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. Author manuscript; available in PMC: 2026 Mar 26.
Published in final edited form as: J Cancer Surviv. 2023 Jan 11;17(3):557–568. doi: 10.1007/s11764-022-01328-3

Mobile health (mHealth) interventions in prostate cancer survivorship: a scoping review

Motolani E Ogunsanya 1,2, Munjireen Sifat 2, Olufikayo O Bamidele 3, Elochukwu F Ezenwankwo 4, Shari Clifton 5, Chelsea Ton 1, Jennifer M Knight 6, Folakemi T Odedina 7, Joseph A Greer 8, Kathleen Dwyer 9, Darla E Kendzor 2,10
PMCID: PMC13014381  NIHMSID: NIHMS2104797  PMID: 36627464

Abstract

Purpose

This scoping review explores the application of mHealth technology in prostate cancer (CaP) management along the survivorship continuum.

Methods

The scoping review was conducted using the five-step framework developed by Arksey and O’Malley. Using predefined criteria, we screened citations from Embase, EBSCOHost, Cochrane Library, PubMed, ProQuest, SCOPUS, and Web of Science for primary studies published before December 2021. We selected studies that explored the application of mHealth technology in CaP management and survivorship. Evidence from 14 eligible studies was summarized using narrative synthesis.

Results

Fourteen studies published between 2015 and 2021 were included. Ten mHealth apps were identified with only one still in use. Most apps were explored for their supportive care roles during radiotherapy (n = 9) and androgen deprivation therapy (ADT) (n = 1) treatment, mainly to assess outcomes (n = 1) and manage patient-reported symptoms (n = 5). One study deployed mHealth to facilitate recovery after surgery. Very few studies (n = 3) applied mHealth for lifestyle management (i.e., physical activity). Barriers to app usage included connectivity issues, end-user familiarity with the app, login hurdles, and time constraints. Facilitators of app usage included apps being downloaded for participants, devices provided for participants, and the ability to connect with providers through the platform.

Conclusions and implications for cancer survivors

The improving survival rates from CaP suggest that men are now living longer with unfavorable treatment side effects such as reduced sexual functioning, pain, and fatigue. Hence, mHealth represents new hope in men’s illness trajectory. However, current application in patients’ care pathways remains poor, particularly in the active phase of CaP management. Efforts must be accelerated to explore individual and healthcare-level drivers of mHealth use. The feasibility and descriptive nature of current studies point to a lack of attention to actual implementation and scale-up issues in research considering mHealth application in CaP, hence accounting partly for the gap in research/practice.

Introduction

Over the last few decades, electronic health (eHealth) intervention approaches have diversified enormously to introduce mobile health (mHealth) applications. The Global Observatory for eHealth of the World Health Organization defines mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” [1]. Nearly one-third of a million applications have been developed to assist with uninterrupted care delivery for chronic health conditions, with hundreds of applications dedicated to cancer treatment and management [2, 3].

mHealth applications constitute 1% of the apps hosted on the digital market, with a total of 3.3 million and 2.11 million apps available on the Android and iOS platforms, respectively [4]. A myriad of mHealth applications track fitness data, and the initial evidence of the utility of apps for chronic disease management is emerging [5]. The global mHealth application market is projected to incur a compound annual growth rate (CAGR) of 11.8% through 2022–2030. The proliferation of medical apps is remarkably surpassing that of other health applications [6].

A study in 2015 reported 150 available applications that guide the treatment of genitourinary tract diseases, with a few dedicated to prostate disease management [7]. However, it is unknown how many mHealth applications are available to survivors of and people living with and beyond prostate cancer (CaP). The American Cancer Society estimated that there would be 268,490 new cases and 34,500 deaths from CaP in the USA in 2022 [8]. CaP is the 2nd most common cancer diagnosed in men in the USA, constituting 15.1% of all cancers [9]. Both mortality and incidence rates of CaP remain highest among Black men globally due to gaps in access to care, cultural differences in dietary factors and social habits, genetic characteristics, and environmental factors [10].

CaP is associated with many distressing and life-limiting lower urinary tract symptoms, such as nocturia, poor urinary stream, erectile dysfunction, and visible hematuria [11]. The complexity and often multimodal nature of CaP treatment and the associated multisystemic impacts imply that many men with a positive diagnosis will not receive any treatment for a considerable period, while a significant proportion will eventually receive treatment as outpatients [12]. For men who receive treatment, many will experience several life-limiting complications, including chronic inflammation, fatigue, weakness, altered body weight, psychosocial issues (i.e., anxiety, depression, fear), neurological issues (i.e., impaired cognition, poor coordination, and balance), loss of bladder and bowel control, erectile and sexual dysfunctions, frailty, osteoporosis, and increased risk of falling and fracture. Many of these complications persist for years, even after treatment is completed. As a result, many patients suffer socioeconomic losses, resulting in significant life changes. Whether an individual receives treatment for CaP upon diagnosis or is actively monitored over time, mHealth applications offer great opportunities for men to receive personalized, timely, high-quality, and evidence-based care [13].

A recent systematic review of mHealth in men with CaP showed that there is a wide variety of mobile phone applications related to CaP currently available for clinicians and patients, the majority addressing issues such as lifestyle changes, health literacy, screening, diagnostics, symptom profiling, outcome estimates, education, and research [13]. However, no study has been done to identify apps specifically used in CaP survivorship. While the availability and broad usage of mHealth in this population group are laudable, efforts are needed to understand how these tools have been applied along the continuum of CaP survivorship care. Therefore, using a scoping review approach (to map the extant literature), the objectives of this study were to (1) characterize the current field of CaP survivorship apps, (2) critically appraise the content and functionality of apps used in CaP survivorship, and (3) identify gaps in the field to guide future development of an app to support CaP survivors.

Methods

Stages of scoping review

A scoping review was conducted to identify gaps and explore the extent of existing literature on the mHealth applications used across the continuum of CaP survivorship. This methodology was guided by the five-step framework of Arksey and O’Malley [14]: (1) identifying the research question, (2) identifying the relevant studies (defining the inclusion and exclusion criteria), (3) searching and selecting the evidence, (4) charting the evidence, and 5) collating, summarizing, and reporting the evidence.

Stage one: identifying the research question

The review questions of interest included the following: (1) What mHealth apps are available for use in CaP survivors? (2) How were these mHealth apps designed and developed?, and (3) How are these apps currently used for the care of patients with CaP?

Stage two: identifying the relevant studies

Using a search strategy and developed search terms (see the Appendix for sample), searches were conducted on APA PsycArticles (ProQuest), APA PsycINFO (Ovid), Black Studies Center (ProQuest), CINAHL Complete (EBSCO), ClinicalTrials.gov, Cochrane Central Register of Controlled Trials (Ovid), Cochrane Database of Systematic Reviews (Ovid), Dissertations & Theses (ProQuest), Embase (Ovid), Google Scholar; MEDLINE including Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily (Ovid), PubMed Central, Scopus, and Web of Science Core Collection. Supplementary searches were conducted by examining reference lists of included citations to potentially identify additional eligible studies. Primary studies were included if they involved men who had undergone active treatment for CaP or intervention studies that reported specific data on mHealth apps used across the continuum of CaP survivorship. Studies were excluded if they were not conducted in CaP or literature that was not primary research (e.g., editorials, letters). Studies with multiple cancer types, including CaP, or did not report on findings specific to CaP were also excluded. Search strategies were customized for each database to retrieve literature relevant to the concepts of CaP and mHealth. Searches were executed from journal inception to September 2021. All included studies were screened, extracted, and analyzed independently by at least two reviewers using Microsoft Excel and Covidence.

To achieve broad coverage of the extant literature, as recommended by Arksey and O’Malley [14], appropriate keywords were used as search terms to capture literature relevant to mHealth and CaP. The following keywords and search terms were used to ensure consistency during the database search: smartphone*, smartwatch*, m health*, prostat* interventions. The “PICo” (population, interest, context) model informed these search terms and keywords.

Stage three: searching and selecting the evidence

A total of 1560 articles were identified from the initial searches. After duplicate records were removed, 766 articles were retained, which were screened against the preset inclusion and exclusion criteria by three independent reviewers (MO, EE, and OB), yielding 151 articles. The full texts of these 151 articles were further scrutinized against the review inclusion/exclusion criteria, after which 14 articles were finally included in the review. Articles excluded were non-mHealth related (n = 43), wrong outcomes (n = 31) — for example, the articles focused on retention of participants in a program or screening rather than directly related to the care of patients such as managing patient symptoms, wrong study design (n = 22), conference abstracts/proceedings (n = 13), lacked focus or subanalysis on CaP (n = 11), systematic reviews (n = 9), clinical trials without published studies, i.e., registries (n = 6), and dissertations (n = 2). Figure 1 shows the flowchart of this process.

Fig. 1.

Fig. 1

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Process of identification and inclusion of studies — PRISMA Diagram flow

Stage four: charting the evidence

The fourth stage of Arksey and O’Malley’s [14] methodological framework involves charting the selected articles. Subsequently, via Covidence, each article was summarized by at least four reviewers (MO, CT, OB, MS) using the following information: study characteristics (author names, publication year, aims/objectives of the study, study type, sample demographics, target group, method of recruitment), app-specific domains (app name, platform available, country/language, app framework, app features, components of use, usage status, app maintenance, funding for app, app version, app modality, app administration), and intervention-specific domains (intervention type, intervention, and comparison, refusal rate, follow-up, and attrition, reported barriers and facilitators to implementation and use of the intervention, main study findings), and mHealth summaries (term used to define mHealth, operational definition of mHealth, strengths/added values, limitations, recommendations for mHealth delivery).

Stage five: collating, summarizing, and reporting the evidence

In the final stage of this framework, the articles were collated, summarized, and study findings were reported. See Table 1 for a summary of the included studies.

Table 1.

Summary of the main characteristics of the studies and applications

Reference Study type (aim) Sample demographics Method of recruitment Application name Country/language
Sundberg et al., 2015 [15] Descriptive—feasibility, and acceptability study (to test the feasibility and acceptability of the app for assessing and managing patient-reported symptoms during radiotherapy) Prostate cancer (n = 10) patients receiving radiotherapy Clinic patients Unknown Sweden/Swedish
Halleberg-Nyman et al., 2017 [16] Descriptive, qualitative (to explore how patients with prostate cancer perceived their participation with or without the support of the smartphone app during radiotherapy) 28 prostate cancer patients receiving adjuvant radiotherapy. Age ranged from 57 to 77 years Clinic patients Interaktor Sweden/English
Langius-Eklof et al., 2017 [17] Mixed methods (to investigate user behavior, adherence to reporting, and the patients’ experiences of using Interaktor during radiotherapy) 66 patients undergoing radiotherapy for prostate cancer. Mean age of 69 years, married (n = 57, 86%), postgraduate or university degree holders (n = 32, 50%) Clinic patients Interaktor Sweden/English
Sundberg et al., 2017 [18] Nonrandomized experimental study (to evaluate the effect of the app on symptom burden and QoL for real-time symptom assessment and management during radiotherapy) 130 patients with prostate cancer (66 in the treatment group and 64 in the control group); mostly married; and educated to senior high school and above; mostly retired Clinic patients Interaktor Sweden/English
Zhang et al., 2018 [19] Descriptive (to empower patients and strengthen self-management in cancer diseases) 138 cancer patients (n = 60 prostate cancer patients) Clinic patients My Health Avatar/iManage Cancer App U.K. and Italy/English
Lee et al., 2019 [20] Randomized controlled trial (to compare the effectiveness of smartphone-based and conventional pedometer-based exercise monitoring systems in promoting home exercise) 100 prostate cancer patients (50 in the treatment group and 50 in the control group) Clinic patients Smart After-Care South Korea/Korean
Roberts et al., 2019 [21] Descriptive, qualitative (to seek post-treatment nonmetastatic breast, prostate, and colorectal cancer survivors’ opinions of using smartphone apps to promote physical activity) 32 individuals were diagnosed with breast (8/32), prostate (8/32), or colorectal cancer (16/32). Mean age was 60 years (range 37–78 years; SD 11 years) Other: Recruited via advertisements within community-based cancer support groups Unknown United Kingdom/English
Belarmino et al., 2019 [22] Descriptive (to examine the feasibility and utilization of a mobile app to characterize and facilitate post-RARP (robot-assisted radical prostatectomy)) 19 patients undergoing robot-assisted radical prostatectomy Clinic patients iPhone mobile app USA/English
Faria et al., 2020 [23] Randomized controlled trial (to test the effects of the app on response rates pre-and post-surgical CaP) 100 patients diagnosed with prostate cancer (50 in the treatment group and 50 in the control group) Clinic patients UroHealth Brazil/Portuguese
Crafoord et al., 2020 [24] Randomized controlled trial (to describe engagement with the Interaktor app among patients with breast or prostate cancer during treatment) Patients with breast cancer receiving neoadjuvant chemotherapy (n = 74) and patients with locally advanced prostate cancer receiving treatment with radiotherapy (n = 75) Clinic patients Interaktor Sweden/English
Trojan et al., 2020 [25] Descriptive (to provide insight and usability, and acceptance of a smartphone app for monitoring of electronically captured patient-reported outcomes in patients undergoing immunotherapy) 6 patients (n = 1, prostate cancer; n = 4 lung; and n = 1, urothelial). Patients were aged 47 −72 years (median 62 years); all patients were male and suffered from metastasis either in lymph nodes, bones, or lungs Clinic patients Consilium Switzerland/English
Nabi et al., 2020 [26] Descriptive, qualitative (to evaluate the usability and patient experience of the app on androgen deprivation therapy and physicians’ beliefs about the potential benefits of using the app) Five men diagnosed with prostate cancer initiating ADT. Participants were aged 45–75 years and an average age of 62 years) Clinic patients myPROHealth USA/English
Sundberg et al., 2021 [27] Nonrandomized experimental study (to determine levels of health literacy and self-care ability in men with prostate cancer during radiotherapy) 130 patients undergoing radiotherapy for prostate cancer (66 in the treatment group and 64 in the control group) with an average age of 69 years Clinic patients Interaktor Sweden/English
Blair et al., 2021 [28] Randomized controlled trial (to evaluate the feasibility, acceptability, and preliminary efficacy of the mHealth intervention to interrupt and replace sedentary time with light-intensity physical activity (standing and stepping) Participants included survivors of breast cancer (21/54, 39%), prostate cancer (16/54, 30%; 10 in the intervention group and 6 in the control group), and a variety of other cancer types. Participants, on average, were 70 years old SD 4.8), 55% (30/54) female, 24% (13/54) Hispanic, and 81% (44/54) overweight or obese Other: Multiple methods: cancer registry, mails, as well as flyers at selected locations (senior centers and libraries) Jawbone (UP2) USA/English
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Results

Study selection

A total of 14 studies that met the inclusion criteria were included in the review.

Study characteristics

The publication years ranged from 2015 to 2021. Included studies spanned several different countries: six were from Sweden [15–18, 24, 27], three were from the USA [22, 26, 28], two were from the UK [19, 29], and one each from Brazil [23], South Korea [20], and Switzerland [25]. In addition, seven descriptive studies [15, 16, 19, 21, 22, 25, 26], one mixed method study [17], four randomized controlled trials (RCTs) [20, 23, 24, 28], and two nonrandomized controlled studies [18, 27] were identified (see Table 1).

Participant characteristics

A total of 772 patients with CaP participated in the studies, 258 of whom were included in the control groups. Some studies (Sundberg [18, 27]) also used the same set of participants in reporting the use of the Interaktor. CaP patients undergoing treatment only were the population of interest (n = 10; 71%) [15–20, 22, 23, 26, 27], though some studies included other patients with cancer [21, 24, 25, 28]. Patients mostly underwent radiotherapy (n = 7; 50%) [15–18, 20, 24, 27] and were recruited directly via clinics (n = 12; 86%) [15–20, 22–27]. Finally, most study participants were White males (75–80% of the total patient population) (see Table 1).

App characteristics

From the 14 studies included, seven different apps were identified [16–20, 22–27], while three were either unnamed or not specified [15, 21, 28] (see Table 2). Apps were primarily available on both the Android and iOS platforms (n = 6; 43%) [18, 24–26, 28, 30]. Interaktor, an app developed in Sweden, was the most commonly featured app (n = 5; 36%) [16–18, 24, 27]. In total, ten mobile applications were identified in the reviewed articles, and all but one [22] were self-administered. Only one app was currently maintained and still in use [26], with the last app maintenance/update year in 2018. Study apps were made available to the participants directly, and in five studies [15, 17, 18, 24, 27], smartphone and tablet devices were provided by the investigators. While most apps were only made available on smartphone devices, three studies extended device use to include tablets and desktops [19, 23, 24]. In addition, some apps included relevant website links to aid physicians’ monitoring and analysis of patient data [19, 23] or provide participants with continuous access to relevant evidence-based information on self-care and symptom management [15, 17]. The majority of the apps included patient-reported outcome measures (n = 10; 71%) [15, 17–19, 22–27], which were designed for symptom management (n = 9; 64%) [15–19, 22, 24, 25, 27] and quality of life (QoL) assessments [18, 22].

Table 2.

Summary of app characteristics

App name Maintenance/version (Still in use?) App use along CaP care path App description Theoretical framework underpinning app development App modality App administration
iPhone mobile app [22] No data available (Unsure) Recovery: self-management of symptoms and side-effects App includes (1) standardized health-related QoL measures, (2) push notifications to complete surveys, ambulate, hydrate, and perform Kegel exercises, (3) step-count tracking to confirm ambulation via the Health Kit app, and (4) a library with various pre-and post-operative instructions and Kegel instructions with diagrams Patient-reported outcomes iPhone (iOS) Hybrid with research team involvement
Unnamed [28] Oct 2, 2017 (No) Nonspecific: physical activity monitoring Awareness and self-monitoring of both physical activity and inactivity Theoretical framework (guided by social cognitive theory) Smartphone (Android and iOS) Self-administered
Interaktor [24] No data available (Unsure) Active treatment: self-management of symptoms and side-effects App includes (1) daily reports of symptoms, (2) a risk assessment model, (3) evidence-based self-care advice, and (4) provision of immediate access to clinicians Patient-reported outcomes Smartphone and tablet (patients were lent devices) (Android and iOS) Self-administered
UroHealth [23] No data available (Unsure) Treatment recovery: symptom monitoring Post-operative potency and urinary continence after radical prostatectomy. Reassessments post-discharge at 1, 3, 6, and 12 months Patient-reported outcomes iPhone, iPad tablet, and website (iOS and website) Self-administered
Interaktor [16–18, 27] No data available (Unsure) Active treatment: self-management of symptoms and side-effects App includes (1) daily reports of symptoms, (2) a risk assessment model, (3) evidence-based self-care advice, and (4) provision of immediate access to clinicians Theoretical framework (naturalistic inquiry) [16] and patient-reported outcomes [17, 18, 27] Smartphone and tablet (not specified) Self-administered
Smart After-Care [20] No data available (Unsure) Post-treatment: physical activity monitoring plus nutritional and exercise education Smart after-care promotes physical activity, as assessed by rates of uptake, adherence, and completion Physical activity Smartphone (Android) Self-administered
myPROHealth [26] Dec 3, 2018/ version 2.0.0 (> 10 k downloads) (Yes) Nonspecific: physical activity monitoring App includes (1) daily reports of symptoms, (2) a risk assessment model, (3) evidence-based self-care advice, and (4) provision of immediate access to clinicians Patient-reported outcomes Smartphone (Android and iOS) Self-administered
Unnamed [21] No data available (Unsure) Nonspecific: physical activity monitoring Video clip of prescribed resistance and stretching exercise, the goal of exercise per week, nutritional education Theoretical framework (behavior change techniques (BCTs)) Smartphone (Android and iOS) Self-administered
Unnamed [15] No data available (Unsure) Active treatment: self-management of symptoms and side-effects Symptom management and self-care Patient-reported outcomes Smartphone and tablet (not specified) Self-administered
Consilium [25] No data available (Unsure) Active treatment: self-management of symptoms and side-effects Symptoms and therapy side effects Patient-reported outcomes Smartphone (Android and iOS) Self-administered
My Health Avatar / iManage Cancer App [19] No data available (Unsure) Active treatment: self-management of symptoms and side-effects Cancer-specific health information, patient reporting, and medication reminders Patient-reported outcomes Desktop, tablet and smartphone (Android) Self-administered
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Symptom management and monitoring varied across the apps. For example, within the Interaktor app, different levels of alerts (low vs. high) were created so that alerts triggered by symptom reporting, regardless of their level, generated text messages to the clinical monitoring nurse. Low-level alerts were followed up on the same day by the nurse, while high-level alerts required contact with the patient within an hour [24]. Within the Consilium app, a similar setup with alerts was provided to app users; however, the timeframe for a response from the clinical team and how the alerts were generated was not specified [25]. Symptoms tracked by participants included fatigue, pain, urinary symptoms, bowel symptoms, worry, depression, sleep, and flushing [16–19, 22–25, 27]. The frequency of reported symptoms varied, with some apps designed for daily captures [16–19, 24, 27] while a few promoted weekly entries [22, 25], with one study reporting how patients who reported their symptoms daily did not consider this burdensome [17, 24]. Finally, different rating scales were used, including numeric and sliding scales.

Physical activity levels were also outcomes of interest [20, 21, 26, 28], with two apps integrating activity data with wearable activity trackers [20, 28]. Unlike apps focused on symptom monitoring, apps geared towards physical activity encouraged daily reporting. Physical activity was assessed through daily total sedentary time and the number of breaks in between [28], engagement in home aerobic exercises [20], and other physical activities [21, 26]. Of the apps focusing on physical activity, only one assessed dietary intake [26]. While this app did not make dietary recommendations, participants were encouraged to record and take photos of their meals [26]. Other patient-reported outcome measures included the Common Terminology Criteria for Adverse Events (CTCAE) to monitor toxicity [25], quality of life [18, 19, 22, 28], sleep [15, 17, 19], adherence to therapy [17, 24], or other relevant measures such as orientation to life, self-care ability, and health literacy [15, 18, 27]. Quality of life (QoL) measures used included the Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) [22], Short Form 36 (SF-36) [28], and the EORTC QLQ-C30 [18].

Most apps were used either post-operatively with participants who had undergone radical prostatectomy [22] or during active treatment for CaP [15–18, 20, 23–27]. Finally, four apps were funded via federal grants [19, 20, 26, 27], while two were funded through private foundations [17, 24].

Finally, most of the apps reviewed in this study are no longer publicly available for use in the routine care and management of CaP survivors.

Intervention characteristics

In studies including randomized clinical trials (RCTs), participants were randomized to either receive the intervention (usually the app) or standard care/control. In most cases, the allocations were done 1:1, except for the study by Blair et al. [28] in which participants were randomized in a 1:1:1 allocation to the tech support group, tech support + health coaching group, or waitlist control group. In the clinical trials assessing physical activity [20, 28], participants in the control group were either waitlisted [28] or received a pedometer [20]. In addition to receiving access to the mobile apps, participants in the intervention group also received weekly remote consultations [20] or health coaching [28].

Similarly, in the nonrandomized controlled studies, apps were made available to participants in the intervention group, while the historically controlled group received standard care [18, 27]. For example, participants in the treatment group utilized the Interaktor app to send daily reports of symptoms for up to 3 weeks post-treatment [27]. The average refusal rate (those who refused to participate) across all intervention-based studies was 11.1% (range: 2% [24] to 31% [27]). At the final follow-up, attrition rates ranged from 11% [18] to 24% [20], with an average of 15.7%. Finally, the follow-up duration ranged from 3 weeks [24] to 3 months [18, 27].

In some studies, patients in the intervention groups had better adherence rates to daily symptom monitoring [24, 28], increased physical activity [20], lower levels of fatigue and nausea, and reduced burden in emotional functioning, insomnia, and urinary-related symptoms [18]. However, other studies reported no significant between-group differences in physical activity [28] nor uptake and completion rates [20].

Intervention barriers and facilitators

Reported facilitators to implementation and use of the intervention included availability of technical support [28], apps being downloaded for participants [27], and relevance of the app to addressing patients’ needs in a timely manner [16, 24, 26] which provided a sense of security to participants as they felt their concerns were prioritized, providing devices for use [17, 24], and an embedded interface to connect with the healthcare team provided through the platform [16, 17, 28]. Barriers included network and other device connection issues [19, 28], lack of end-user familiarity with the app [16, 19, 26], hurdles associated with logins [17, 28], the burden of use [21, 24, 26], and lack of time [16, 26].

mHealth characteristics

Terms used to describe mHealth ranged from mHealth [18, 20, 24, 26, 28], app [23–25, 27], mobile software [23], and mobile smart devices [16] to mobile health/smartphone application [16, 22]. Of all the included studies, only one provided an operational definition for the term “m-health,” defining it as the use of portable devices such as smartphones or tablets as an ideal tool to engage patients for health purposes [26, 31]. Strengths and added value of using mHealth in patient care included facilitating improvements in overall patient outcomes [22], improving physical activity [28], improving patient-provider communication [16], ease of use, and implementation of interventions [15, 24, 26], especially in vulnerable populations [17]. Other benefits included the delivery of patient-centered care [18], patient self-empowerment and self-care, symptom management and control [25], and broad applicability to other health conditions and behaviors [21]. Limitations of mHealth-delivered interventions included selection bias (especially when participants were excluded for not owning smartphones) [20, 28], lack of fidelity measures in intervention delivery [28], usability issues, especially among the older participants [26], operational costs, logistics, interfacing, and data interpretation [25].

Finally, recommendations provided for effective mHealth delivery–based interventions included reminder prompts [23], clarifying FAQs [23], continuous content development and adjustment of app features [17], consideration of participants’ preferences (e.g., adjusting the reporting time) [24], self-identity (e.g., the use of younger and “healthy looking” fitness experts in videos targeted to older CaP survivors) [24], as well as established trust with their healthcare providers [21], and fixing bugs, improving synchronization with network server and internet data usage [19].

Discussion

This scoping review highlights the broad utility of technology-based interventions guided by mobile applications in CaP care, especially for patient engagement, self-management of symptoms, and treatment side effects. A total of 14 studies that used mobile applications and web-based interventions were included in the review. This review is also the first to describe mobile apps used in CaP care that include a description of app-specific domains (e.g., framework, features, components, funding) as well as strengths/added value and limitations of mHealth and recommendations for intervention delivery. The studies included in this scoping review were conducted from 2015 to 2021 in six countries, with 43% in Sweden alone (6 out of 14 studies). This demonstrates the increasing use of mHealth applications in developed countries in delivering patient-centered interventions.

The most commonly used operating systems were Android and iOS, with most studies (43%) developing apps for both Android and iOS platforms, followed by Android only. This is expected considering the high prevalence of use of both operating systems. Moreover, based on 2019 statistics, Android holds an 87% share of the global market of smartphone usage [32]. Therefore, given the potential to reach a broad population, mHealth apps should be made available to run on both operating systems and even extend use to include web browsers. Nonetheless, user preferences, budget, and intervention goals should be considered when deciding which operating system to use [33].

The target group of most studies (71%) comprises men in active treatment with intervention mostly aimed at the management of symptoms and treatment side effects. One reason may be that the treatment of CaP comes with a myriad of side effects that significantly impact QoL [34–38]. Therefore, the ability to have symptoms closely monitored by their healthcare providers demonstrates the value of mobile applications for patients with cancer. In addition to this utility, the use of push notification reminders improved adherence to outcomes, which has been well documented in other studies [39, 40]. For example, in the study by Crafoord et al. [24], high adherence to daily symptom reporting, lower symptom rates, and burden were reported in the intervention group using the Interaktor app compared to those receiving standard care alone. Other studies have also demonstrated high end-user acceptance of remote monitoring via mHealth apps [41, 42].

mHealth apps also allowed participants to connect and work closely with their healthcare team. Close monitoring of symptoms with adequate response time and the delivery of high-quality care to patients with cancer requires a multidisciplinary team that involves physicians, nurses, and psychologists. In this regard, mHealth interventions can improve patient-provider trust, communication, and relationships. Beyond symptom tracking, apps included in this study also increased patients’ access to reliable, personalized information from their healthcare providers. Collaborative approaches like this can be incorporated into mHealth apps developed for CaP care.

In one study, patients reported feeling a sense of safety knowing that their urgent concerns would be addressed promptly [16]. This also shows how mHealth apps can be used in a complementary way to improve patient experiences with their healthcare providers [43]. Interestingly, although most reminder prompts were daily, most participants did not find reporting symptoms daily to be burdensome [17, 24]. This finding contrasts with a study that reported that older participants found mHealth apps to be intrusive and an invasion of privacy [44]. Future mHealth intervention efforts could consider incorporating patients’ preferences, given their impact on adoptability and feasibility of use.

Real-time assessment and monitoring, evaluation, and data collection are also attributes associated with mHealth apps, which can help overcome mobility and logistic issues that patients might experience [45]. This ability to capture real-time patient-level data has a significant potential for use in patients undergoing chemotherapy treatment to aid in effective decision-making and delivering tailored interventions. Mobile apps have also been associated with improvements in overall outcomes. For example, the study by Roberts et al. [21] demonstrates that the use of mobile apps is associated with increased physical activity. Improvements in additional patient-reported outcomes have also been extensively described in other studies [46–48]. Symptom management and monitoring were the most frequent patient-reported outcome measure in the apps reviewed in this study. Indeed, remote assessment of symptoms in patients undergoing active treatment is an essential contribution of mHealth due to the portability and ease of use of mobile devices [49].

Facilitators are essential factors to consider when promoting the adoption and use of mHealth apps. In the studies included in this review, the ability to connect with healthcare providers, receive quick feedback, and address patients’ needs were facilitators reported, which have also been described in other studies [50–53]. These motivators should be considered carefully when designing mHealth interventions to maintain the interests of patients undergoing cancer treatment. Likewise, as documented in this review, barriers to the use of mHealth apps (such as the burden of use, end-user hurdles, and time constraints) reflect the need for flexibility and to incorporate patient preferences in the content and delivery of interventions for CaP patients. Incorporating mHealth apps into routine care can also be time and resource-consuming, and therefore, the use is still limited [25]. In addition, none of the mHealth apps included in this review was routinely used in standard care. One of the reasons could be that nearly all the apps included in this study were mostly preliminary studies designed primarily to test feasibility (including effectiveness); thus, none focused on the actual implementation or intervention scale-up. This failure of mHealth apps to launch in routine clinical use is consistent and not just isolated to CaP apps but has been reported widely in other settings as well [54–56]. Perhaps future studies could investigate possible logistic and implementation challenges of integrating mHealth applications in routine clinical care, specifically, integrating an implementation science framework into the development of future mHealth app development to help improve incorporation as standard care [57]. Nonetheless, gains observed in the significant outcomes from mHealth interventions, as discussed, most importantly symptom management and clinical outcomes, have the potential to outweigh barriers to implementation.

A strength of this review is the comprehensive scoping methodology [14] that included available features of mHealth apps, intervention types, barriers, and facilitators. The review process also included at least two independent coders with a defined list of exclusion and inclusion criteria. However, this review also has some limitations that should be acknowledged. Although the search was designed to be comprehensive, it is possible that some articles published in other databases were not included. Including English-only published articles may also have limited inclusion of potentially relevant non-English studies. Because most of the apps identified in this study are no longer available for public use, it was impossible to download and interact with included apps to explore and evaluate their contents. Given that most of the apps were developed in Sweden and participants were predominantly White men, there are additional limitations surrounding cultural applicability and generalizability. For example, culturally designed mHealth apps might serve the unique needs of ethnically diverse minority groups. Despite these limitations, the findings summarized in this review provide evidence sufficient to address the study’s aims and objectives.

Conclusion

mHealth apps have broad utility in CaP care, especially for the self-management of symptoms and treatment side effects. Findings from this review will inform future research focused on developing and refining mHealth interventions for CaP. Notably, overall findings indicate an urgent need to focus greater attention on the cultural relevance and suitability of apps for ethnically diverse CaP populations, especially in routine clinical use.

Supplementary Material

Supplement

The online version contains supplementary material available at https://doi.org/10.1007/s11764-022-01328-3.

Funding

This research was primarily supported by National Institute on Minority Health and Health Disparities (NIMHD) grant R25MD011564 (to MO). Partial support was additionally provided by Oklahoma Tobacco Settlement Endowment Trust (TSET) grant R22-02, and National Cancer Institute (NCI) Cancer Center Support Grant P30CA225520 awarded to the Stephenson Cancer Center.

Footnotes

Declarations

Competing interests The authors declare no competing interests.

Conflict of interest The authors declare no competing interests.

Data Availability

Relevant data generated and analysed during this study are included in this published article and its supplementary information files.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement
NIHMS2104797-supplement-Supplement.docx (14.4KB, docx)

Data Availability Statement

Relevant data generated and analysed during this study are included in this published article and its supplementary information files.

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