Published on in Vol 10, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35851, first published .
Smartphone Apps for Patients With Hematologic Malignancies: Systematic Review and Evaluation of Content

Smartphone Apps for Patients With Hematologic Malignancies: Systematic Review and Evaluation of Content

Smartphone Apps for Patients With Hematologic Malignancies: Systematic Review and Evaluation of Content

Review

1Hospital Universitario Reina Sofía, Córdoba, Spain

2Hospital Universitario Virgen del Rocío, Sevilla, Spain

*these authors contributed equally

Corresponding Author:

Héctor Rodríguez Ramallo, PharmD

Hospital Universitario Virgen del Rocío

Avenida Manuel Siurot, s/n

Sevilla, 41013

Spain

Phone: 34 688 933 454

Email: hector.rodriguez.sspa@juntadeandalucia.es


Background: Hematological malignancies (HMs) are a heterogeneous group of cancers representing a significant cause of morbidity and mortality. The chronification of HMs and the increasing use of smartphones may lead patients to seek their current unmet needs through mobile health apps.

Objective: The goal of this review was to identify and assess the quality of smartphone apps aimed at patients diagnosed with HMs.

Methods: A systematic search of apps that were aimed at patients diagnosed with HMs, accessed from a Spain IP address, and were available on the iOS (App Store) and Android (Google Play) platforms was conducted in November 2021. The search terms used were “hematology,” “blood cancer,” “leukemia,” “lymphoma,” and “myeloma” apps in English, Spanish, or both languages. The identified apps were downloaded and analyzed independently by 2 reviewers. Information about general app characteristics was collected. The Mobile Application Rating Scale (MARS) was used to assess quality. The resulting parameter of the analyses, the mean score of the apps, was compared by Student t test.

Results: Overall, 18 apps were identified; 7 were available on Android, 5 were available on iOS, and 6 were available on both platforms. All included apps were free; 3 were published in 2021, and among the apps published before 2021, only 6 were updated in 2021. Most (16/18, 89%) of the apps were aimed at patients with leukemia or lymphoma (16). The primary purposes of the apps were to provide general information about the condition (16/18, 89%) and monitor symptoms and clinical parameters (11/18, 61%). Health care professionals contributed to the development of 50% (9/18) of apps; 6 were owned and supported by scientific societies, and 3 were developed with the participation of health care professionals. The mean MARS score for the overall quality of the apps was 3.1 (SD 1.0). The engagement and aesthetics subscales were the lowest rated subscales, with only 44% (8/18) and 67% (12/18), respectively, of the apps obtaining acceptable scores. None of the included apps proved clinical efficacy through clinical trials in patients with HMs. Statistically significant differences were found in the MARS scores between operating systems (+1.0, P=.003) in favor of iOS apps. The participation of health care professionals in the development of the apps did not have a statistically significant impact on the MARS scores.

Conclusions: This systematic search and evaluation identified few acceptable quality mobile apps for patients with HMs. Current and future apps for patients with HMs should provide evidence-based valuable information, improve user engagement, incorporate functions according to patient preferences, and generate evidence regarding the efficacy of app use by patients with HMs.

JMIR Mhealth Uhealth 2022;10(9):e35851

doi:10.2196/35851

Keywords



Hematological malignancies (HMs) are a heterogeneous group of cancers that affect hematopoietic and lymphoid tissue. These disorders constitute 6.8% of all cancers, representing a significant cause of morbidity and mortality [1]. In the past decade, an increased incidence rate of HMs has been described [2,3], possibly due to a growth in life expectancy and the aging of the population [4].

HMs are frequently aggressive and require urgent, lengthy, and burdensome treatment. The complexity of HM treatments could reduce patient adherence and increase the risk of potential drug-drug interactions and adverse events [5-10]. Furthermore, patients with HMs and, specifically, patients with lymphoma and myeloma often experience psychological despair and poor quality of life throughout their illness course [11-13]. Although HM treatments add a burden to various aspects of a patient’s life, the wide range of treatments now available has significantly improved the management of these patients, often transforming HMs into chronic diseases with long-term survival [14,15].

The chronification of HMs in the digital era may lead patients to seek their current unmet needs through internet-based health care, mobile health (mHealth), and, specifically, smartphone apps. An increase in mHealth use is a likely scenario, since the use of smartphones is growing, with estimates indicating that 77.6% of Europeans own a smartphone [16]. In this context, health apps designed for patients with HMs could serve as additional tools for telemedicine, patient education and life coaching, medication adherence, communication, and social media connections [17-19].

As the number of health-related apps has increased rapidly in the last decade, there is an increasing need for research to assess the quality of eHealth tools and identify patients’ unmet needs.

This study aimed to identify apps that are accessible from Spain and are designed for patients diagnosed with HMs, analyze their characteristics, and evaluate their quality with the Mobile Application Rating Scale (MARS) [20].


Search Strategy

A systematic search of apps that were aimed at patients diagnosed with HMs, accessed from a Spain IP address, and were available on the iOS (App Store) and Android (Google Play) platforms was conducted from November 10, 2021, to November 25, 2021. The search was carried out following the PRISMA (Preferred Reporting Items for a Systematic Review and Meta-analysis) guidelines [21].

The search terms used were “hematology,” “blood cancer,” “leukemia,” “lymphoma,” “myeloma,” and their equivalents in the Spanish language—“hematología,” “cáncer de Sangre,” “leucemia,” “linfoma,” and “mieloma,” respectively.

The inclusion criteria used for selection were apps available in English or Spanish that were targeted at patients with HMs and included general information about HMs. The exclusion criteria used were apps that included inappropriate content (including horoscopes and astrology, among others), apps exclusively for fundraising for HMs that did not provide functions or tools designed for their use, or apps with nonfunctional links. The apps analyzed had to comply with all inclusion criteria to be included in the data extraction phase.

The names and descriptions of the apps from the search on Google Play and the App Store were selected against a priori selection criteria. The apps that met the inclusion criteria were downloaded for further screening using an Mi 9 Lite smartphone (Android version 10, Xiaomi Inc) for Google Play apps. For the apps downloaded from the App Store, an iPad Pro 2020 (Apple Inc) running iPadOS 15.1 was used. For apps duplicated across stores, the iOS app was evaluated because of the lower number of apps in the App Store compared with those in Google Play.

Data Extraction

Data were obtained from the App Store and Google Play online app descriptions (app characteristics and the narrative text) by 2 independent researchers. Data were extracted and entered into a structured Excel database (Microsoft Corp).

The variables collected for each app were the name, developer/owner name, type of developer/owner (commercial, scientific society, patient association), platform (Android or iOS), language (English or Spanish), app store category (eg, education, health and fitness, medicine), cost (€), date of publication (year), date of the last update (year), app file size (MB), app version, participation of health care professionals in the design and development of the app (yes/no), and HM targeted (eg, acute lymphocytic leukemia, multiple myeloma, Hodgkin lymphoma).

The participation of health care professionals was considered when specified in the app description or when the app was developed by a health organization (eg, a scientific society or a hospital). The purposes of the apps were classified into the following categories: assessment (eg, providing clinical scales, classifying adverse event severity, or interpreting laboratory findings), general information (eg, information about HMs, medications, or adverse events), the monitoring of clinical parameters (eg, register of laboratory parameters or symptoms), register of patient activities (eg, calendars for patients to add appointments and treatment administration), and contact with health care professionals or other patients.

A descriptive analysis was developed with continuous and discrete variables presented as mean and standard deviation and frequency and percentage, respectively. The means of continuous variables were compared using a t test. Results with a P value of <.05 were considered statistically significant. The generated data were analyzed using SPSS (version 26.0, IBM Corp).

App Quality Evaluation

To evaluate app quality, the MARS tool was used [20]. The MARS is a validated system to assess health apps that has been described as the most comprehensive for evaluating technical information and capabilities of apps [20,22,23]. This tool has been widely used to evaluate health apps designed for many diseases [24-30].

The MARS comprises 23 items across 5 subscales:

  • Engagement: evaluates the entertainment, interest, customization, interactivity, and adequacy of the target group
  • Functionality: assesses the performance, ease of use, navigation, and gestural design of the app
  • Aesthetics: examines the layout, graphics, and visual appeal of the app
  • Information: assesses the accuracy of the description, establishment of goals, quality and quantity of information, visual information, credibility of the source, and evidence-based development of the app
  • Subjective quality: determines willingness to recommend app, times app will be used, willingness to pay, and overall rating of app

Each criterion is evaluated from 1 to 5 (1=inadequate; 2=poor; 3=acceptable; 4=good; 5=excellent). A mean score of the 5 subscales is calculated to describe overall quality.

Before the app search was carried out, reviewers read and became acquainted with the MARS tool. All authors then discussed each rating criteria to achieve a consensus on how to apply them. The first app included was evaluated concurrently by all reviewers to ensure a common understanding and application of the MARS tool. The apps included were independently assessed by 2 reviewers, whose scores were then compared, and a final MARS score was agreed upon. A third reviewer was invited to resolve discrepancies if a consensus on the final MARS score was not reached.

In order to quantify the subjectivity degree of the reviewers’ evaluations, the interrater reliability of the quality scores between the independent evaluations was calculated using the intraclass correlation coefficient (ICC) for a 2-way random effects model.


Overview

The titles, descriptions, and screenshots of 1390 apps were screened in a pair-review fashion. Overall, 18 apps met the inclusion criteria for a comprehensive evaluation with the MARS tool (Figure 1).

Figure 1. Prisma Flow-Chart.
View this figure

App Characteristics

Table 1 displays the general features of the apps. Regarding the apps available on the platforms searched, 7 were available on Google Play (Android), 5 were available on the App Store (iOS), and 6 were available on both platforms. The apps were categorized in different sections in their respective platforms, as follows: medicine (11), health and well-being (4), education (1), news (1), and books and reference works (1). All apps evaluated were free. The average file size of the apps was 27.2 (SD 25.3) MB.

Table 2 displays apps’ purposes and health care professional involvement in their design. All 7 apps with a unique purpose were designed to provide information about HMs and their management. Eleven apps had multiple purposes, the most frequent being providing information and monitoring symptoms and clinical parameters. Overall, 9 of the apps were developed with the participation of health care professionals, 6 were owned and supported by scientific societies, and 3 were developed with the participation of physicians or nurses. None of the included apps reported clinical efficacy through clinical trials in patients with HMs.

Table 1. Smartphone app technical characteristics.
NameYear of publicationYear of the last updatePlatformLanguage



AndroidiOSEnglishSpanish
ALL Manager20192021

CLL Manager20172021

CLL Watch and Wait Tracker20172020
CML Life20182019
CML Today20152015
Don’t Walk Alone20172018

Focus on Lymphoma20132018

Hodgkin Lymphoma Manager20192021

Leucemia: Síntomas Y Tratamiento: FAQ20182018

Leukaemia (Leukaemia) News20202020

Leukemia: Causes, Diagnosis, and Treatment20212021

Leukemia Disease20172017
Leukemia Drug Tracker20212021

LLS CAR T20202020
LLS Health Manager20192021
LRF Understanding Lymphoma20202021
Mieloma20212021

Multiple Myeloma Manager20152021

Table 2. App purpose and health care professional involvement.
NameHematologic malignancyPurposeHPPa
 
IbMcRdCe,fCPg
ALL ManagerALLh
CLL ManagerCLLi
CLL Watch and Wait TrackerCLL


CML LifeCMLj


CML TodayCML


Don’t Walk AloneCLL
Focus on LymphomaLymphoma
Hodgkin Lymphoma ManagerHLk
Leucemia: Síntomas Y Tratamiento: FAQLeukemia




Leukaemia (Leukaemia) NewsLeukemia




Leukemia: Causes, Diagnosis, and TreatmentLeukemia




Leukemia DiseaseLeukemia




Leukemia Drug TrackerLeukemia


LLS CAR THMl



LLS Health ManagerHM
LRF Understanding LymphomaLymphoma



MielomaMMm



Multiple Myeloma ManagerMM

aHPP: health care professional participation.

bI: general information.

cM: monitoring of symptoms and clinical parameters.

dR: register of patient activities.

eC: contact with health care professionals.

fThis feature was included in the app but was not available for Spanish patients.

gCP: contact with other patients.

hALL: acute lymphocytic leukemia.

iCLL: chronic lymphocytic leukemia.

jCML: chronic myeloid leukemia.

kHL: Hodgkin lymphoma.

lHM: hematological malignancy.

mMM: multiple myeloma.

App Quality Assessment

Tables 3 and 4 display the MARS subscales scores for the individual apps. The scores in the functionality section were similar between the apps. The movements between menus and screens were fast and satisfactory for most apps. The most remarkable differences were found in the information and aesthetics sections, for which only 67% (12/18) and 61% (11/18) of the apps showed acceptable scores (>3), respectively. None of the apps were tested in clinical trials.

The mean MARS score was 3.1 (SD 1.0), considering this value as acceptable. Table 5 contains the overall quality scores. For 5 apps, the mean MARS score was >4, meaning these apps were of good quality. On the other hand, 7 apps received a mean score of <3 and were classified as poor-quality apps.

iOS apps obtained better MARS scores when compared to apps for Android (+1.0) points when comparing apps based on their platforms. This difference was statistically significant for the mean MARS score (P=.003) and for all 5 domains: engagement (+1.0; P=.001), functionality (+0.6, P=.05), aesthetics (+1.1, P=.01), information (+1.0, P=.01), and subjective evaluation (+1.1, P=.002). For apps available on both platforms, the MARS mean scores were higher than those for apps only available on one of the platforms (+0.6, P=.19). The aesthetic domain score was statistically superior for apps available on both platforms (+1.2, P=.04). The participation of health care professionals had a positive impact on the appraisals of the apps (+0.6 points), but no statistically significant differences were found (P=.22).

For the overall MARS ratings, the ICC was 0.94 (95% CI 0.75-0.98), confirming excellent interrater reliability. For the engagement, functionality, aesthetics, information, and subjective domains, the ICCs were 0.84, 0.86, 0.94, 0.95, and 0.93, respectively.

Table 3. The Mobile Application Rating Scale engagement and functionality subscales.
NameEngagement, scoreFunctionality, score
 EntaIntbCuscIydTgePerfEUgNavhGDi
ALL Manager444444444
CLL Manager444444444
CLL Watch and Wait Tracker233245344
CML Life444243344
CML Today233245444
Don’t Walk Alone233343333
Focus on Lymphoma444354545
Hodgkin Lymphoma Manager444444444
Leucemia: Síntomas Y Tratamiento: FAQ221133333
Leukaemia (Leukaemia) News221132422
Leukemia: Causes, Diagnosis, and Treatment211112211
Leukemia Disease221132333
Leukemia Drug Tracker212232333
LLS CAR T441145455
LLS Health Manager433444444
LRF Understanding Lymphoma441144343
Mieloma122323243
Multiple Myeloma Manager444444444
Overall, mean (SD)2.9 (1.1)3 (1.1)2.6 (1.3)2.4 (1.2)3.6 (0.9)3.5 (1.0)3.4 (0.8)3.6 (0.9)3.5 (1.1)

aEnt: entertainment.

bInt: interest.

cCus: customization.

dIy: interactivity.

eTg: target group.

fPer: performance.

gEU: ease of use.

hNav: navigation.

iGD: gestural design.

Table 4. The Mobile Application Rating Scale aesthetics and information subscales.
NameAesthetics, scoreInformation, score
 LayaGrabVAcAADdGoaeQIfQyIgVIhCrei
ALL Manager444535543
CLL Manager444535543
CLL Watch and Wait Tracker4345N/Aj43N/A3
CML Life4344N/A3343
CML Today43344N/AN/A43
Don’t Walk Alone5443N/A2422
Focus on Lymphoma5445N/A5543
Hodgkin Lymphoma Manager444535543
Leucemia: Síntomas Y Tratamiento: FAQ1211N/A21N/A1
Leukaemia (Leukaemia) News1214N/A13N/A2
Leukemia: Causes, Diagnosis, and Treatment1212N/A12N/A1
Leukemia Disease1211N/A22N/A1
Leukemia Drug Tracker2124N/A31N/A1
LLS CAR T5545N/A4453
LLS Health Manager434544343
LRF Understanding Lymphoma4555N/A5443
Mieloma3124N/A33N/A3
Multiple Myeloma Manager444535543
Overall, mean (SD)3.3 (1.5)3.1 (1.2)3.1 (1.4)4 (1.4)3.3 (0.5)3.5 (1.5)3.4 (1.4)4 (0.7)2.4 (0.8)

aLay: layouts.

bGra: graphics.

cVA: visual appeal.

dAAD: accuracy of app description.

eGoa: goals.

fQI: quality of information.

gQyI: quantity of information.

hVI: visual information.

iCre: credibility.

jN/A: not applicable.

Table 5. The Mobile Application Rating Scale overall quality scores.
NameEngagement (score), mean (SD)Functionality (score), mean (SD)Aesthetics (score), mean (SD)Information (score), mean (SD)Subjective quality (score), mean (SD)Overall score, mean (SD)
ALL Manager4 (0.0)4 (0.2)4 (0.0)4.2 (1.0)4.3 (0.8)4.1 (0.1)
CLL Manager4 (0.0)4 (0.2)4 (0.0)4.2 (1.0)4.3 (0.8)4.1 (0.1)
CLL Watch and Wait Tracker2.8 (0.8)4 (0.7)3.7 (0.5)3.8 (1.0)2.8 (0.4)3.4 (0.6)
CML Life3.6 (0.9)3.5 (0.5)3.7 (0.5)3.5 (0.6)2.4 (0.8)3.3 (0.5)
CML Today2.8 (0.8)4.3 (0.4)3.3 (0.5)3.8 (0.5)2.5 (0.9)3.3 (0.7)
Don’t Walk Alone3 (0.7)3 (0.0)2.8 (0.2)2.6 (0.9)1.8 (0.4)2.7 (0.5)
Focus on Lymphoma4 (0.7)4.6 (0.5)4.3 (0.5)4.4 (0.9)3.6 (0.5)4.2 (0.4)
Hodgkin Lymphoma Manager4 (0.0)4 (0.2)4 (0.0)4.2 (1.0)4.3 (0.8)4.1 (0.1)
Leucemia: Síntomas Y Tratamiento: FAQ1.8 (0.8)3 (0.0)1.3 (0.5)1.3 (0.5)1.5 (0.4)1.8 (0.7)
Leukaemia (Leukaemia) News1.8 (0.8)2.5 (0.9)1.3 (0.5)2.5 (1.3)1.8 (0.4)2.0 (0.5)
Leukemia: Causes, Diagnosis, and Treatment1.2 (0.5)1.5 (0.5)1.3 (0.5)1.5 (0.6)1.1 (0.2)1.3 (0.2)
Leukemia Disease1.8 (0.8)2.8 (0.4)1.3 (0.5)1.5 (0.6)1.3 (0.3)1.8 (0.6)
Leukemia Drug Tracker2 (0.7)2.8 (0.4)1.7 (0.5)2.3 (1.5)1.5 (0.4)2.0 (0.5)
LLS CAR T2.8 (1.7)4.8 (0.4)4.7 (0.5)4.2 (0.8)2.3 (0.8)3.8 (1.1)
LLS Health Manager3.6 (0.5)4 (0.0)3.7 (0.5)3.8 (0.8)2.5 (0.4)3.5 (0.6)
LRF Understanding Lymphoma2.8 (1.7)3.5 (0.5)4.7 (0.5)4.2 (0.8)3.1 (0.7)3.7 (0.8)
Mieloma2.0 (0.7)3 (0.7)2 (0.8)3.3 (0.5)1.7 (0.4)2.4 (0.7)
Multiple Myeloma Manager4 (0.0)4 (0.2)4 (0.0)4.2 (1.0)4.3 (0.8)4.1 (0.1)
Overall2.9 (0.9)3.5 (0.8)3.1 (1.3)3.3 (1.1)2.6 (1.1)3.1 (1.0)

Principal Findings

Health apps have the potential to become a standard of care for chronic patients. Apps have been associated with positive patient outcomes such as improved adherence to medication and quality of life and decreased use of health care–related resources [31]. Therefore, health apps may offer valuable tools for patients with HMs in a health care context [17,32-34].

This systematic review highlighted the limited number of apps available for patients with HMs. The number of apps identified in this study was significantly lower than previously published reviews on COVID-19, neoplasms, and specific types of cancer such as breast cancer [35-37]. The apps were developed for a narrow range of diseases that HMs encompass. Currently, patients with acute myeloid leukemia or non-Hodgkin lymphoma do not have access to eHealth tools in the main app stores that specifically target these diseases. In addition, there is a lack of apps specifically designed for children, regardless of them being a significant population of patients with HMs who have distinct needs [38,39].

The low number of apps found in this review may be due to several reasons. First, HMs are relatively rare; according to the Global Cancer Observatory, HMs account for an estimated 6.8% of all cancers worldwide [1]. Similarly, systematic searches for diseases with low prevalence tend to find few apps directed to patients [1,24,40,41]. Second, strict exclusion criteria were applied for app selection—specifically, the exclusion of apps aimed at health care professionals, which may encompass half of the health apps [42]. Third, HMs often affect older patients, an age group for which developers may often not design eHealth tools [43,44]. Older age has been associated with a lack of interest in eHealth and smartphones [45,46]. This population also faces additional barriers to using eHealth, such as physical disability and technology inexperience [47,48].

All apps included for evaluation were designed to serve as an information source or monitoring tool for medications and symptoms. The information was not frequently reviewed and updated, as only half of the HM apps (9/18, 50%) were updated during 2021. The management of HMs is in continuous change, often requiring guidelines and recommendations to be updated with frequency, often yearly. As an illustration of this constant evolution, the European Medicines Agency has recommended for approval an average of 5 drugs for HMs per year [49]. The growing arsenal of new treatments highlights the importance of regularly updating apps according to changes in guidelines and recommendations, as the management of HMs is a complex field of constant change [50].

Among the few apps that complied with the inclusion criteria for this study, the overall quality was acceptable. The highest rated subscale on the MARS was functionality due to app efficiency and ease of use. On the other hand, the engagement subscale was the worst evaluated, specifically the customization and interactivity items. Poor results in the engagement domain could mean that even if quality apps include valuable tools, patients will use them for limited periods. Furthermore, the customization of app interactions by setting personalized reminders is perceived as highly beneficial by patients with HMs [51-53]. Finally, the credibility item was poorly evaluated, as the existence of commercial interests or the source’s legitimacy could not be verified.

Currently, there is a lack of high-quality evidence on the eHealth needs of patients with HMs. Surveys about communication technologies conducted on hematology and oncology patients found that mobile phones were the most frequently used device to search for health-related information. Patients were highly interested in staying informed about health issues, disease prevention, healthy lifestyles, and general information about the disease [31,54]. Patients seem to be interested in eHealth helping with practical issues, such as appointment management, the provision of advice on disease and symptom management, and direct communication with health care professionals in addition to providing information. Patients were less interested in features that can add additional burdens to their daily activities, such as recording and monitoring medication, symptoms, and adverse events [31]. Patients with HMs also desire the creation of communication channels that allow health care professionals to answer their concerns rapidly [31]. A scoping review of virtual care in patients with HMs described high patient satisfaction with telemedicine interventions that allowed clinicians and nurses to communicate via phone calls or videoconferencing [33].

The 18 apps that were designed for patients with HMs and were evaluated in this review did not fulfill the previously described preferences. Although 17 apps provided information about the disease, most of the information did not focus on disease prevention, symptom management, or healthy lifestyles. In addition, direct communication with health care professionals for Spanish patients was not possible, and the management of medical appointments was not an included feature. Further research on the virtual care needs of patients with HMs is warranted; surveys and validated questionnaires explicitly designed for patients with HMs are needed to gain knowledge of the unmet needs of these patients. Understanding patient preferences through a continuous user-centered design is essential for the success of mHealth for patients with HMs [50,55]. The differences found between features included in apps and the preferences of patients with HMs highlight the apparent necessity of their participation in the design of health-related apps. The inclusion of health care professionals may not be sufficient to adequately provide quality apps that cover patient needs, as there is a divide between the expectations and needs of patients and those identified by health care professionals [56,57].

Among the stakeholders involved in the management of HMs, the participation of health care professionals in the development of mHealth apps is frequently described as a critical factor related to their general quality and, specifically, the quality of the information provided [24,25,58]. A systematic review that evaluated expert participation and adherence to medical evidence in mHealth apps showed that the lack of participation of health care professionals and inclusion of evidence-based information were persistent characteristics of the available health apps [58]. However, even when these factors are potentially related, expert involvement in the design does not guarantee adherence to evidence-based content [58,59]. In this review, half of the apps (9/18, 50%) targeting patients with HMs included the participation of health care professionals in the development phase. Nevertheless, we did not find statistically significant differences in the quality scores based on this participation. Furthermore, a cross-sectional survey of oncology and hematology patients found that more than 80% of patients would use an app if a health professional recommended it [60]. These data suggest that health care professionals may also play a crucial role in high-quality apps reaching patients with HMs.

Due to the complex nature of HMs, which often require highly complex and burdensome therapeutic protocols, we could presume that patients would benefit from additional tools that help manage different aspects of the disease. However, the role of apps in the care of patients with HMs is uncertain; a review exploring the research on eHealth intervention in HMs found that few trials were designed exclusively for patients with HMs. These studies generally included mixed cancer groups composed of a small sample of hematology patients; moreover, the principal study results were not presented separately [34]. Similarly, among the 18 apps evaluated in this review, none assessed the clinical impact of the intervention through clinical trials; this finding is common among studies that evaluate health apps, with authors often stating the need for studies that assess the efficacy of health apps [23-25,37,61,62]. Additional randomized controlled trials testing the efficacy of apps are warranted before implementing this technology for managing HMs, as eHealth may also increase the burden of treatment on vulnerable patients [63,64].

Recommendations and Future Research for HM App Development

Current and future apps for patients with HMs should focus on improving the lowest rated subscales of the MARS scale: engagement and aesthetics. Specifically, improvements are needed in the interactivity, customization, and entertainment features, as well as in graphical design and visual appeal. Further refinement of the information provided may be needed in order to deliver updated evidence-based information adapted to the necessities and preferences of patients with HMs. In this context, HM apps should be developed through a user-centered, collaborative design process with the participation of all stakeholders involved. The participation of health care professionals can increase the credibility of apps and trustworthiness of sources [24,25,58]. Furthermore, the participation of patients with HMs is essential for developing apps that fulfill their unmet needs, increasing their usability [56,57]. App developers and stakeholders should work to include apps in specialized and primary care assistance circuits by adding functions that facilitate the access of patients with HMs to health assistance through the management of appointments and direct communication with health care professionals [31,54]. Regular updates of app contents should be performed to incorporate information that is consistent with recently generated evidence.

Future research in the field of HM apps should focus on gaining knowledge regarding the eHealth needs of patients with HMs, conducting surveys that investigate the changing needs of specific groups of patients with HMs, measuring the existence of health app safety concerns, and generating evidence-based data on the effectiveness of eHealth interventions through apps in patients with HMs.

Limitations

This systematic review has several limitations. First, the reviewers assigned a score for each category using brief descriptions of what makes an excellent, regular, or mediocre app. Thus, there was a degree of subjectivity when evaluating apps using the MARS [21,22]. However, the multiple reviewer design of this study may narrow the impact of this limitation because of the high degree of interreviewer agreement observed. Second, despite the MARS being a validated tool that is widely used to assess the quality of apps for many diseases [23-29], it does not consider aspects such as privacy, security, and the frequency of updates to apps, which have been considered aspects of particular relevance when evaluating the quality of health software [65]. Third, as health care professionals, the authors evaluated apps aimed at patients; this has limitations, as we may not fully comprehend their needs and preferences [56,57]. Fourth, the search was limited to 2 app platforms: Google Play and the App Store. Although these platforms encompass the vast majority of apps available on the market [66], several platforms (Windows Phone, Blackberry Market, etc) were not included in this review due to the lack of availability of devices for performing the search among the reviewers. Therefore, the possibility exists that some apps dedicated to HMs were missed.

Conclusion

The potential for eHealth to improve the care of patients with HMs exists. However, current apps need further refinement in order to provide evidence-based valuable information, keep users engaged, and provide a visually appealing interface. Moreover, there is a need to generate evidence on the efficacy of eHealth interventions in patients with HMs.

Future eHealth tools for patients with HMs should consider including all stakeholders in the design phase. Notably, the participation of patients in this phase will serve to design and implement tools according to their currently unmet needs.

Acknowledgments

The authors are immensely grateful to Dominica Melgar Carrascal for her valuable ideas and insight into hematological malignancy app potential and limitations from the perspective of patients with hematological malignancies.

Authors' Contributions

NBG and HRR conceived the study. NBG, HRR, and MFG collected the data and wrote the manuscript. NBG, HRR, MFG, and LAKM contributed to the interpretation of the results and reviewed the paper. All authors have reviewed and approved the last version of the manuscript.

Conflicts of Interest

None declared.

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021 Feb 04:209-249 [FREE Full text] [CrossRef] [Medline]
  2. Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Akinyemiju TF, Al Lami FH, Alam T, Alizadeh-Navaei R, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2016: a systematic analysis for the global burden of disease study. JAMA Oncol 2018 Nov 01;4(11):1553-1568 [FREE Full text] [CrossRef] [Medline]
  3. Cowan AJ, Allen C, Barac A, Basaleem H, Bensenor I, Curado MP, et al. Global burden of multiple myeloma: a systematic analysis for the Global Burden of Disease Study 2016. JAMA Oncol 2018 Sep 01;4(9):1221-1227 [FREE Full text] [CrossRef] [Medline]
  4. GBD 2017 Mortality Collaborators. Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018 Nov 10;392(10159):1684-1735 [FREE Full text] [CrossRef] [Medline]
  5. Umıt EG, Baysal M, Bas V, Asker I, Kırkızlar O, Demır AM. Polypharmacy and potentially inappropriate medication use in older patients with multiple myeloma, related to fall risk and autonomous neuropathy. J Oncol Pharm Pract 2020 Jan;26(1):43-50. [CrossRef] [Medline]
  6. Leger DY, Moreau S, Signol N, Fargeas J, Picat M, Penot A, et al. Polypharmacy, potentially inappropriate medications and drug-drug interactions in geriatric patients with hematologic malignancy: observational single-center study of 122 patients. J Geriatr Oncol 2018 Jan;9(1):60-67. [CrossRef] [Medline]
  7. Dhakal P, Lyden E, Muir KE, Al-Kadhimi ZS, Koll T, Maness LJ, et al. Prevalence and effects of polypharmacy on overall survival in acute myeloid leukemia. Leuk Lymphoma 2020 Jul;61(7):1702-1708. [CrossRef] [Medline]
  8. Mian H, Fiala M, Wildes TM. Adherence to lenalidomide in older adults with newly diagnosed multiple myeloma. Clin Lymphoma Myeloma Leuk 2020 Feb;20(2):98-104 [FREE Full text] [CrossRef] [Medline]
  9. Kristjánsdóttir ER, Toksvang LN, Schmiegelow K, Rank CU. Prevalence of non-adherence and non-compliance during maintenance therapy in adults with acute lymphoblastic leukemia and their associations with survival. Eur J Haematol 2022 Feb;108(2):109-117. [CrossRef] [Medline]
  10. Mancini J, Simeoni M, Parola N, Clement A, Vey N, Sirvent N, et al. Adherence to leukemia maintenance therapy: a comparative study among children, adolescents, and adults. Pediatr Hematol Oncol 2012 Aug;29(5):428-439. [CrossRef] [Medline]
  11. Hall A, Lynagh M, Bryant J, Sanson-Fisher R. Supportive care needs of hematological cancer survivors: a critical review of the literature. Crit Rev Oncol Hematol 2013 Oct;88(1):102-116. [CrossRef] [Medline]
  12. Allart-Vorelli P, Porro B, Baguet F, Michel A, Cousson-Gélie F. Haematological cancer and quality of life: a systematic literature review. Blood Cancer J 2015 Apr 24;5:e305 [FREE Full text] [CrossRef] [Medline]
  13. Allart P, Soubeyran P, Cousson-Gélie F. Are psychosocial factors associated with quality of life in patients with haematological cancer? A critical review of the literature. Psychooncology 2013 Feb;22(2):241-249. [CrossRef] [Medline]
  14. Phillips JL, Currow DC. Cancer as a chronic disease. Collegian 2010;17(2):47-50. [CrossRef] [Medline]
  15. Global Burden of Disease 2019 Cancer Collaboration. Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. JAMA Oncol 2022;8(3):420-444 [FREE Full text] [CrossRef] [Medline]
  16. Smartphone market in Europe: statistics and facts. Statista.   URL: https://www.statista.com/topics/3341/smartphone-market-in-europe/ [accessed 2022-06-07]
  17. Jagannath S, Mikhael J, Nadeem O, Raje N. Digital health for patients with multiple myeloma: an unmet need. JCO Clin Cancer Inform 2021 Oct;5:1096-1105 [FREE Full text] [CrossRef] [Medline]
  18. Binder AF, Handley NR, Wilde L, Palmisiano N, Lopez AM. Treating hematologic malignancies during a pandemic: utilizing telehealth and digital technology to optimize care. Front Oncol 2020 Jun 26;10:1183 [FREE Full text] [CrossRef] [Medline]
  19. Psihogios AM, Li Y, Ahmed A, Huang J, Kersun LS, Schwartz LA, et al. Daily text message assessments of 6-mercaptopurine adherence and its proximal contexts in adolescents and young adults with leukemia: a pilot study. Pediatr Blood Cancer 2021 Feb;68(2):e28767. [CrossRef] [Medline]
  20. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015;3(1):e27 [FREE Full text] [CrossRef] [Medline]
  21. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015 Jan;4:1 [FREE Full text] [CrossRef] [Medline]
  22. Dawson RM, Felder TM, Donevant SB, McDonnell KK, Card EB, King CC, et al. What makes a good health 'app'? Identifying the strengths and limitations of existing mobile application evaluation tools. Nurs Inq 2020 Apr;27(2):e12333. [CrossRef] [Medline]
  23. Terhorst Y, Philippi P, Sander LB, Schultchen D, Paganini S, Bardus M, et al. Validation of the Mobile Application Rating Scale (MARS). PLoS One 2020;15(11):e0241480 [FREE Full text] [CrossRef] [Medline]
  24. Kwan YH, Ong WJ, Xiong M, Leung YY, Phang JK, Wang CTM, et al. Evaluation of mobile apps targeted at patients with spondyloarthritis for disease monitoring: systematic app search. JMIR Mhealth Uhealth 2019 Oct 28;7(10):e14753 [FREE Full text] [CrossRef] [Medline]
  25. Amor-García, Collado-Borrell R, Escudero-Vilaplana V, Melgarejo-Ortuño A, Herranz-Alonso A, Arranz Arija, et al. Assessing apps for patients with genitourinary tumors using the Mobile Application Rating Scale (MARS): systematic search in app stores and content analysis. JMIR Mhealth Uhealth 2020 Jul 23;8(7):e17609 [FREE Full text] [CrossRef] [Medline]
  26. Kim BY, Sharafoddini A, Tran N, Wen EY, Lee J. Consumer mobile apps for potential drug-drug interaction check: systematic review and content analysis using the Mobile App Rating Scale (MARS). JMIR Mhealth Uhealth 2018 Mar 28;6(3):e74 [FREE Full text] [CrossRef] [Medline]
  27. Devan H, Farmery D, Peebles L, Grainger R. Evaluation of self-management support functions in apps for people with persistent pain: systematic review. JMIR Mhealth Uhealth 2019 Feb 12;7(2):e13080 [FREE Full text] [CrossRef] [Medline]
  28. Gong E, Zhang Z, Jin X, Liu Y, Zhong L, Wu Y, et al. Quality, functionality, and features of chinese mobile apps for diabetes self-management: systematic search and evaluation of mobile apps. JMIR Mhealth Uhealth 2020 Apr 07;8(4):e14836 [FREE Full text] [CrossRef] [Medline]
  29. Escoffery C, McGee R, Bidwell J, Sims C, Thropp EK, Frazier C, et al. A review of mobile apps for epilepsy self-management. Epilepsy Behav 2018 Apr;81:62-69. [CrossRef] [Medline]
  30. Masterson CRM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and analysis of existing mobile phone apps to support heart failure symptom monitoring and self-care management using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth 2016 Jun 14;4(2):e74. [CrossRef] [Medline]
  31. Collado-Borrell R, Escudero-Vilaplana V, Narrillos-Moraza Á. Patient-reported outcomes y aplicaciones móviles. Revisión de su impacto en los resultados en salud de los pacientes. Farmacia Hospitalaria 2022:9. [CrossRef]
  32. Tsatsou I, Konstantinidis T, Kalemikerakis I, Adamakidou T, Vlachou E, Govina O. Unmet supportive care needs of patients with hematological malignancies: a systematic review. Asia Pac J Oncol Nurs 2021;8(1):5-17 [FREE Full text] [CrossRef] [Medline]
  33. Suleman A, Vijenthira A, Berlin A, Prica A, Rodin D. The use of virtual care in patients with hematologic malignancies: a scoping review. Curr Oncol 2022 Feb 07;29(2):892-900 [FREE Full text] [CrossRef] [Medline]
  34. Taylor S, Bellhouse S, Allsop M, Radford J, Yorke J. The role of eHealth in the delivery of care for patients with hematological cancers: a systematic literature review. Telemed J E Health 2020 Sep;26(9):1093-1105. [CrossRef] [Medline]
  35. Collado-Borrell R, Escudero-Vilaplana V, Villanueva-Bueno C, Herranz-Alonso A, Sanjurjo-Saez M. Features and functionalities of smartphone apps related to COVID-19: systematic search in app stores and content analysis. J Med Internet Res 2020 Aug 25;22(8):e20334 [FREE Full text] [CrossRef] [Medline]
  36. Adam R, McMichael D, Powell D, Murchie P. Publicly available apps for cancer survivors: a scoping review. BMJ Open 2019 Sep 30;9(9):e032510 [FREE Full text] [CrossRef] [Medline]
  37. Mobasheri MH, Johnston M, King D, Leff D, Thiruchelvam P, Darzi A. Smartphone breast applications—what's the evidence? Breast 2014 Oct;23(5):683-689. [CrossRef] [Medline]
  38. Hummelinck A, Pollock K. Parents' information needs about the treatment of their chronically ill child: a qualitative study. Patient Educ Couns 2006 Aug;62(2):228-234. [CrossRef] [Medline]
  39. Modi AC, Pai AL, Hommel KA, Hood KK, Cortina S, Hilliard ME, et al. Pediatric self-management: a framework for research, practice, and policy. Pediatrics 2012 Feb;129(2):e473-e485 [FREE Full text] [CrossRef] [Medline]
  40. Owens OL, Beer JM, Reyes LI, Thomas TL. Systematic review of commercially available mobile phone applications for prostate cancer education. Am J Mens Health 2019;13(1):1557988318816912 [FREE Full text] [CrossRef] [Medline]
  41. Stolwijk C, van Onna M, Boonen A, van Tubergen A. Global prevalence of spondyloarthritis: a systematic review and meta-regression analysis. Arthritis Care Res (Hoboken) 2016 Sep;68(9):1320-1331 [FREE Full text] [CrossRef] [Medline]
  42. Cannon C. Telehealth, mobile applications, and wearable devices are expanding cancer care beyond walls. Semin Oncol Nurs 2018 May;34(2):118-125. [CrossRef] [Medline]
  43. Smith A, Howell D, Patmore R, Jack A, Roman E. Incidence of haematological malignancy by sub-type: a report from the Haematological Malignancy Research Network. Br J Cancer 2011 Nov 22;105(11):1684-1692 [FREE Full text] [CrossRef] [Medline]
  44. Tietsche de Moraes Hungria V, Chiattone C, Pavlovsky M, Abenoza LM, Agreda GP, Armenta J, et al. Epidemiology of hematologic malignancies in real-world settings: findings from the hemato-oncology latin america observational registry study. J Glob Oncol 2019 Nov;5:1-19 [FREE Full text] [CrossRef] [Medline]
  45. Heponiemi T, Kaihlanen A, Kouvonen A, Leemann L, Taipale S, Gluschkoff K. The role of age and digital competence on the use of online health and social care services: a cross-sectional population-based survey. Digit Health 2022;8:20552076221074485 [FREE Full text] [CrossRef] [Medline]
  46. Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F. Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 2021 Aug 17;21(1):1556 [FREE Full text] [CrossRef] [Medline]
  47. Nieman CL, Oh ES. Connecting with older adults via telemedicine. Ann Intern Med 2020 Nov 17;173(10):831-832. [CrossRef] [Medline]
  48. Lam K, Lu AD, Shi Y, Covinsky KE. Assessing telemedicine unreadiness among older adults in the United States during the COVID-19 pandemic. JAMA Intern Med 2020 Oct 01;180(10):1389-1391. [CrossRef] [Medline]
  49. Medicine evaluation figures. European Medicines Agency.   URL: https://www.ema.europa.eu/en/about-us/what-we-do/authorisation-medicines/medicine-evaluation-figures [accessed 2022-06-09]
  50. Finnes HD. 2019-2020 drug updates in hematologic malignancies. J Adv Pract Oncol 2021 Apr;12(3):279-283 [FREE Full text] [CrossRef] [Medline]
  51. Wu Y, Linder L, Kanokvimankul P, Fowler B, Parsons B, Macpherson C, et al. Use of a smartphone application for prompting oral medication adherence among adolescents and young adults with cancer. Oncol Nurs Forum 2018 Jan 01;45(1):69-76 [FREE Full text] [CrossRef] [Medline]
  52. Heneghan MB, Hussain T, Barrera L, Cai SW, Haugen M, Morgan E, et al. Access to technology and preferences for an mhealth intervention to promote medication adherence in pediatric acute lymphoblastic leukemia: approach leveraging behavior change techniques. J Med Internet Res 2021 Feb 18;23(2):e24893 [FREE Full text] [CrossRef] [Medline]
  53. Pereira-Salgado A, Westwood JA, Russell L, Ugalde A, Ortlepp B, Seymour JF, 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 Dec 06;5(12):e184 [FREE Full text] [CrossRef] [Medline]
  54. Raghunathan NJ, Korenstein D, Li QS, Tonorezos ES, Mao JJ. Determinants of mobile technology use and smartphone application interest in cancer patients. Cancer Med 2018 Nov;7(11):5812-5819 [FREE Full text] [CrossRef] [Medline]
  55. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med 2016 Dec 13;7(2):254-267. [CrossRef] [Medline]
  56. Tarricone R, Cucciniello M, Armeni P, Petracca F, Desouza KC, Hall LK, et al. Mobile health divide between clinicians and patients in cancer care: results from a cross-sectional international survey. JMIR Mhealth Uhealth 2019 Sep 06;7(9):e13584 [FREE Full text] [CrossRef] [Medline]
  57. Nair SC, Hassen WA, Sreedharan J, Qawasmeh K, Ibrahim H. Comparison of perceptions of unmet supportive care needs between cancer patients and their oncologists. Support Care Cancer 2019 Nov;27(11):4229-4235. [CrossRef] [Medline]
  58. Subhi Y, Bube SH, Rolskov BS, Skou TAS, Konge L. Expert involvement and adherence to medical evidence in medical mobile phone apps: a systematic review. JMIR Mhealth Uhealth 2015 Jul 27;3(3):e79 [FREE Full text] [CrossRef] [Medline]
  59. Haffey F, Brady RRW, Maxwell S. A comparison of the reliability of smartphone apps for opioid conversion. Drug Saf 2013 Feb;36(2):111-117. [CrossRef] [Medline]
  60. Collado-Borrell R, Escudero-Vilaplana V, Calles A, Garcia-Martin E, Marzal-Alfaro B, Gonzalez-Haba E, et al. Oncology patient interest in the use of new technologies to manage their disease: cross-sectional survey. J Med Internet Res 2018 Oct 23;20(10):e11006 [FREE Full text] [CrossRef] [Medline]
  61. Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med 2013 Jan;10(1):e1001363 [FREE Full text] [CrossRef] [Medline]
  62. Connor K, Brady RRW, de Beaux A, Tulloh B. Contemporary hernia smartphone applications (apps). Hernia 2014 Aug;18(4):557-561. [CrossRef] [Medline]
  63. Mair FS, Montori VM, May CR. Digital transformation could increase the burden of treatment on patients. BMJ 2021 Nov 25;375:n2909. [CrossRef] [Medline]
  64. Mair FS, May CR. Thinking about the burden of treatment. BMJ 2014 Nov 10;349:g6680. [CrossRef] [Medline]
  65. Lewis TL, Wyatt JC. mHealth and mobile medical Apps: a framework to assess risk and promote safer use. J Med Internet Res 2014 Sep 15;16(9):e210 [FREE Full text] [CrossRef] [Medline]
  66. Biggest app stores in the world. Statista.   URL: https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/ [accessed 2022-06-11]


HM: hematological malignancy
ICC: intraclass correlation coefficient
MARS: Mobile Application Rating Scale
mHealth: mobile health
PRISMA: Preferred Reporting Items for a Systematic Review and Meta-analysis


Edited by L Buis; submitted 20.12.21; peer-reviewed by R Halkes, L Guo; comments to author 06.05.22; revised version received 21.06.22; accepted 02.09.22; published 20.09.22

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©Nerea Báez Gutiérrez, Héctor Rodríguez Ramallo, Marcos Fernández González, Laila Abdel-Kader Martín. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 20.09.2022.

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