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Mobile health (mHealth) solutions have proven to be effective in a wide range of patient outcomes and have proliferated over time. However, a persistent challenge of digital health technologies, including mHealth, is that they are characterized by early dropouts in clinical practice and struggle to be used outside experimental settings or on larger scales.
This study aimed to explore barriers and enablers to the uptake of mHealth solutions used by patients with cancer undergoing treatment, using a theory-guided implementation science model, that is, the Consolidated Framework for Implementation Research (CFIR).
A scoping literature review was conducted using PubMed (MEDLINE), Web of Science, and ScienceDirect databases in March 2022. We selected studies that analyzed the development, evaluation, and implementation of mHealth solutions for patients with cancer that were used in addition to the standard of care. Only empirical designs (eg, randomized controlled trials, observational studies, and qualitative studies) were considered. First, information on the study characteristics, patient population, app functionalities, and study outcomes was extracted. Then, the CFIR model was used as a practical tool to guide data collection and interpretation of evidence on mHealth uptake.
Overall, 91 papers were included in the data synthesis. The selected records were mostly randomized controlled trials (26/91, 29%) and single-arm, noncomparative studies (52/91, 57%). Most of the apps (42/73, 58%) were designed for both patients and clinicians and could be used to support any type of cancer (29/73, 40%) and a range of oncological treatments. Following the CFIR scheme (
The hype surrounding mHealth in cancer care is hindered by several factors that can affect its use in real world and nonexperimental settings. Compared with the growing evidence on mHealth efficacy, knowledge to inform the uptake of mHealth solutions in clinical cancer care is still scarce. Although some of our findings are supported by previous implementation research, our analysis elaborates on the distinguishing features of mHealth apps and provides an integrated perspective on the factors that should be accounted for implementation efforts. Future syntheses should liaise these dimensions with strategies observed in successful implementation initiatives.
Mobile health (mHealth) apps, defined by the World Health Organization as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” [
In oncology, mHealth apps have shown to provide benefits to patients throughout the care pathway [
Not only do individual patients benefit from using mHealth solutions, but also the broader health care system. There is a growing interest in the uptake of mHealth solutions in clinical practice because they have the potential to offer more accessible and cost-effective health care solutions [
The potential of mHealth is also reflected at the policy level, with an increasing number of countries gradually adopting regulatory frameworks [
Increased interest in mHealth in cancer care has been observed in the fast-growing number of scientific publications in the past few years. However, most studies have investigated the impact of mHealth apps on patient outcomes. For instance, recent literature reviews have assessed the effect of mHealth apps on pain management in patients with cancer [
Therefore, this study aimed to investigate the determinants of mHealth uptake using a theory-guided framework from implementation science, the Consolidated Framework for Implementation Research (CFIR). The CFIR was intended as a practical tool to map and interpret empirical evidence regarding factors (ie, barriers and facilitators) that could affect the implementation of mHealth in cancer care.
This review follows the updated methodological guidance for scoping reviews [
Web of Science, PubMed (MEDLINE) and ScienceDirect were consulted. The search was extended to the papers published from January 2017 to March 2022. A 5-year timeframe was deemed appropriate considering the sharp increase in the number of studies on the topic and the rapid obsolescence of previous studies. Additional relevant studies were identified by screening the bibliographies of other published reviews (snowballing).
The search strategy was defined jointly by the research team and ultimately built around 2 broad content areas, cancer and mHealth. The exact keyword string used was as follows: (cancer OR tumor OR tumour OR oncolog*) AND (mHealth OR “mobile health” OR phone OR smartphone OR app). The search was restricted to titles and abstracts in PubMed, and to titles, abstracts, and keywords in Web of Science and ScienceDirect.
RefWorks [
Only empirical study designs describing the development, evaluation (including testing), and implementation of an mHealth intervention were included. Other study types, including literature reviews, meta-analyses, conference abstracts, and clinical guidelines, were excluded. Studies were included if they focused on mHealth apps used as support for ongoing cancer therapies or management of related adverse events. Typical app functionalities included, but not limited to, enhancing patient self-monitoring, self-efficacy, or education, as well as fostering patient-clinician communication. Conversely, studies assessing mHealth apps used in other phases of the care pathway (eg, screening, diagnosis, and palliative care) were excluded. mHealth apps exclusively delivering noncore ancillary services for patients with cancer (eg, mental health, physical activity, and smoking cessation) were also out of scope. As for the target mHealth users, only adult patients undergoing cancer treatment were considered, whereas studies on cancer survivors, pediatric populations, or other targets with risky conditions or behaviors (eg, comorbidities) were excluded. Finally, studies not published in English were excluded. A detailed illustration of the inclusion and exclusion criteria is provided in
Study design
Empirical studies (eg, randomized controlled trials, observational studies, pre-post studies, and qualitative designs)
App functionality
Mobile health apps facilitating core cancer treatment delivery (eg, symptom-monitoring, tele-visit, and communication with health care professionals)
Moment of care
Mobile health apps used as a support to ongoing cancer therapies or related adverse events
Target population
Adult patients undergoing cancer treatment
Publication language
English
Publication year
From 2017 (included)
Study design
Literature review, meta-analysis, conference abstract, and clinical guideline
App functionality
Mobile health apps exclusively delivering noncore, ancillary services for cancer patients (eg, exercise programs)
Moment of care
Other phases of the care pathway (eg, screening and prevention, diagnosis, and palliative care)
Target population
Cancer survivors, pediatric populations, or other targets with risky conditions (eg, multimorbidities) or behaviors (eg, smokers)
Publication language
Any other language except English
Publication year
Before 2017
After double-checking a sample with a second reviewer (VA), the researcher GG screened all retrieved articles based on title and abstract, whereas full-text reading was performed by GG and VA. Disagreements regarding the inclusion of a given article were resolved by a third researcher (RT). All researchers agreed on the final selection of the studies selected for data synthesis. Owing to the variety of included studies in terms of design, objectives, and sources of evidence, no assessment of the risk of bias or methodological quality was undertaken.
Data extraction was performed in a Microsoft Excel grid. The extracted data included a general overview of the studies (eg, publication country, study objective, design, and duration), information on study participants (eg, number of participants, age, cancer type and stage, and cancer treatment), information on mHealth apps (eg, use time, app name, and main functionalities), study outcomes, and related metrics. The taxonomy by Dodd et al [
The results were summarized using mainly a narrative synthesis and organized into 2 major sections. First, an overview of the selected studies and underlying app functionalities was provided, including key statistics (eg, count and proportions) and summary characteristics when relevant. Evidence on barriers and enablers specific to mHealth implementation was then analyzed following the CFIR framework. We did not expect to find evidence on every CFIR subdomain in each selected study; therefore, data analysis was conceived as a synthesis of subsets of relevant, available observations.
A total of 6190 papers were identified through the search (2564 records from PubMed, 3626 from Web of Science, and 506 from ScienceDirect). After duplicate removal, 3915 records remained for screening based on the title and abstract. A final number of 91 studies were included for analysis.
PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) flowchart.
Of the 91 studies, 78 (86%) [
In terms of study designs, randomized controlled trials (RCTs), including secondary analyses of RCT data, were the most common (26/91, 29%), followed by mixed-methods studies (24/91, 26%), qualitative design studies (12/91, 13%), pilot studies (11/91, 12%), other non-RCTs (7/91, 8%), pre-post studies (3/91, 3%), quasi-experimental studies (3/91, 3%), and other study designs (5/91, 6%). The majority (52/91, 57%) were single-arm studies, whereas 43% (39/91) of the studies were comparative, with 2 or multiple arms. Most of the included studies had a prospective design (84/91, 92%), 3 were retrospective, and others were combined retrospective and prospective branches (4/91, 4%).
Owing to their heterogeneous nature, the selected articles had different study durations, ranging from 2 weeks for small-scale trials to up to 2 years for larger-scale RCTs. The median sample size of the study participants was 51, ranging from a minimum of 5 to a maximum of 4475 patients.
The 91 studies included for analysis describe 73 mHealth apps, of which 29 (40%) were designed for supporting any cancer types [
Many apps did not support a specific cancer treatment (23/73, 32%) [
mHealth users can be patients, clinicians, a broader pool of health care professionals (HCPs), or different combinations of users. Most commonly, apps are designed for both patients and clinicians (42/73, 58%) [
Summary of app functionalities (n=73).
Characteristics of mHealtha apps | n (%) | |
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Any cancer (ie, generic) | 29 (40) |
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Breast | 17 (23) |
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Gastric and colon | 5 (7) |
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Lung | 3 (4) |
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Thyroid | 3 (4) |
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Hematological | 2 (3) |
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Other forms of cancer | 15 (21) |
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Not specified | 23 (32) |
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Chemotherapy | 15 (21) |
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Oral treatment | 13 (18) |
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Surgery | 8 (11) |
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Radiotherapy | 3 (4) |
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Transplantation | 3 (4) |
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Other | 8 (11) |
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Patients and clinicians | 42 (58) |
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Patients only | 23 (32) |
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Patients, clinicians, and caregivers | 3 (4) |
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Patients and caregivers | 2 (3) |
|
Other combinations | 3 (4) |
amHealth: mobile health.
The selected studies assessed mHealth impact using a wide range of outcome metrics analyzed using the taxonomy by Dodd et al [
As for the
Outcomes according to the taxonomy by Dodd et al [
Core area |
Count | Examples | |||
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1. Mortality or survival | 1 | Overall survival | ||
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9. General outcomes | 12 | MDASIa | ||
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16. Outcomes relating to neoplasms: benign, malignant and unspecified | 4 | LARSb | ||
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25. Physical functioning | 8 | KPSc | ||
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26. Social functioning | 7 | PAM-13d | ||
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28. Emotional functioning and well-being | 16 | HADSe | ||
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30. Global quality of life | 37 | EORTC QLQ-C30f | ||
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32. Delivery of care | 73 | SUSg | ||
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34. Economic | 1 | Health resource use (cost) | ||
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35. Hospital | 10 | Reduction in unexpected visits to EDh | ||
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37. Societal burden | 7 | MSPSSi | ||
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38. Adverse events and effects | 9 | CTCAEj |
aMDASI: MD Anderson Symptom Inventory.
bLARS: low anterior resection syndrome score.
cKPS: Karnofsky Performance Status.
dPAM-13: Patient Activation Measure–13.
eHADS: Hospital Anxiety and Depression Scale.
fEORTC QLQ-C30: European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire.
gSUS: System Usability Scale.
hED: emergency department.
iMSPSS: Multidimensional Scale of Perceived Social Support.
jCTCAE: Common Terminology Criteria for Adverse Events.
App characteristics are important predictors of intervention implementation in later stages. Regarding the
The surge in the use of mHealth has attained new social needs and external policy pressures. Nearly every study stems from well-identified
To address these newly developed needs or emerging social pressures,
Finally,
The inner setting refers to both structural characteristics that facilitate the implementation process and to dedicated activities activated by the recipient organizations along the way.
Adopting mHealth apps is perceived as a
Regarding the
The likelihood of embracing a new health intervention also depends on the characteristics of the individuals who will use it. First, individuals’
The perception that individuals have about their ability to use a given intervention and how it changes over time falls under the
I
Finally, among
Built on 4 dimensions (planning, engaging, executing, reflecting, and evaluating),
Key barriers and enablers of mHealth uptake are illustrated in
Summary of key identified enablers and barriers to mobile health implementation.
CFIRa construct and enablers | Barriers | |
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User-friendly interfaces Pretesting through small-scale pilot trials Patient’s and HCP’sb involvement in the app development |
Release of many subsequent app versions Data privacy |
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New patient needs (eg, need for constant monitoring, or real-time communication with HCPs) External policies and incentives fostering digital health Scarcity of resources and need to search more cos-effective ways to deliver health services |
Unharmonized regulatory provisions across EUc countries Tendency not to leverage on networks (ie, unrealized synergies of economies of scales |
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Interoperability with IT systems Workforce shortages New care pathways for cancer (eg, outpatient settings) Social endorsement (eg, peer referral) |
HCPs’ perception of extra workload (eg, more data input) Clinician concern from following-up more patients Linkage between app uptake and incentives only possible at organizational level |
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Routine use of smartphones, regardless of age Positive attitude toward digital health |
Cultural norms (eg, smartphone use in the workplace Perceived poorer communication with HCPs Weakened sense of identification with health service providers |
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Training on app benefits and functioning Nurses’ active support |
Unclear contribution of different stakeholders to implementation Implementation plans missing or poorly defined |
aCFIR: Consolidated Framework for Implementation Research.
bHCP: health care professional.
cEU: European Union.
The overarching aim of this study was to investigate the determinants of mHealth uptake to inform the translation efforts of mHealth interventions in routine care. Studies illustrating the development, evaluation, and implementation of mHealth apps for cancer patients were considered, and information on barriers and enablers of app uptake was extracted following the CFIR scheme.
Many facilitators of app implementation in clinical settings have been identified. The involvement of patients and HCPs in app development has frequently been observed. Codevelopment was presented as a way to include desired mHealth features in early design efforts, to prevent unnecessary shortcomings, and activate a sense of ownership. These findings corroborated the idea that users should be intimately involved in the identification, design, and conduct phases of research, and not just be targets for the dissemination of study results [
As for implementation barriers, gradual rollouts and subsequent app version releases could be perceived as burdensome. From the provider’s perspective, mHealth could be referred to as a source of extra workload for the clinical staff. Conversely, factors characterizing providers, such as organizational leaders and management, staff, and culture, which can influence their ability to adapt and successfully use an intervention, were not systematically observed. From the user’s perspective, the fear of poorer patient-clinician interactions (eg, through remote monitoring) can diminish the sense of trust in the organization, in line with what was observed in prior works [
Although some of the findings discussed above are supported in previous research [
mHealth will become increasingly important. On one hand smartphones are becoming increasingly prevalent and provide augmented functionalities (eg, cameras to capture high definition images of body parts). In contrast, demographic and epidemiological trends report a boom in chronic conditions, whose needs can be addressed by mHealth. Digitalization of the health care sector is a key priority in the political agenda, as confirmed by the expected massive capital injection in response to the COVID-19 pandemic. With more than €750 billion (US $798.38 billion), the next-generation European Union fund will invest a relevant share in promoting digital health, further boosting the development of mHealth apps. Although a stronger financial commitment is advocated [
To our knowledge, this is the first review of the literature that uses a theory-guided framework to explore the determinants of mHealth implementation using a comprehensive approach in the area of cancer care. Other syntheses of primary studies mostly investigate the distinguishing features of mHealth [
This study has several limitations. First, the papers selected for analysis were heterogeneous in terms of study characteristics (eg, purposes, study setting, design, duration, number, and types of participants). The decision to include a diverse range of studies was justified by the exploratory nature of scoping reviews [
This review sheds light on the determinants of mHealth uptake in clinical practice, exploring the barriers and enablers of the implementation of cancer care apps using an established implementation science framework. It contributes to filling the knowledge gap by systematizing the dimensions that should be factored into when designing an implementation strategy for mHealth apps.
Future studies should investigate whether and how specific dimensions such as app development and deployment platforms could affect implementation-related elements. In addition, a core set of outcomes associated with successful implementation, measured in studies that discuss implementation initiatives including hybrid designs, should be developed [
The 22-item Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for scoping reviews.
Consolidated Framework for Implementation Research domains and constructs.
Overview of the descriptive statistics.
Characteristics of the selected studies.
Consolidated Framework for Implementation Research
digital health technology
Digital Health Applications
health care professional
mobile health
Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
randomized controlled trial
This study is part of a larger research conducted by CERGAS SDA Bocconi School of Management, which received unconditioned funding from Roche Italy. VA, OC, and RT designed this study. VA and GG developed the search strategy for this study. GG conducted the original literature searches and VA and GG were involved in data screening and study selection. GG extracted the data, while VA checked the extracted data for consistency, and RT mediated where there was disagreement or uncertainty regarding inclusion. VA and GG synthesized the findings. All authors have contributed to and approved the final manuscript. The data sets analyzed during this study are available from the corresponding author upon reasonable request. Requests for materials should be sent to VA.
None declared.