A comprehensive computerized search was conducted across 8 English and Chinese databases: PubMed, Web of Science, Embase, MEDLINE, China National Knowledge Infrastructure, Wanfang Data, China Science and Technology Journal Database, and SinoMed, covering literature from inception to March 2025. The search focused on identifying influencing factors of mHealth apps in kidney transplant care. Search terms were structured using a combination of Medical Subject Headings and keywords. English search terms: “kidney transplant* OR renal transplant* OR kidney graft* OR renal graft* OR kidney allograft* OR renal allograft* OR transplanted kidney OR kidney homotransplantation* OR renal homotransplantation*,” “tele?medicine OR telemedicine OR mobile health OR m?Health OR mHealth OR tele?health OR telehealth OR e?Health OR eHealth OR tele?rehabilitation OR telerehabilitation OR tele?monitoring OR telemonitoring OR video?conferencing OR videoconferencing OR online OR technolog* OR mobile information technolog* OR electronic OR digital OR Internet-based OR app* OR mobile app* OR software OR wearable electronic device* OR WeChat OR platform* OR smartphone*,” “motivator* OR motivation* OR enabler* OR promote OR drive OR encourage OR facilitator* OR barrier* OR obstacle* OR challenge* OR difficult* OR issue* OR influencing factor* OR impact factor* OR relative factor* OR experience* OR perception* OR opinion* OR feeling* OR need* OR attitude*.” The search strategy was adjusted according to different databases. Key adaptations included the use of database-specific subject headings (eg, Medical Subject Headings in PubMed, Emtree in Embase) and adjustments in field tags (eg, [Title/Abstract] in PubMed; ab, kw, ti in Embase). Taking the PubMed database as an example, the search query is provided in Multimedia Appendix 1. To ensure comprehensive reporting, the PRISMA checklist is provided in Checklist 1.

Inclusion criteria:

Exclusion criteria:

Two researchers trained in evidence-based nursing systematically and independently conducted the screening process using predefined protocols. Duplicates were removed using NoteExpress 3.8 software. Initial exclusion was performed through title and abstract screening, followed by full-text assessment. Discrepancies were resolved through consultation with a third researcher.

Data extraction was independently performed by 2 researchers. To ensure accuracy and completeness, all extracted data were cross-checked against the original full-text articles by a third researcher. Any disagreements or uncertainties were resolved through team discussion. The data extraction form was independently developed by the researchers, subsequently refined through research team discussions, and ultimately finalized for implementation. Extracted data included authors, publication year, country, study design, sample size, study population, sampling method, data collection methods or tools, names and functions of mHealth apps, and Mixed Methods Appraisal Tool (MMAT) rating.

Given the heterogeneity of study designs, 2 researchers independently evaluated the methodological quality of included studies using the MMAT, 2018 edition [18]. Discrepancies were resolved through consensus discussions with a third researcher. The MMAT was developed by a research team at McGill University, Canada, in 2006 and updated in 2018 [18]. It is designed to appraise the quality of qualitative, quantitative, and mixed methods research studies. The tool comprises 2 screening questions and 15 appraisal criteria. After addressing the 2 screening questions, the appropriate study category is selected for appraisal. Qualitative and quantitative studies require the evaluation of 5 criteria each. Each item was rated as “Yes,” “No,” or “Unclear.” A rating of 5***** or 100% indicates fulfillment of all quality criteria, 4**** or 80% indicates fulfillment of 80% of the criteria, and so forth. For mixed methods studies, all 15 criteria (instead of 5) must be appraised. The overall quality rating adheres to the principle that the quality of the whole is determined by its weakest component.

This study conducted data analysis anchored in the 5 domains of the CFIR [16] and adhered to the Joanna Briggs Institute guidelines [19] for mixed methods systematic reviews. The analytical process involved: (1) integrating findings from distinct research methodologies separately; (2) translating quantitatively synthesized results into qualitative narratives and merging them with qualitatively synthesized evidence; and (3) finally establishing integration categories, inducing thematic patterns, and deriving consolidated outcomes.

A total of 9919 relevant studies were retrieved from the databases. After removing duplicates, 7256 studies remained. Following preliminary screening, 86 studies were retained. Ultimately, 19 studies were included after full-text review. The literature selection process and the list of included studies are presented in Figure 1 and Table 1, respectively.

All included studies were published in English, with publication years spanning from 2009 to 2025. These studies originated from 8 countries, with the United States contributing the highest number of publications [11,14,26,28,30,33,35]. The research designs comprised 9 qualitative studies, 5 mixed methods studies, and 5 quantitative studies. The study populations included 1265 kidney transplant recipients and 34 health care professionals. The basic characteristics of the included studies are summarized in Table 1.

A total of 9 qualitative studies exhibited high overall quality, each scoring 5*****. Among the 5 quantitative studies, 1 [14] did not clearly describe whether the sampling method was appropriate for addressing the research questions, another [14] failed to specify the representativeness of the sample relative to the target population, 1 [30] lacked clarity on the risk of nonresponse bias, 1 [26] did not explicitly address confounding factors in the study design or analysis, and 1 [31] did not confirm whether interventions were implemented as intended during the research period. The 5 mixed methods studies demonstrated lower overall quality: 4 studies [21,23,25,35] did not justify the rationale for adopting a mixed methods design to address the research questions, 4 [21,23,25,35] inadequately explained the integration of qualitative and quantitative results, 4 [21,23,25,35] insufficiently addressed discrepancies or heterogeneity between quantitative and qualitative findings, and 3 [21,25,35] failed to cohesively integrate distinct components of their research. The MMAT scores for all included studies are presented in Table 1.

This study identified 16 facilitators and 14 barriers influencing the application of mHealth in kidney transplant care. Based on the CFIR, both facilitators and barriers were categorized into 5 domains, as detailed in Table 2.

An important finding of this review is that, despite the comprehensive 5-domain analytical structure offered by CFIR, a notable imbalance exists in the focus of existing research. Influencing factors are predominantly concentrated within the domains of intervention characteristics, characteristics of individuals, and the inner setting. In contrast, factors related to the outer setting and process domains, such as policy support and stakeholder collaboration, remain significantly underrepresented.

This pattern may be explained by 2 primary reasons. First, the majority of included primary studies focused on technology acceptance and user experience. Their participants were predominantly kidney transplant recipients, who are generally not in a position to provide in-depth perspectives on macrolevel issues such as health care policies, regional resource distribution, or multistakeholder coordination. Second, evidence concerning the complex factors of the outer setting and process is more likely to reside in what is known as “grey literature,” including project evaluation reports, dissertations, or specific policy studies, which was not captured by our predefined academic database search.

The findings of this study indicate that the application of mHealth is closely tied to its inherent characteristics. The domain of intervention characteristics contains the highest number of influencing factors, which aligns with prior studies [36]. For successful integration into the workflow, the intervention needs to align with the prevailing value system of key stakeholders.

After enduring the hardships of dialysis, most kidney transplant recipients highly value their transplanted kidney and exhibit heightened caution in health management [37]. The authoritative nature of mHealth content helps build trust in the platform and strengthens users’ confidence. Studies [38,39] reveal that approximately 30% of kidney disease–related apps on the market lack clinically validated content, posing risks such as incorrect medication advice or deviations from the latest guidelines, which significantly undermine patient trust and hinder sustained use. Therefore, mHealth development should be grounded in evidence-based medicine and clinical guidelines, with regular updates. Prior to releasing content, experts in kidney transplantation should review materials to ensure scientific rigor. The design and performance of mHealth directly impact user experience. Kidney transplant recipients vary widely in age, educational background, and health literacy. With global population aging, the proportion of older recipients is rising [40,41]. Older users may struggle with complex medical terminology or operational workflows due to cognitive decline or limited technical adaptability. Thus, mHealth design should prioritize accessibility, using plain language and intuitive interfaces to lower barriers and improve acceptance. Kidney transplant recipients commonly face psychological challenges such as anxiety and depression [42]. Research [43] shows that integrating emotional support features into mHealth can alleviate patient anxiety and enhance long-term engagement. Additionally, lifelong self-management is essential for recipients. If apps lack personalized feedback, users may abandon them due to perceived low value. To address this, personalized features such as achievement badges to motivate users or tailored notifications to boost engagement could be incorporated.

mHealth is a double-edged sword. While offering advantages such as convenience, flexibility, and reduced infection risks, it also has limitations, including security risks, hindered clinician-patient interaction, and exposure to negative content, which diminish user willingness to adopt it. Studies [44,45] highlight widespread patient concerns about data security in mHealth, particularly for long-term conditions such as kidney transplantation, where sensitivity to privacy breaches is heightened [46]. Thus, health care providers should strengthen awareness of data protection and regularly debug and update software to prevent cyberattacks. Beyond safeguarding privacy, stakeholders could enhance user experience by improving content moderation and integrating wearable devices to mitigate mHealth’s shortcomings.

The findings of this study reveal that the adoption of mHealth is influenced by multiple internal and external factors. Additionally, outer setting factors are relatively few among all influencing factors. This aligns with a prior scoping review by Rangachari et al [47].

Peer, professional, and multidisciplinary team support significantly impact kidney transplant recipients’ engagement with mHealth, suggesting that health care providers should establish multidimensional support strategies. Peer support fosters emotional resonance and experience sharing among patients with similar backgrounds, reducing loneliness and enhancing willingness to use mHealth. Therefore, integrating peer communication modules into mHealth platforms is recommended; however, medical advice or medication sharing should be explicitly prohibited to ensure compliance. Guidance and feedback from health care professionals can alleviate patients’ technological apprehensions. Studies [48] indicate that lack of clinician support is a barrier to mHealth adoption, aligning with this study’s conclusions. Thus, remote follow-ups by professionals to guide patients, alongside accessible channels for reporting issues, are proposed to build trust and sustain engagement. The development and implementation of mHealth require collaboration and guidance from multidisciplinary teams. Without such support, unresolved technical or medical challenges during use may lead to unmet needs and reduced compliance [49]. Future initiatives should establish mHealth teams comprising physicians, specialist nurses, rehabilitation therapists, and IT engineers to optimize application effectiveness. Additionally, internal and external factors such as additional costs and unstable network infrastructure hinder mHealth accessibility. Expenses related to data consumption or smart device procurement may impose financial burdens on patients. Research [50] reports that 23% of patients in resource-limited settings abandon mHealth due to unaffordable device costs. To address this, governments and hospitals should establish subsidy mechanisms to reduce user expenses. Developing low-data or offline versions of mHealth platforms could simultaneously mitigate data consumption and connectivity issues, thereby improving resource accessibility.

The findings of this study indicate that the application of mHealth is influenced by individual characteristics, such as intrinsic motivation and perceived benefits. Notably, factors at the individual level are relatively numerous among all identified influences, which is similar to the findings of the previous review by Neil-Sztramko et al [51].

Intrinsic motivation refers to an individual’s inherent tendency to seek novel challenges, develop competencies, and engage in exploratory learning driven by personal interest or enjoyment of the activity itself, which is more likely to sustain long-term behavioral adherence [52]. For users lacking intrinsic motivation, motivational interviewing techniques such as goal setting, reflective practices, verbal persuasion, and action planning can be used to initiate and reinforce their motivation, thereby promoting behavioral persistence [53]. This study also reveals that perceived benefits can enhance users’ sustained engagement with mHealth. Research demonstrates [54] that perceived benefits indirectly influence behavioral adoption through the mediating role of self-efficacy. Health care professionals should acknowledge and encourage users’ perceived benefits, reinforce positive experiences through multiple channels, and further enhance self-efficacy to facilitate long-term mHealth adherence. When users fail to perceive benefits, their behavioral persistence may be compromised [55]. In such cases, practitioners should promptly adjust intervention strategies through telephone follow-ups, video demonstrations, and other methods to strengthen perceived benefits and improve self-efficacy. Furthermore, this study identifies that insufficient eHealth literacy constitutes a barrier to mHealth utilization. Some users experience difficulties in independently operating smartphones or applications due to age, educational background, or limited digital skills, leading to resistance or abandonment of mHealth tools. Therefore, beyond simplifying the usability of mHealth systems during development, health care providers should strengthen preimplementation training on device functionalities. Personalized training programs tailored to users’ varying health literacy levels should be developed to compensate for eHealth literacy deficiencies.

Guided by the CFIR, this study systematically analyzed the influencing factors of mHealth apps in kidney transplant care, which were categorized into 5 domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and process. The findings revealed that intervention characteristics constituted the most prominent influencing domain, while the outer setting and process demonstrated relatively fewer associated elements. Future research should further investigate the impact of these identified factors on mHealth apps in kidney transplant care. Additionally, health care providers should adopt multidimensional strategies targeting these determinants to optimize mHealth integration into clinical practice.