Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy.

Introduction

Oral systemic anticancer therapy (SACT) has revolutionised the treatment landscape for certain haematological malignancies, facilitating a transition towards a chronic disease model and enabling prolonged survival rather than a terminal prognosis. Haematological malignancies encompass a diverse group of cancers that primarily target the blood, bone marrow, and lymphoid tissues. This category includes significant types, such as leukaemia, lymphoma, and myeloma, which collectively account for 6.5% of the estimated 20 million new cancer cases and 7.1% of cancer-related mortalities reported in 2022. 1

Despite their classification as incurable diseases, many indolent blood cancers have demonstrated improved prognoses, mainly driven by advancements in molecularly targeted therapies. The rising global incidence of cancer, which accelerated from approximately 18 million in 2020 2 to 22 million in 2022, 1 presents significant public health concern and warrants effective strategies to meet the complex needs of patients treated on oral SACT living in the community, especially in the setting of haematological malignancies where surgical resection is not a treatment option.

However, a fundamental challenge persists in ensuring patient adherence to prescribed treatment regimens. A study conducted in the Netherlands revealed an alarming 50% adherence rate among patients with haematological cancers, including conditions such as acute leukaemia, chronic leukaemia, non-Hodgkin lymphoma, and multiple myeloma. 3 Suboptimal adherence to oral SACT can lead to serious therapeutic complications, such as the emergence of resistance to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukaemia (CML), due to oscillations in drug concentration within cancer cells. 4 Moreover, suboptimal adherence complicates patient management, leading to increased healthcare costs associated with switching therapies, increased hospitalisation for procedures such as stem cell transplantations, poor prognoses, higher relapse rates, and decreased overall survival. 3 , 5 – 7 Notably, significant differences in major molecular response (MMR) and event-free survival (EFS) rates were observed among CML patients based on their adherence levels. 8

Several factors contributing to non-adherence to oral SACT have been identified in various studies. A scoping review highlighted three primary barriers: side effects and toxicity, forgetfulness, and limited access to reliable information. 9 Forgetfulness has been consistently implicated in reduced medication adherence among patients with leukaemia. 10 – 12 Furthermore, a systematic review concerning multiple myeloma patients found that up to 36% reported discontinuing treatment, frequently owing to adverse effects such as haematologic and gastrointestinal toxicity. 13

The autonomy associated with home-based oral SACT administration often leads to diminished oversight and monitoring from healthcare providers, contrasting with the well-established protocols for intravenous chemotherapy. Patients with haematological malignancies often experience unique clinical challenges such as prolonged treatment, require frequent monitoring, and a high symptom burden across different phases of the disease. These distinct clinical characteristics may necessitate tailored solutions that differ from those developed for solid tumour cancer populations.

mHealth or mobile health, as outlined by WHO's Global Observatory for eHealth, refers to “Medical and public health practices supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs) and other wireless devices”. 14 The COVID-19 pandemic has accelerated the integration of mHealth applications into the contemporary healthcare system. These applications promote self-management by providing actionable feedback that can mitigate adverse effects and enhance patients’ quality of life. 15 , 16 Medication self-management, as described by Bailey et al., includes taking medications as prescribed while considering aspects such as dosage, frequency, and long-term safety. 17 In the context of oral SACT, self-management entails health surveillance, recognising changes in health status, seeking assistance, and autonomously managing side effects. 18

Understanding patient acceptance and engagement with mHealth applications is essential to developing practical tools that align with the self-management needs of patients undergoing oral SACT. A systematic review by Gutierrez et al. assessed available mobile applications designed for patients with haematological malignancies, uncovering a disappointing average quality score of merely 3.1 out of 5, with particularly low ratings for engagement and aesthetics. 19 Moreover, Chow et al. carried out a review and meta-analysis revealing that while mHealth applications effectively managed side effects related to oral SACT compared to usual care, their influence on adherence was not statistically significant. 20 Eminently, only a small percentage (7.1%) of patients included in this analysis had haematological malignancies, underscoring the necessity for more tailored investigations. Furthermore, user experience studies indicated that many cancer-focused mHealth applications were developed with insufficient input from patients, showcasing a gap in understanding user preferences regarding the application. 21

According to Hinrichs-Krapels and Grant, acceptability is defined as the extent to which an intervention is perceived as suitable or favourable for engagement by the target population. 22 On the other hand, Kim defines the efficacy as the degree to which an intervention produces beneficial outcomes under controlled settings, whereas in real-world or usual application is called effectiveness . 23 Notably, acceptability and efficacy or effectiveness are interlinked, with each influencing the other and together determining the potential success of the intervention. Combining these aspects in a review serves as a prerequisite for a comprehensive understanding of how mHealth applications perform in practical settings. As mHealth app is still an emerging field, most existing reviews have focused on evaluating a variety of adherence enhancing strategies for oral SACT, not limited to mHealth-based intervention, or a mixture of mobile interventions delivery including both text message and mobile apps, 24 – 27 while our study has provided a more tailored investigation specific to the effects of mHealth apps in this specific population, which presents the recent trends in the landscape of digital health intervention.

Therefore, this study aims to systematically assess the acceptability of mHealth apps designed to support the self-management of oral SACT in patients with haematological malignancies and to evaluate the efficacy of these apps in achieving intended clinical and patient-reported outcomes. Addressing the challenges of adherence to oral SACT through innovative, technology-driven solutions represents a promising avenue for enhancing the quality of care offered to patients grappling with these conditions. By exploring these dimensions, this research seeks to contribute to the development of effective self-management practices within the context of oral SACT.

Methods

This review was conducted to answer the research question: "Are mHealth applications designed to support oral SACT self-management practice for patients with haematological malignancies acceptable and efficacious?" The reporting of this systematic review adhered to the synthesis without meta-analysis (SWiM) guideline 28 and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as in Appendix 1 . 29 The protocol for this review was registered with PROSPERO (ID: CRD42024583595) in October 2024.

Eligibility criteria

Fully published original research that evaluated the acceptability and efficacy of mHealth applications developed to aid self-management of oral SACT in patients with haematological malignancies was included in this review ( Table 1 ). Eligible study designs included quantitative (survey, pilot study, trial), qualitative (focus group, interview) and mixed-methods research published in English-language peer-reviewed journals. The outcomes focused on mHealth applications’ acceptability or efficacy on medication self-management skills (self-efficacy, knowledge, medication adherence) and clinical outcomes (symptom, health-related quality of life) reported by patients or healthcare providers.

Studies were excluded if they (1) involved patients with haematological malignancies exclusively receiving parenteral SACT, undergoing stem cell or bone marrow transplants in an inpatient setting, or included mixed cancer population; (2) assessed mHealth solutions other than smartphone applications such as text messaging; or (3) failed to report outcomes on the acceptability or application efficacy in supporting medication self-management practices from the patient and/or healthcare provider perspective.

Search strategy

Five electronic databases, including PubMed, APA PsycINFO, CINAHL, Cochrane Library and Web of Science, were searched for relevant literature published from inception to October 2024. The search employed Medical Subject Headings (MeSH) terms and Text Word keywords aligned with the research question using the Population Intervention Comparison Outcome framework ( Appendix 2 ). Keywords included ‘haematological malignancies’, ‘mobile applications’, ‘self-management’, ‘adherence’ and ‘side effects’, alongside relevant synonyms. Search terms were combined using ‘OR’ and ‘AND’ Boolean operators to develop robust search strategies. Initial pilot testing of search strategies was conducted in PubMed by LM, with results subsequently discussed with BKT and LCC before replication across other databases.

Selection of studies

Search results retrieved from the five databases were imported into Zotero citations management software (version 6.0.37) for removal of duplicates, then uploaded to Rayyan.ai, a systematic review tool, for title and abstract screening. Two researchers (LM and BKT) independently screened articles based on the predetermined inclusion and exclusion criteria ( Table 1 ). Each article was classified as ‘included’, ‘excluded’, or ‘maybe’ (if the title and abstract lacked sufficient information for a definitive decision). To assess the consistency of ratings between the two reviewers (LM and BKT), the intraclass correlation coefficient (ICC) and 95% confidence interval (95%CI) were calculated using SPSS (software, Chicago, IL, USA version 26). 30 ICC values indicated poor (<0.5), moderate (0.5 to 0.75), high (0.75 to 0.9) and excellent (>0.90) reliability.

The full texts of articles categorised as ‘included’ and ‘maybe’ were retrieved accordingly to undergo a more thorough evaluation. Both LM and BKT assessed these full texts against the eligibility criteria independently to determine their relevance to the study. Articles that met all inclusion criteria were confirmed for final selection, while those that did not were excluded. For any discrepancies and articles categorised as ‘maybe’ , reviewers engaged in further discussion to resolve differences and reach a consensus.

Quality assessment

The methodological quality of the included studies was assessed using the Mixed Method Appraisal Tool 31 (MMAT; version 2018). Two reviewers (LM and BKT) conducted the appraisal independently. Each paper underwent an initial screening to identify eligibility. Studies receiving ‘No’ or ‘Can't tell’, responses to one or both screening questions were excluded from a detailed appraisal. The quality of each study design (qualitative, quantitative, and mixed methods studies) was assessed using the applicable 5-item quality criteria. 31 Each quality criterion was classified as ‘Yes’, ‘No’ or ‘Can't tell’ with an assigned score, 20% for ‘Yes’, and 0% for ‘No’ or ‘Can't tell’. The total score ranged from 0% to 100%, reflecting the number of criteria met. Mixed methods studies were evaluated using both the mixed methods criteria and the specific criteria for each study component.

Data extraction and analysis

Data regarding study design, author, publication year, country, sample size, cancer type, features of mHealth applications, comparators (for trials), acceptability and efficacy measures, and results were extracted by LM using a standardised form, which was then reviewed by the research team. The form was piloted on two studies to ensure it met this review's objective. The features of the included mHealth interventions were extracted and categorised using the groupings and labels specified in the behaviour change technique (BCT) Taxonomy v1, 32 a standardised guideline for identifying behaviour change components. The developmental stages of the mHealth applications were categorised based on the 6-step development and evaluation framework of mHealth interventions as proposed by Whittaker et al. 33

Given the heterogeneity of the included studies, a narrative synthesis 34 of quantitative findings and a thematic analysis 35 of qualitative findings extracted from the result sections of the included studies were conducted. Thematic analysis followed Braun and Clarke's (2006) 35 six-phase framework: (1) familiarisation with the qualitative findings from the included studies; (2) generating initial codes to systematically identify recurring factors influencing acceptability; (3) constructing themes by organising codes into conceptually meaningful categories, guided by the four domains (coherence, cognitive participation, collective action, and reflexive monitoring) of the normalisation process theory (NPT) developed by May et al., 36 which explains how new practices like mHealth become integrated into routine care; (4) reviewing themes collaboratively to ensure coherence and theoretical alignment with NPT; (5) refining and naming themes to clearly incorporate the factors influencing acceptability within the four domains of NPT; and (6) reporting themes as illustrated using a conceptual framework. Mapping to NPT provided a robust framework for interpreting how these interventions are sustained in clinical practice.

Results

Study selection

The systematic search across five databases yielded a total of 267 articles, of which 74 duplicates were removed. Following the title and abstract screening of 193 articles, 173 were deemed irrelevant or did not meet the inclusion criteria. Agreement between reviewers on study inclusion was high (ICC=0.789; 95%CI: 0.729, 0.837). Full texts for the remaining 20 articles were retrieved for eligibility assessment, leading to the exclusion of 12 additional articles, resulting in 8 articles for this review. The study selection process is illustrated in Figure 1

Characteristics of included studies and mHealth application models

The included studies comprised one randomised controlled trial (RCT; n=1), 37 one quasi-experimental design (n=1), 38 three qualitative studies (n=3), 39 – 41 and three mixed methods studies (n=3), 42 – 44 published between 2017 and 2024. Sample sizes varied from 10 to 108 participants: the RCT included 38 participants, the quasi-experimental study had 108, qualitative studies ranged from 11 to 47 participants, and mixed methods studies ranged from 10 to 106 participants. Geographically, three studies were conducted in the Netherlands, two in Australia, and one each in the United States, South Korea and Iran. The majority (n=5) 38 , 40 , 41 , 43 , 44 focused on CML, while two studies examined a mix of two or more haematological malignancies 37 , 42 and one specifically concentrated on acute lymphocytic leukaemia (ALL). 39 Seven studies targeted adult populations, 37 , 38 , 40 – 44 while one was designed for paediatrics. 39 Four studies sought input on application functionalities and acceptability from both patients and healthcare providers, 39 – 41 , 44 whereas the other four relied solely on patient feedback. 37 , 38 , 42 , 43 A summary of the study characteristics is presented in Table 2 .

Of the eight interventions, seven 37 – 43 were hybrid systems combining mobile apps and web platforms, while only one 44 was solely mobile app-based. All studies reported various features of the mHealth applications. The most common feature was symptom/side effect self-monitoring (n=8), 37 – 44 followed by symptoms monitoring by HCP (n=7), 37 – 43 information on disease/treatment (n=7), 37 – 40 , 42 – 44 reminder (n=6), 37 – 39 , 41 – 43 wellness information (n=6), 37 – 40 , 43 , 44 engagement with HCP or peers (n=6), laboratory results (n=4), 38 , 40 , 42 , 43 symptom assessment (n=4). 39 , 41 , 42 , 44

These intervention features were grouped and mapped to the corresponding BCT categories and codes, using the BCT Taxonomy v1. The mapping process revealed techniques spanning several key domains. Within ‘goals and planning’, features such as symptom assessment was assigned accordingly, while laboratory results, symptom tracking, and provider-led monitoring were placed under ‘feedback and monitoring’. Interaction with healthcare providers or peers was linked to ‘social support’. Content related to lifestyle or mindfulness practices fell within the ‘shaping knowledge’ category. Likewise, information about disease and treatment was coded under ‘natural consequences’, and reminders were associated with the ‘Associations’ domains. A detailed classification and summary can be found in Table 3 .

Based on Whittaker et al.' s mHealth intervention development and evaluation framework, this review identified one study 40 in the formative research phase, four studies (three mixed methods 42 – 44 and one qualitative 39 ) in the pre-testing phase, two were pilot studies 37 , 41 and only one non-randomised trial, 38 as illustrated in Table 4 . Additionally, various methodological approaches were utilised across the studies, including behavioural science research, 42 participatory action research, 43 design thinking development, 40 and user-centred design 39 ; however, only two 41 , 44 followed specific self-management frameworks, such as the middle-range theory of adaptation to chronic health conditions. 45

Quality of the included studies

The eight studies included were classified by design: three qualitative, three mixed-method, one RCT, and one non-randomised trial. The quality assessment of individual studies is shown in Appendix 3 .

The MMAT scores of the studies ranged from 40% (one qualitative study 39 and one mixed method study 44 ) to 100% (non-randomised trial, 38 which fulfilled all the quality criteria). Among the qualitative studies, two 39 , 40 lacked sufficient data, coherence and transparency in the analysis process, while another 41 reported a small sample size without evidence of thematic saturation. For the quantitative studies, LeBlanc et al. 37 did not detail random number generation or allocation concealment. Also, participant retention was low, with 53% and 39% completing surveys in weeks 4 and 8. Among the mixed-method studies, two 42 , 43 fully met qualitative criteria but scored 60–80% in the quantitative criteria due to sampling limitations and the use of unvalidated questionnaires. Finally, Song et al. 44 scored the lowest, providing minimal, non-integrative results from a small sample despite meeting qualitative (80%) and quantitative (60%) criteria.

Acceptability towards the applications and influencing factors

mHealth applications for self-managing oral SACT were generally acceptable to patients with haematological malignancies, with usability/satisfaction ratings between 60% and 78%. In this study, acceptability of mHealth applications refers to users’ perceptions assessed through relevant validated scales or TAM focusing on domains such as usefulness, usability, satisfaction, and feasibility. LeBlanc et al. 37 defined the acceptability of the Blood Cancer Coach application based on perceived helpfulness and satisfaction, considering the application acceptable if over 70% of participants rated their satisfaction as moderate or higher (Likert score ≥ 3) and they found 73% of participants reported overall satisfaction with the Blood Cancer Coach application.

In contrast, Dang et al. 42 utilised the Evaluation Tool for Mobile and Web-based eHealth Interventions (Enlight) to assess the SAMSON application based on six quality constructs, including usability. Among 27 participants, 21 (78%) of SAMSON users rated it easy to use. On the other hand, Verweij et al. 43 applied the System Usability Scale (SUS) to evaluate the usability of the CMyLife application, scoring it 65.3 on the guideline and 60 on the medication platform. A SUS score <51 indicates poor usability, 51–68 indicates acceptable usability, 68–80.3 indicates good usability, and 80.3–100 indicates excellent usability. Furthermore, 64% (n=39) of the CMyLife application users reported daily usage, indicating practical usability.

Qualitative data extracted from six studies (three mixed-method and three qualitative) uncovered four themes influencing applications’ acceptability. The acceptability influencing factors of mHealth applications in this review are illustrated in Figure 2 and mapped to the constructs of NPT. Figure 2. Themes of mHealth application acceptability factors and efficacy. Coherence – beneficial features that add value Coherence in NPT refers to users’ ability to comprehend the purpose of the intervention and assess its benefits. 46 The first theme centres on application features that add value; the reminder function was the most common for enhancing medication adherence and accountability. Monitoring and self-managing symptom distress were pivotal in the SAMSON application 42 and the REMIND system. 41 Both the CMyLife application 40 and the REMIND system 41 incorporated healthcare professional engagement, offering screen-to-screen and nurse-assisted consultations, respectively, which were well-received. Haematologists noted the empowering potential of CMyLife 40 in transferring care outside the hospital, while nurses highlighted its utility in addressing adherence issues. 36 Cognitive Participation – technical glitches and suboptimal user interfaces Cognitive participation involves users’ willingness to commit to and actively engage with the intervention 46 ; however, technical glitches and suboptimal user interfaces can obstruct this, forming the second theme. SAMSON application 42 users reported frustrations due to long loading times and navigation difficulties, particularly among older adults with limited technological proficiency. Pereira-Salgado et al. 41 noted that users with unstable network connections faced delays in receiving medication reminders. Users of the CanSelfMan, 39 SAMSON, 42 and CMyLife 43 suggested enhancing clarity, primarily through visuals and simplified language. Limitations in functionality were reported, as well as difficulties in editing lists and reminders. Participants using the CanSelfMan, 39 aimed at children, recommended adding engaging elements like cartoon characters and improving the visual representation of symptom evaluation. Collective Action – excessive notifications, insufficient support and updates Collective action, the third construct of NPT, describes the coordinated efforts and support of all stakeholders to ensure the functionality of the intervention. 46 Given this context, the third theme emerges, highlighting excessive notifications, inadequate support and inconsistent content updates. Users of ReLive, 44 REMIND, 41 and CMyLife 43 reported frustrations due to frequent notifications, including relentless symptom logging and adherence reminders. Concerns arose in Verweij et al.' s CMyLife study 43 regarding content updates, whereas ReLive 44 users noted insufficient guidance post-symptom scoring. Reflexive Monitoring – challenges integrating into daily routine Reflexive monitoring focuses on the implementation of the intervention and provides feedback for continuous improvement. 46 The fourth theme discusses challenges in integrating the applications into daily routines. Haematologists in the CMyLife study 40 expressed difficulty in integrating the application with hospital electronic health systems and the time commitment required for patient-provider interactions. Similarly, a nurse from the REMIND 41 study mentioned the need for more time to adapt to the system. Furthermore, users highlighted the need for CMyLife 40 to address comorbidities rather than functioning as a standalone application. Some ReLive 44 users rated the application poorly due to perceived disruption, with newly diagnosed patients expressing a greater inclination to engage compared to those with long-term, stable conditions. 37

Efficacy of applications

Two studies assessing mHealth applications’ efficacy showed no major clinical or behaviour improvement, though some impacts on awareness of additional help outside hospital, online health knowledge, symptom burden and impact on daily life were noted. LeBlanc et al. 37 evaluated the efficacy of Blood Cancer Coach on clinical and behavioural outcomes, measuring patient-reported outcomes at baseline, 4 weeks, and 8 weeks. However, no significant differences were observed between the control (n=22) and intervention (n=16) groups in global health, posttraumatic stress, or cancer symptom measures. 37

In Verweij's study, improvements in the intervention group were observed in aspects such as “ additional help outside the hospital ” and online health information. However, no significant differences were found in other eHealth literacy parameters, patient activation measure, and medication adherence. 38 Notably, EORTC-QLQ-CML24 health-related quality of life scores indicated a reduction in the impact on daily life, though patients reported heightened symptom burden, as evidenced by significant statistical changes. 38 The results of the mHealth application acceptability factors and efficacy in supporting self-management practice of oral SACT are summarised in Figure 2 .

Discussion

Principle findings

This review investigated the acceptability and efficacy of mHealth applications among patients with haematological malignancies undergoing oral SACT. Participants in six studies investigating acceptability reported overall positive feedback regarding usability and satisfaction. This aligns with Technology Acceptance Model 47 which posits that acceptability is driven by perceived ease of use and perceived usefulness, both of which significantly influence technology adoption. Although there were no major changes in clinical or behavioural outcomes in the two remaining studies, the CMyLife application successfully increased patients’ knowledge of online health information, consistent with studies by Potdar et al. and Ashruf et al. 48 , 49 It also offered additional help outside the hospital, indicating a positive impact on quality of life through heightened symptom awareness and management.

This review underscored four key factors influencing application acceptability based on the four constructs of NPT, 36 which are coherence, cognitive participation, collective action, and reflexive monitoring. Within coherence, features such as reminders, symptom tracking, educational resources, and communication with healthcare providers were perceived as valuable for patients and experts in supporting self-management beyond clinical settings. The same result is evident in Leidong et al.'s study where mobile applications for cancer management successfully supported adherence through behavioural changes, symptom management, healthcare professionals interaction, and improved quality of life. 50

For cognitive participation, technical difficulties and suboptimal interfaces may hinder user acceptability, discouraging them from investing time and energy into the application. To combat this, application developers must prioritise a seamless, user-friendly experience that facilitates intuitive navigation. According to Langote et al. , optimal healthcare interface can be achieved through user-centred design approach, visibility, learnability, flexibility, and error prevention, among others. 51

In terms of collective actions, excessive notifications may overwhelm users, while inadequate support and inconsistent content updates generate dissatisfaction, which impedes further participation. Under reflexive monitoring, users struggled to integrate the application into their daily routine, constantly evaluating if it met their needs. This resonates with findings by Han et al., Cao et al. and Yadav et al. whereby if the expected benefits do not outweigh the effort required, sustained usage is uncertain. 52 – 54 This entire process ultimately dictates whether the application can become a routine part of self-management practices. 46

Results of our review showed that a common barrier to implementation of the applications is the added workload on clinical staff which is in agreement with findings by Ardito et al. and Richards et al. 55 , 56 This often includes extra effort and time commitment required from doctors or other healthcare providers to oversee the application. 57 – 59 Our review also revealed that the involvement of healthcare providers in application development was overlooked in some studies. This echoes findings of a review of 123 cancer applications where only 3% (4 out of 123) involve participation of healthcare providers in their development and assessment. 60 The absence of expert contribution can lead to the uncertainty of content verification. 61 – 64 Hence, collaborating with healthcare professionals during the application development process is essential for ensuring the accuracy of medical content and the inclusion of user-centred design elements that add value to patient care without increasing the burden on providers.

The advent of mHealth applications represents a transformative step in healthcare delivery, significantly improving access to health services and allowing users to manage their conditions proactively. However, the successful integration of mobile applications 65 into healthcare heavily relies on the quality of real-time data exchange between patients and healthcare professionals. To truly meet the needs of users, their feedback must be included at all stages of application development. In this review, seven included studies employed patient-centric approaches, 66 – 69 yet widespread barriers, such as lack of technical support, persist and hinder adoption.

In contrast to the findings of Wu et al. , who reported several cancer pain self-management applications improved health outcomes - such as medication adherence and symptom control - this review suggests the gaps present in the current landscape of mHealth for haematological malignancies. 70 Future applications should incorporate features like colour-coded alerts for toxicity severity, self-care instructions, and tracking for condition-specific metrics, such as sleep, 71 to enhance user acceptability and adherence.

This systematic review also highlighted the limited availability of mHealth applications specifically tailored for patients with haematological malignancies, particularly for acute myeloid leukaemia (AML). With the rising incidence of AML, especially among aging populations and an increase in newly approved oral SACT such as venetoclax and midostaurin since 2017, 72 there is a crucial need for resources to support this demographic. Current mHealth solutions primarily address patients with solid tumours, particularly breast cancer, thereby underscoring the pressing need for innovations targeted at patients with chronic conditions like haematological malignancies. 73 Additionally, we found a notable absence of studies conducted in low-income countries, where Short Message Service (SMS) solutions are more common than application-based solutions.

Whittaker et al. emphasised the importance of involving both patients and experts throughout the development of mHealth interventions, recommending a six-step process: conceptualisation, formative research, pre-tests, pilot studies, RCT, and qualitative assessments. 33 Only the CMyLife application 38 , 40 , 43 had progressed from conceptualisation to trial. Other applications 39 , 42 , 44 in this review were in their pre-testing phase or had not advanced beyond the pilot study. Despite the incorporation of expert feedback in four 39 – 41 , 44 of the six analysed applications, the overall efficacy of these studies did not yield significant improvements in clinical and behavioural outcomes. One possible explanation is the small sample sizes, which may not provide adequate statistical power to identify differences. Additionally, the heterogeneity of outcome measures used across the studies complicates comparisons and conclusions regarding the overall efficacy of these applications, consistent with findings from Flaucher et al.' s review of breast cancer applications. 74

Future research direction

Further development of mHealth applications should prioritise well-designed, large-scale randomised controlled trials to rigorously evaluate their clinical and behavioural efficacy for patients receiving oral SACT. These trials should move beyond pre-testing and pilot phases, incorporating adequately powered and diverse participant groups to generate robust evidence for integration into standard medical practice.

In addition, future research should examine the long-term user engagement and adherence to mHealth applications, with a focus on addressing key implementation challenges such as technical glitches, notification fatigue, and difficulties with daily integration. For example, one potential strategy to limit the volume of notifications is to implement machine learning algorithms by adapting to individual patient medication regimens and symptoms over time, thus providing tailored alerts. The development and validation of standardised measures for assessing acceptability and efficacy will also be essential to enhance comparability across studies.

Implications for practice and policy

Our findings suggest that mHealth applications have the potential to facilitate remote monitoring of side effects, symptoms, and adherence in patients undergoing oral SACT, thereby enabling healthcare providers to better track patients’ symptom and adherence beyond traditional clinical settings. The insights from this review regarding application content and contextual factors provide valuable guidance for future research, development, and implementation strategies. Specifically, incorporating an aesthetically pleasing interface aligned with user experience (UX) principles can significantly enhance user engagement. 75

Additionally, concerns about infrequent updates, consistent with findings by Gutierrez et al., 19 underscore the need for ongoing maintenance and timely updates to ensure that applications remain aligned with evolving clinical guidelines, especially in the rapidly advancing field of haematological malignancies. Addressing these challenges will be critical for the successful integration of mHealth solutions into routine practice, facilitating personalised care delivery and improving the organisation and management of treatment plans for patients in their home environments.

Limitations

This review has several limitations, including the exclusive focus on studies published in English, potentially overlooking relevant research in other languages. The small sample sizes in the included studies may also limit the statistical power to substantiate findings. The included studies demonstrated substantial diversity across several key parameters, including sample populations, haematological cancer types, app features, and study designs. This variability restricts direct comparison and generalisability of the findings. Nevertheless, it underscores the broad applicability of mHealth interventions across different clinical and demographic contexts. Additionally, the variability in assessment measures used to evaluate mHealth app acceptability (SUS, Enlight, Likert-based satisfaction ratings) complicates comparison across studies and renders a meta-analysis infeasible. Consequently, the evidence regarding the efficacy and acceptability of mHealth applications for patients with haematological malignancies remains limited, highlighting opportunities for future research to bridge these gaps.

Conclusion

The available evidence indicates that mHealth applications are generally acceptable and show promise in supporting the self-management of oral SACTs among patients with haematological malignancies. However, usability challenges such as notification fatigue, and inadequate integration into daily routine, remain key barriers that must be addressed to realise the full potential of these applications. Current evidence on clinical efficacy is limited by heterogeneous outcome measures, and a lack of adequately powered RCTs. Future research should prioritise rigorous trial designs with standardised outcome metrics to determine the true impact of mHealth application interventions on patient outcomes. Moreover, future interventions should focus on enhancing personalisation such as using machine learning algorithms to adapt to patients’ evolving medications to potentially reduce notification fatigue, and integration into patients’ ongoing care to sustain long-term engagement.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251361221 - Supplemental material for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy

Supplemental material, sj-docx-1-dhj-10.1177_20552076251361221 for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy by Lena Mahalingam, Bee Kim Tan, Ping Chong Bee, Chee Hooi Teoh, Renukha Sellappans, Diana Leh Ching Ng, Azlan Husin, Sen Mui Tan and Li-Chia Chen in DIGITAL HEALTH

sj-docx-2-dhj-10.1177_20552076251361221 - Supplemental material for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy

Supplemental material, sj-docx-2-dhj-10.1177_20552076251361221 for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy by Lena Mahalingam, Bee Kim Tan, Ping Chong Bee, Chee Hooi Teoh, Renukha Sellappans, Diana Leh Ching Ng, Azlan Husin, Sen Mui Tan and Li-Chia Chen in DIGITAL HEALTH

sj-docx-3-dhj-10.1177_20552076251361221 - Supplemental material for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy

Supplemental material, sj-docx-3-dhj-10.1177_20552076251361221 for Mobile health applications for supporting self-management of oral systemic anticancer therapy in haematological malignancies: A systematic review of acceptability and efficacy by Lena Mahalingam, Bee Kim Tan, Ping Chong Bee, Chee Hooi Teoh, Renukha Sellappans, Diana Leh Ching Ng, Azlan Husin, Sen Mui Tan and Li-Chia Chen in DIGITAL HEALTH

Acknowledgement

The authors would like to record our appreciation to the Malaysia Ministry of Higher Education (MOHE) for funding this publication under the Fundamental Research Grant Scheme for the project: A study on a framework of a new remote personalised oral systemic anticancer therapy self-management model for patients with haematological malignancies (FRGS/1/2023/SKK16/TAYLOR/02/1). We also thank Taylor's University Malaysia for partial funding of this publication.

ORCID iD: Lena Mahalingam https://orcid.org/0009-0009-8909-7905

Bee Kim Tan https://orcid.org/0000-0003-3380-3072

Ping Chong Bee https://orcid.org/0000-0003-4253-760X

Chee Hooi Teoh https://orcid.org/0000-0002-4867-0244

Renukha Sellappans https://orcid.org/0000-0002-9338-735X

Diana Leh Ching Ng https://orcid.org/0000-0001-9634-3718

Azlan Husin https://orcid.org/0000-0003-0899-0443

Sen Mui Tan https://orcid.org/0000-0002-3761-6752

Li-Chia Chen https://orcid.org/0000-0002-6158-6645

Ethics consideration: Approval from Institutional Review Board was not sought as this systematic review was conducted using publicly accessible scientific literature.

Author contributions: LM conducted the literature search, study screening and selection, data extraction, and quality assessment, and draft the original manuscript. BKT contributed to the conceptualisation, study screening and selection, data extraction, quality assessment, as well as reviewed and edited the manuscript. PCB supported the literature search, contributed to the interpretation of results, and participated in the review and editing of the manuscript. CHT, RS, DNLC, AH and SMT contributed to the interpretation of findings, and participated in the review and editing of the manuscript. LCC contributed to the conceptualisation of the review, the interpretation of findings, and the review and editing of the manuscript.

Funding: This research and publication were financially supported by the Malaysia Ministry of Higher Education (MOHE) under the Fundamental Research Grant Scheme for the project: A study on a framework of a new remote personalised oral systemic anticancer therapy self-management model for patients with haematological malignancies (FRGS/1/2023/SKK16/TAYLOR/02/1). The publication of this paper is also partially funded by Taylor's University Malaysia. The funders played no role in conducting the study, analysing the data or developing the contents of this manuscript.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability: The datasets used and analysed in this study are available from the corresponding author on reasonable request.

Supplemental material: Supplemental material for this article is available online.

References

1 Bray F Laversanne M Sung H , et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA Cancer J Clin . 2024 ; 74 : 229 – 263 . 38572751 10.3322/caac.21834

2 Lin X Wang J Huang X , et al . Global, regional, and national burdens of leukemia from 1990 to 2017: a systematic analysis of the global burden of disease 2017 study . Aging . 2021 ; 13 : 10468 – 10489 . 33820874 10.18632/aging.202809 PMC8064161

3 Bouwman L Eeltink CM Visser O , et al. Prevalence and associated factors of medication non-adherence in hematological-oncological patients in their home situation . BMC Cancer . 2017 ; 17 : 739 . 29121889 10.1186/s12885-017-3735-1 PMC5679497

4 Apperley JF . Part I: mechanisms of resistance to imatinib in chronic myeloid leukaemia . Lancet Oncol . 2007 ; 8 : 1018 – 1029 . 17976612 10.1016/S1470-2045(07)70342-X

5 Reshma V Chacko AM Abdulla N , et al. Medication Adherence in Cancer Patients: A Comprehensive Review . Cureus 2024 ; 16 : 125 – 138 . 10.7759/cureus.52721 PMC10880514 38384629

6 Bekalu YE Wudu MA Gashu AW . Adherence to chemotherapy and associated factors among patients with cancer in amhara region, northeastern Ethiopia, 2022. A cross-sectional study . Cancer Control. 2023 ; 30 : 1 – 10 . 10.1177/10732748231185010 PMC10557419 37795961

7 Cheung WY . Difficult to swallow: issues affecting optimal adherence to oral anticancer agents . Am Soc Clin Oncol Edu Book 2013 ; 265 – 270 . doi: 10.14694/EDBOOK_AM.2013.33.265 23714519

8 Cheng F Cui Z Li Q , et al. Adherence to tyrosine kinase inhibitor and clinical outcomes in patients with chronic myeloid leukemia . Int Immunopharmacol . 2023 ; 124 : 110847 . 37639851 10.1016/j.intimp.2023.110847

9 Skrabal Ross X Gunn KM Suppiah V , et al. A review of factors influencing non-adherence to oral antineoplastic drugs . Support Care Cancer . 2020 ; 28 : 4043 – 4050 . 32335731 10.1007/s00520-020-05469-y

10 Lea CS Bohra S Moore T , et al. Exploring behaviors, treatment beliefs, and barriers to oral chemotherapy adherence among adult leukemia patients in a rural outpatient setting . BMC Res Notes . 2018 ; 11 : 843 . 30497510 10.1186/s13104-018-3935-z PMC6267791

11 Gatea A Al-Jabory M Baiee N . Evaluation of imatinib adherence in chronic myeloid leukemia patients in babylon province, Iraq . Medical Journal of Babylon . 2023 ; 20 : 388 .

12 Boons CCLM Harbers L Timmers L , et al . Needs for information and reasons for (non)adherence in chronic myeloid leukaemia: be aware of social activities disturbing daily routines . Eur J Haematol . 2018 ; 101 : 643 – 653 . 10.1111/ejh.13155 30058149

13 Naser AY Ofori-Asenso R Awawdeh S Al , et al. Real world adherence to and persistence with oral oncolytics in multiple myeloma: a systematic review and meta-analysis . Clin Lymphoma Myeloma Leuk . 2022 ; 22 : 760 – 773 . 35764491 10.1016/j.clml.2022.05.003

14 Kay M Santos J Takane M . MHealth: New Horizons for Health through Mobile Technologies.; 2011 .

15 Puspitasari Aw Kristina SA Kusnadi SA , et al. Digital Intervention to Improve Oral Chemotherapy Adherence: Systematic Review . International Journal of Pharmaceutical Research . 2021 ; 13 : 1310 – 1319 .

16 Kumar D Hasan Y Afroz S . Mobile health monitoring system: a comprehensive review . International Journal of Research Publication and Reviews . 2023 ; 4 : 1922 – 1954 .

17 Bailey SC Oramasionwu CU Wolf MS . Rethinking adherence: a health literacy–informed model of medication self-management . J Health Commun . 2013 ; 18 : 20 – 30 . 10.1080/10810730.2013.825672 PMC3814610 24093342

18 Mason M Harris MR Greer JA , et al. A concept analysis of oral anticancer agent self-management . Cancer Nurs . 2022 ; 45 : E374 – E387 . 10.1097/NCC.0000000000000934 PMC8390565 33654013

19 Báez Gutiérrez N Rodríguez Ramallo H Fernández González M , et al. Smartphone apps for patients with hematologic malignancies: systematic review and evaluation of content . JMIR Mhealth Uhealth . 2022 ; 10 : e35851 . 10.2196/35851 PMC9533204 36125860

20 Chow SM Tan BK . Effectiveness of mHealth apps on adherence and symptoms to oral anticancer medications: a systematic review and meta-analysis . Support Care Cancer . 2024 ; 32 : 426 . 38864924 10.1007/s00520-024-08635-8

21 Vaffis S Whaley S Axon DR , et al . Features of cancer mHealth apps and evidence for patient preferences: scoping literature review . JMIR Cancer . 2023 ; 9 : e37330 . 10.2196/37330 PMC10182455 37115587

22 Hinrichs-Krapels S Grant J . Exploring the effectiveness, efficiency and equity (3e’s) of research and research impact assessment . Palgrave Commun . 2016 ; 2 : 16090 .

23 Kim SY . Efficacy versus effectiveness . Korean J Fam Med. 2013 ; 34 : 227 . 23904951 10.4082/kjfm.2013.34.4.227 PMC3726789

24 Skrabal Ross X Gunn KM Patterson P , et al. Mobile-Based oral chemotherapy adherence-enhancing interventions: scoping review . JMIR Mhealth Uhealth . 2018 ; 6 : e11724 . 10.2196/11724 PMC6320412 30578182

25 Gambalunga F Iacorossi L Notarnicola I , et al. Mobile health in adherence to oral anticancer drugs . CIN: Computers, Informatics, Nursing . 2021 ; 39 : 17 – 23 . 10.1097/CIN.0000000000000643 32568900

26 Dang TH Forkan ARM Wickramasinghe N , et al . Investigation of intervention solutions to enhance adherence to oral anticancer medicines in adults: overview of reviews . JMIR Cancer . 2022 ; 8 : e34833 . 10.2196/34833 PMC9096640 35475978

27 Puspitasari Aw Kristina SA Kusnadi SA , et al. Digital Intervention to Improve Oral Chemotherapy Adherence: Systematic Review . Int J Pharm Res . 2021 ; 13 : 1310 – 1319 .

28 Campbell M McKenzie JE Sowden A , et al . Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline . Br Med J 2020 ; 368 : l6890 . 10.1136/bmj.l6890 PMC7190266 31948937

29 Page MJ McKenzie JE Bossuyt PM , et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews . Br Med J 2021 ; 372 : n71 . 10.1136/bmj.n71 PMC8005924 33782057

30 Koo TK Li MY . A guideline of selecting and reporting intraclass correlation coefficients for reliability research . J Chiropr Med . 2016 ; 15 : 155 – 163 . 27330520 10.1016/j.jcm.2016.02.012 PMC4913118

31 Hong QN Fàbregues S Bartlett G , et al . The mixed methods appraisal tool (MMAT) version 2018 for information professionals and researchers . Educ Inf . 2018 ; 34 : 285 – 291 .

32 Michie S Richardson M Johnston M , et al . The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions . Ann Behav Med . 2013 ; 46 : 81 – 95 . 23512568 10.1007/s12160-013-9486-6

33 Whittaker R Merry S Dorey E , et al. A development and evaluation process for mHealth interventions: examples from New Zealand . J Health Commun . 2012 ; 17 : 11 – 21 . 22548594 10.1080/10810730.2011.649103

34 Popay J Roberts H Sowden A , et al . Guidance on the Conduct of Narrative Synthesis in Systematic Reviews A Product from the ESRC Methods Programme Peninsula Medical School, Universities of Exeter and Plymouth. Published online 2006 .

35 Braun V Clarke V . Using thematic analysis in psychology . Qual Res Psychol . 2006 ; 3 : 77 – 101 .

36 May CR Mair F Finch T , et al . Development of a theory of implementation and integration: normalization process theory . Implement Sci . 2009 ; 4 : 29 . 19460163 10.1186/1748-5908-4-29 PMC2693517

37 LeBlanc MR LeBlanc TW Yang Q , et al. A Mobile App to Support Self-Management in Patients with Multiple Myeloma or Chronic Lymphocytic Leukemia: Pilot Randomized Controlled Trial . JMIR Cancer . 2023 ; 9 . doi: 10.2196/44533 PMC10360008 37410541

38 Verweij L Ector GICG Smit Y , et al . Effectiveness of digital care platform CMyLife for patients with chronic myeloid leukemia: results of a patient-preference trial . BMC Health Serv Res . 2023 ; 23 : 228 . 36890512 10.1186/s12913-023-09153-9 PMC9994406

39 Mehdizadeh H Asadi F Emami H , et al. An mHealth Self-management System for Support Children With Acute Lymphocytic Leukemia and Their Caregivers: Qualitative Co-design Study . JMIR Form Res . 2022 ; 6 . doi: 10.2196/36721 PMC9055480 35228195

40 Ector GI Westerweel PE Hermens RP , et al . The development of a web-based, patient-centered intervention for patients with chronic myeloid leukemia (CMyLife): design thinking development approach . J Med Internet Res . 2020 ; 22 : e15895 . 10.2196/15895 PMC7260663 32412424

41 Pereira-Salgado A Westwood JA Russell L , et al . Mobile health intervention to increase oral cancer therapy adherence in patients with chronic myeloid leukemia (the REMIND system): clinical feasibility and acceptability assessment . JMIR Mhealth Uhealth . 2017 ; 5 : e184 . 10.2196/mhealth.8349 PMC5738545 29212628

42 Dang TH Wickramasinghe N Forkan ARM , et al. Co-Design, development, and evaluation of a Mobile solution to improve medication adherence in cancer: design science research approach . JMIR Cancer . 2024 ; 10 : e46979 . 10.2196/46979 PMC11024750 38569178

43 Verweij L Metsemakers SJJPM Ector GICG , et al. Improvement, implementation, and evaluation of the CMyLife digital care platform: participatory action research approach . J Med Internet Res . 2023 ; 25 : e45259 . 10.2196/45259 PMC10541637 37713242

44 Song CE An M . The self-management smartphone application for cancer survivors, ReLive: development and usability testing . CIN - Computers Informatics Nursing . 2021 ; 39 : 312 – 320 . 10.1097/CIN.0000000000000690 33214389

45 Schofield P Chambers S . Effective, clinically feasible and sustainable: key design features of psycho-educational and supportive care interventions to promote individualised self-management in cancer care . Acta Oncol (Madr) . 2015 ; 54 : 805 – 812 . 10.3109/0284186X.2015.1010016 25813474

46 Murray E Treweek S Pope C , et al . Normalisation process theory: a framework for developing, evaluating and implementing complex interventions . BMC Med . 2010 ; 8 : 63 . 20961442 10.1186/1741-7015-8-63 PMC2978112

47 Davis FD Bagozzi RP Warshaw PR . User acceptance of computer technology: a comparison of two theoretical models . Manage Sci . 1989 ; 35 : 982 – 1003 .

48 Potdar R Thomas A DiMeglio M , et al . Access to internet, smartphone usage, and acceptability of mobile health technology among cancer patients. Published online November 22, 2019. doi:10.21203/rs.2.17660/v1 10.1007/s00520-020-05393-1 32166381

49 Ashruf R Parasuraman S Ismail NE , et al. A review on educational intervention approaches on cancer knowledge and awareness in quasi-experimental studies . J Pharm Bioallied Sci . 2024 ; 16 : 87 – 92 . 39691544 10.4103/jpbs.jpbs_291_24 PMC11648583

50 Leidong W Monachino M Lloyd-Williams D , et al. Mobile apps for cancer patients: Identifying positive impacts and concerns . Digit Health . 2025 ; 11 . doi: 10.1177/20552076241305707 PMC11877465 40041393

51 Langote M Saratkar S Kumar P , et al . Human–computer interaction in healthcare: comprehensive review . AIMS Bioeng . 2024 ; 11 : 343 – 390 .

52 Han K Zo H . Understanding the mobile healthcare applications continuance: the regulatory focus perspective . Int J Med Inform . 2023 ; 177 : 105161 . 37544241 10.1016/j.ijmedinf.2023.105161

53 Cao J Feng H Lim Y , et al. How Social Influence Promotes the Adoption of Mobile Health among Young Adults in China: A Systematic Analysis of Trust, Health Consciousness, and User Experience . Behav Sci . 2024 ; 14 : 468 . 38920829 10.3390/bs14060498 PMC11200938

54 Yadav R Giri A Chatterjee S . Understanding the users’ motivation and barriers in adopting healthcare apps: a mixed-method approach using behavioral reasoning theory . Technol Forecast Soc Change . 2022 ; 183 : 121932 .

55 Ardito V Golubev G Ciani O , et al. Evaluating barriers and facilitators to the uptake of mHealth apps in cancer care using the consolidated framework for implementation research: scoping literature review . JMIR Cancer . 2023 ; 9 : e42092 . 10.2196/42092 PMC10131717 36995750

56 Richards R Kinnersley P Brain K , et al. Cancer clinicians’ views regarding an app that helps patients with cancer meet their information needs: qualitative interview study . JMIR Cancer . 2021 ; 7 : e23671 . 10.2196/23671 PMC8138703 33955836

57 Thomson K Brouwers C Damman OC , et al. How health care professionals evaluate a digital intervention to improve medication adherence: qualitative exploratory study . JMIR Hum Factors . 2018 ; 5 : e7 . 10.2196/humanfactors.8948 PMC5840481 29463494

58 Ayre J Bonner C Bramwell S , et al . Factors for supporting primary care physician engagement with patient apps for type 2 diabetes self-management that link to primary care: interview study . JMIR Mhealth Uhealth . 2019 ; 7 : e11885 . 10.2196/11885 PMC6352005 30664468

59 Daniel F Jabak S Sasso R , et al. Patient-Physician communication in the era of Mobile phones and social Media apps: cross-sectional observational study on Lebanese physicians’ perceptions and attitudes . JMIR Med Inform . 2018 ; 6 : e18 . 10.2196/medinform.8895 PMC5910531 29625955

60 Charbonneau DH Hightower S Katz A , et al . Smartphone apps for cancer: A content analysis of the digital health marketplace . Digit Health . 2020 ; 6 . doi: 10.1177/2055207620905413 PMC7016299 32110428

61 Mobasheri MH Johnston M King D , et al. Smartphone breast applications – what’s the evidence? The Breast . 2014 ; 23 : 683 – 689 . 25153432 10.1016/j.breast.2014.07.006

62 Collado-Borrell R Escudero-Vilaplana V Ribed-Sánchez A , et al. Smartphone applications for cancer patients; what we know about them? Farm Hosp . 2016 ; 40 : 25 – 35 . 26882831 10.7399/fh.2016.40.1.8993

63 Liu P Li X Zhang XM . Healthcare professionals’ and patients’ assessments of listed mobile health apps in China: a qualitative study . Front Public Health . 2023 ; 11 : 1220160 . 37780445 10.3389/fpubh.2023.1220160 PMC10538635

64 Richards R Kinnersley P Brain K , et al. The preferences of patients with cancer regarding apps to help meet their illness-related information needs: qualitative interview study . JMIR Mhealth Uhealth . 2019 ; 7 : e14187 . 10.2196/14187 PMC6693303 31368446

65 Istepanian RSH . Mobile health (m-health) in retrospect: the known unknowns . Int J Environ Res Public Health . 2022 ; 19 : 3747 . 35409431 10.3390/ijerph19073747 PMC8998037

66 Gagnon MP Ngangue P Payne-Gagnon J , et al. m-Health adoption by healthcare professionals: a systematic review . J Am Med Inform Assoc . 2016 ; 23 : 212 – 220 . 26078410 10.1093/jamia/ocv052 PMC7814918

67 Ariens LF Schussler-Raymakers FM Frima C , et al . Barriers and facilitators to eHealth use in daily practice: perspectives of patients and professionals in dermatology . J Med Internet Res . 2017 ; 19 : e300 . 10.2196/jmir.7512 PMC5605757 28874336

68 Vis C Mol M Kleiboer A , et al . Improving implementation of eMental health for mood disorders in routine practice: systematic review of barriers and facilitating factors . JMIR Ment Health . 2018 ; 5 : e20 . 10.2196/mental.9769 PMC5878369 29549072

69 Addotey-Delove M Scott RE Mars M . Healthcare workers’ perspectives of mHealth adoption factors in the developing world: scoping review . Int J Environ Res Public Health . 2023 ; 20 : 1244 . 36673995 10.3390/ijerph20021244 PMC9858911

70 Wu W Graziano T Salner A , et al. Acceptability, effectiveness, and roles of mHealth applications in supporting cancer pain self-management: integrative review . JMIR Mhealth Uhealth . 2024 ; 12 : e53652 . 10.2196/53652 PMC11294773 39024567

71 Magalhães B Fernandes C Martinez-Galiano JM , et al. Exploring the use of Mobile applications by cancer patients undergoing chemotherapy: a scoping review . Int J Med Inform . 2020 ; 144 : 104293 . 33091832 10.1016/j.ijmedinf.2020.104293

72 Bryant AL LeBlanc TW Albrecht T , et al . Oral adherence in adults with acute myeloid leukemia (AML): results of a mixed methods study . Support Care Cancer . 2020 ; 28 : 5157 – 5164 . 32060702 10.1007/s00520-020-05349-5

73 Samadbeik M Garavand A Aslani N , et al. Mobile health interventions for cancer patient education: a scoping review . Int J Med Inform . 2023 ; 179 : 105214 . 37729837 10.1016/j.ijmedinf.2023.105214

74 Flaucher M Zakreuskaya A Nissen M , et al . Evaluating the effectiveness of Mobile health in breast cancer care: a systematic review . Oncologist . 2023 ; 28 : e847 . 10.1093/oncolo/oyad217 PMC10546835 37536278

75 Hassenzahl M Tractinsky N . User experience - a research agenda . Behav Inf Technol . 2006 ; 25 : 91 – 97 .