%). The selection process and included articles are illustrated in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram (Figure 2). Following the removal of duplicates and initial screening, 422 (10.21%) articles were retained for full-text screening, and 126 (3.05%) studies were included in the final synthesis, and of these, 20 (0.48%) articles from the initial scoping search contributed to the initial preliminary hypothesis generation. Details of the studies included in the final synthesis are presented in Table 2.

Publication dates ranged from 2001 to 2026. Studies from high-income countries were conducted in the Americas (n=45, 35.71%), Europe (n=26, 20.63%), Australasia (n=12, 9.52%), and Asia (n=2, 1.58%). The LMICs represented were from Africa (n=4, 3.17%, representing 22 countries), the Middle East (n=2, 1.58%; ie, Iran and Lebanon), and Asia (n=2, 1.58%; ie, China and India) [9,35,116]. Studies featured qualitative design, including participatory co-design studies and case studies (n=48, 38.09%), mixed method studies (n=25, 19.84%), reviews (n=33, 26.19%), and quantitative studies, which included descriptive (n=14, 11.11%), randomized (n=4, 3.17%), and nonrandomized (n=2, 1.58%) trials.

The majority of the studies met most of the quality criteria (Multimedia Appendix 1). Regarding MMAT scores, the studies scored between 0 and 5 criteria, with a median score of 4.5 (IQR 3-5). For systematic reviews and meta-analyses, the median score was 9 (IQR 8-10) on the Joanna Briggs Institute quality assessment tool.

The contextual factors within which mHealth interventions were described using the Framework of Complexity in Palliative Care Context [32] (Figure 3). They encompassed a range of technological, organizational, economical, and social dimensions.

In the microsystem, important concepts were persons’ or mHealth end users’ needs (ie, complex health care and technology-related requirements) [45,63,142]. Individuals’ characteristics were also key, which included limited knowledge about and the skills to support complex palliative care needs [92,97,123,156], the burden experienced by caregivers [9,12,121,128,142], and information gaps [88,134]. Concerning the acceptance of new mHealth tools, contextual concerns reported by end users encompassed their suitability in a palliative care context [95] and the perceived burden of monitoring symptoms for patients and family carers [48]. Furthermore, health care professionals had worries regarding reimbursement with the transition to virtual models of care [49,144]. In addition, having previous experience with similar DHIs and users’ technical competency appeared to influence end users’ confidence in using mHealth [57,76,90,92,99]. As depicted in the mesosystem, existing interpersonal relationships significantly influenced the utility of mHealth interventions. In fragmented health care systems, communication and information exchange challenges were particularly prominent, signifying the relevance of mHealth in such contexts [7,9,11,45,48,51,56,58,59,66,68,69,72,74,85,90,92,95,96,99,102,103,108,116,119,120,122,123,129,137,139,140,142,144,147,148,151,154,157,164,170,171,173,174]. Contextual factors that were identified in the chronosystem comprised the dynamic needs and fluctuating symptoms, which influenced patients’ goals of care and treatment preferences [45,47,51,63,142] and thus impacted their willingness and ability to engage with mHealth technologies. Furthermore, end users, especially older adults, needed time to feel confident and become proficient with digital health tools [51,68].

In the exosystem, there were challenges in terms of a rising demand for palliative care [9,48,85,94,95,108,110,112,115,116,118-120,127,131,136], the lack of standardized assessment tools to appropriately identify patients who require palliative care [35,94,102,105,108,113,126,133,140,143], inefficient documentation and data sharing [87,127,147], and fragmented or siloed health care interventions [60,102,147]. Finally, contextual factors in the macrosystem encompassed disparities in health care access due to geographical barriers, lack of mHealth infrastructure, and inadequate technical support for end users [35,69,71,84,91,116,118,145,174]. Aligning technology with relevant social and cultural values [54,65,67] was reported as key to the successful adoption of mHealth [12,61,88,92,103,105,107,114,121,128,134]. The studies also highlighted the need for legal and organizational frameworks and regulations to ensure data privacy and security [51,94,110,115,119,120,124,126,132,148]. Multisectoral stakeholder engagement was important in the design and implementation stages as buy-in from each stakeholder influences intervention uptake and sustained use [49,99,103,134].

The CMOCs for the refined program theory are reported below and summarized in Table 3 and shown in Figure 4.

mHealth implementation may be resisted by various end users due to the concerns they may have about the intervention. One of the concerns is the suitability of using DHIs in palliative care for vulnerable patients and inadequately prepared informal caregivers who are already burdened with illness and caretaking responsibilities. This is mitigated by providing appropriate orientation on what to expect from DHIs, the role of end users, and when to use such tools. This in turn creates a shared understanding and tailored use directed to goals of care. Regarding apprehensions about patient data security and privacy, mHealth interventions that are designed adhering to international and local standard legal and ethical protocols and guidelines ensure feelings of security when recording and sharing data and safeguard patient information, which will encourage uptake and sustained use. Health care workers perceived they would experience job security threats and increased workload, for example, with parallel documentation. Furthermore, conventional health care systems were anticipated to take time to adopt mHealth enhanced models, with the need to develop payment protocols or billing systems and lease with the health workforce in the implementation. The shift toward mHealth creates concerns that existing interpersonal interactions would be disrupted. Thus, both service users and providers demanded that health care visits should be a combination of in-person and mHealth. Responding to such concerns will increase the acceptance of digital health technologies for improved care delivery.

Despite increasing access to mobile phones and increased routine use of mHealth, universal access to mobile devices has not been achieved. Reliability issues and inadequate technical support persist as key challenges in home-based palliative care. Sustained use depends on reliable infrastructure and technology tailored to local capacities. For mHealth to effectively support health care delivery, it should accommodate users’ varying capabilities. National government and institutional support, through policies that promote mHealth adoption, is essential for successful implementation. Moreover, at the design phase, it is important to ensure that the technology is user-friendly and meets the needs of patients, families, and care providers. Health care workers who are confident in their technical skills are more likely to adopt these technologies. Yet, to be inclusive, all end users should receive orientation on the technical and functional aspects of such technologies for standardized use. This will improve self-efficacy and competency, thus ensuring usability with potential for wide-scale user penetration.

In contexts where both service users and care providers are required to navigate complex health care pathways, the development of clinically meaningful mHealth interventions must be designed with relevant multisectoral stakeholders from diverse disciplines. This approach will facilitate the consideration of unique perspectives and address the utility and usability needs and preferences of patients, family caregivers, and health care workers. It is also important to assess the experiences and expectations of various end users, their competencies, and systems constraints to achieve optimal usage for intended outcomes. Furthermore, mHealth interventions must fulfill the standard requirements of health management information systems to ensure effective implementation and integration with routine care. The integration of mHealth in home-based palliative care presents numerous opportunities to enhance care delivery. For example, when mHealth interventions are seamlessly fitting into the standard workflow, health care workers’ capacity to address patients’ needs will increase, which improves the care experience for patients, optimizes resource use, and potentially saves costs and time for both service users and providers.

mHealth interventions represent efficient care delivery approaches that are well accepted by patients, families, and health care providers who value collaborative care planning and enhanced care coordination to improve patient outcomes. mHealth interventions designed to allow structured collection and reporting of symptoms, such as pain, using digitalized symptom reporting tools by patients or their carers enhance appropriate and timely management. Electronic health recording allows health care providers to closely monitor patients’ conditions even remotely, triaging patients based on severity of symptoms for tailored response, which results in improved quality of care and health care workers’ productivity. Primary care clinicians and generalist palliative care providers could document visits and share data for remote consultations with palliative care specialists, promoting knowledge exchange among health care teams. In addition, mHealth provides the opportunity for palliative care specialists to directly connect with service users (patients and family carers) through real-time audiovisual support. mHealth facilitates home-based care, improving comfort and convenience for patients and families while reducing risks linked to facility-based treatment.

In the management of chronic life-limiting illnesses, direct access to health care providers and regular symptom support are important. mHealth interventions designed to augment home-based palliative care play a pivotal role in facilitating access by surmounting geographical constraints, particularly for underserved populations and communities residing in remote areas. In palliative care context, such tools assist patients with an advanced stage of illness who have difficulties visiting health care facilities or those facing physical barriers to conventional health care services under unique circumstances, as observed during the COVID-19 pandemic. Both health care providers and recipients benefit from reduced burdens associated with logistical challenges and financial expenses associated with traveling to distant health care facilities. Moreover, mHealth fostered close communication between patients and providers and strengthened relationships. Family carers felt more supported, and their capacity to care for their loved ones improved, which reduced the anxiety associated with providing care for patients with serious illnesses. Considering the increasing demand for palliative care and the limited number of palliative care specialists, this is highly relevant to ensuring continuity of care and access to more equitable services.

The implementation of mHealth interventions serves as a means of disseminating information, thereby augmenting patient engagement in their health care journey. Patients are empowered as their comprehension of illness and its management deepens through personalized self-care advice to manage common symptoms. Empowered patients are more likely to engage in proactive healthy behaviors and adhere to medical advice, which improves symptoms and quality of life. Informal caregivers also benefit from applying mHealth tools in their caregiving roles to enhance their understanding and capabilities to support their loved ones. Health care professionals also use such interventions to access educational resources to assist them in care delivery.