Supportive eHealth Technologies and Their Effects on Physical Functioning and Quality of Life for People With Lung Cancer: Systematic Review

Introduction

Background

Lung cancer is the most prevalent cancer globally, with 2.21 million new cases being diagnosed in 2020; this is anticipated to increase to 3.8 million by 2050 [1]. Lung cancer also accounts for the highest number of cancer-related deaths across all cancer types [2].

In the United Kingdom, approximately 48,500 new lung cancer cases are diagnosed per year [3]. The incidence of lung cancer strongly correlates with age, peaking among older individuals. In the United Kingdom, from 2016 to 2018, >44% of new cases annually were in those aged ≥75 years. Age-specific incidence rates rise sharply from around ages 45 to 49 years, reaching a peak in women aged 75 to 79 years and men aged 85 to 89 years, and then decline in older age groups. Women typically have lower incidence rates than men, particularly evident at age ≥90 years, where the rate in women is half that of men [3]. One-year survival rates have almost doubled since the 1970s due to early diagnosis and improved treatments. However, lung cancer survival rates at 5 and 10 years have not improved as much as those for other cancers [3].

For people living with lung cancer, it is imperative that supportive care needs, which are central to patient-centered care [4], are addressed promptly because their condition is associated with a high symptom burden and high levels of unmet needs throughout the disease trajectory [5]. In addition, approximately two-thirds of people with lung cancer have at least 1 other preexisting health condition, and up to half have ≥2 preexisting health conditions [6]. Addressing their supportive care needs will contribute to efficient use of health care resources and minimize hospital admissions. If not managed effectively, this could negatively impact patient outcomes, including physical functioning, psychological well-being, and health-related quality of life (HRQoL) [4].

Common symptoms of lung cancer include fatigue, breathlessness, and depression—all of which can be alleviated by exercise interventions [7]. More generally, other positive implications for people with cancer undertaking physical activity include improvements in HRQoL [8-11], lung function [8,10], sleep [8,12], immune function and markers [8,13], mood [8,9], bone strength [8,14], and muscle mass [8,15], as well as decreased cancer cell proliferation [8,13]. However, less than one-third of people with lung cancer meet recommended exercise guidelines to reduce time spent sedentary, increase strength- and balance-building activities, and undertake 150 minutes of aerobic activity per week [16].

Self-management practices, including those with an exercise component, can help patients with cancer to regain health and fitness, reduce side effects from treatment and symptoms of the disease, relax the mind and body, enhance HRQoL, and regain a sense of normality [17]. More recently, the National Institute for Health and Care Excellence has recognized exercise as a first-line treatment within health care and holistic disease management [18]. In the absence of a robust national rehabilitation system, there is pressure for self-management support to be integrated into routine cancer care [19]. However, patient adherence to rehabilitation programs delivered at hospital outpatient centers can be low due to the required travel and associated socioeconomic factors [20]. Furthermore, studies have demonstrated that home-based rehabilitation improves patient adherence and HRQoL [21].

Web-based interventions have grown in popularity in recent years. These interventions enable the user to independently navigate a recommended online program that is operated via a website with the aim to create positive changes in health and well-being [22]. Government organizations are actively trying to transition in-person activities to web-based platforms [23]. After the COVID-19–related lockdown, this gained renewed prominence because the advantages of digital technology became increasingly evident. Furthermore, earlier reviews have identified several mobile and electronic apps designed to support various stages of the cancer continuum ranging from prevention to survivorship [24]. The use of mobile health (mHealth) and eHealth technologies, such as wearables and activity trackers as well as apps and web-based programs that can be accessed via smartphones and tablets, provide new methods for educating, monitoring, and supporting patients with chronic conditions and cancer. The World Health Organization recognizes the potential of mHealth and eHealth interventions to support health care delivery [25]. These interventions can assist patients in self-managing their health behaviors and are considered feasible, acceptable, and effective approaches to providing supportive care [26,27].

There is a growing body of evidence to support technology interventions in health care, and this is supported within the UK National Health Service Long Term Plan [28]. Nevertheless, evidence-based mHealth and eHealth interventions to enhance exercise and physical activity for people with lung cancer remain uncommon. Furthermore, of the cancer-related apps that are available, a limited number adopt a personalized approach to physical activity and exercise that accommodates patients’ preferences.

Aim

This study aims to identify the mHealth and eHealth technologies that have been developed to support people with lung cancer to improve or maintain physical functioning and enhance their HRQoL.

Objectives

The primary objective of the review was to determine whether any of the mHealth or eHealth technologies identified impacted the physical functioning and HRQoL of people with lung cancer.

The secondary objectives were to assess the demand on clinician time; evaluate the acceptability of the intervention to patients, carers, and health care professionals; investigate user satisfaction with the technology; identify security features (clinical safety, data protection, and technical security); and examine the cost impact of the mHealth or eHealth app or intervention.

Methods

Design

This review was prospectively registered with PROSPERO (CRD42023414094) and is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to improve the quality of the review and ensure transparency at all stages.

Inclusion Criteria

Types of Studies

We included all primary research studies, without study design or publication status limitations or language or geographic area restrictions. Case studies were also included. Reference lists of systematic reviews were cross-checked to identify any potential studies for inclusion. We limited the search to studies published after January 1, 1990, because internet interventions did not exist before this date [29].

Population

We included studies that were undertaken with adults (aged >18 y) who were diagnosed with lung cancer, regardless of the stage of their disease, treatment allocation, sex, or where they received care.

Intervention

Study participants in the included studies must have been exposed to a physical activity, exercise, or training intervention that was delivered via an electronic or web-based application with or without a comparator.

Outcome Measures

Studies were included if they reported on the impact of the digital intervention on physical function or HRQoL or both physical functioning and HRQoL using any validated measure. We included studies that measured impact at ≥1 time points.

Exclusion Criteria

The following studies were excluded: (1) cancer studies in which the total number of participants with lung cancer accounted for <50% of the study population, (2) studies that focused on telehealth care only and did not include an electronic or web-based intervention (eg, studies that evaluated remote sessions with a clinician via video link), and (3) studies in which apps that were used to track activity could be linked to a wearable device but did not provide any other support.

Search Strategy

A search of 6 databases—PubMed, Web of Science, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO—was carried out on April 17, 2023, via EBSCO, using a list of key terms focusing on 3 distinct categories: the intervention characteristics (eg, web-based, internet, app or application, remote, and digital), physical functioning (eg, activity, exercise, training, movement, and athletics), and the population of interest (eg, patients with lung cancer or survivors of lung cancer and patients with cancer). These were amalgamated using Boolean operators to formulate a comprehensive search string. The full list of search terms is presented in Textbox 1. Other sources, such as references of included records, were also searched.

Data Collection and Analysis

Selection of Studies

Of the 794 papers identified through the database search, 213 (26.8%) duplicates were removed, leaving 581 (73.2%) papers. Two reviewers (SK, PRW, CH, or ZD) screened the titles and abstracts of these 581 papers, excluding 544 (93.6%) in the process and retaining 37 (6.4%). Of these 37 papers, a further 7 (23%) were excluded because they were conference abstracts. A full-text screening of the remaining 30 papers was then conducted, and 22 (73%) were excluded, leaving 8 (27%) for final inclusion. Any discrepancies identified by the reviewers during the screening process were resolved by discussion with a third member of the review team. The screening process is outlined in the PRISMA diagram (Multimedia Appendix 1).

Data Extraction

Data were independently extracted from the included papers by the lead author (SK) using a data extraction template developed for the purpose of this review. The extracted data were reviewed by the coauthors (CH, ZD, and PRW), and discrepancies were resolved. In case of missing study data, we attempted to contact the corresponding authors to obtain the required information. Three authors were contacted [30-32], but none replied. However, these papers were not excluded because they still met the inclusion criteria and reported on some of our objectives. We extracted the data shown in Textbox 2.

Critical Appraisal

Risk of Bias

Two reviewers (SK and ZD) independently assessed the risk of bias of each included study and confirmed agreement. The JBI checklists [33] were used to assess the methodological and reporting quality of the included studies.

Quality Appraisal

The Grading of Recommendations Assessment, Development, and Evaluation approach was adopted to assess the quality of the evidence used to support the synthesized findings [34,35].

Data Synthesis and Analysis

Due to the methodological heterogeneity of the included studies, we were unable to undertake a meta-analysis. Instead, the Synthesis Without Meta-Analysis reporting items checklist was used to aid transparency in the reporting process [36]. This enabled us to report on the key features of the included studies, group the studies, and explain our findings. A narrative synthesis [37] was also undertaken in accordance with the study objectives stated earlier. This allowed us to provide a comprehensive summary of the impact and effectiveness of the interventions identified in the included studies.

Results

Overview

Eight papers were identified as suitable for inclusion. However, 2 (25%) of these 8 papers [30,38] reported on different stages of the same study; therefore, only 7 studies were included in the review. The screening process is outlined in the PRISMA diagram presented in Figure 1.

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Study Characteristics

The included studies were published between 2013 and 2022 across international settings, including the United States [30,38], the Netherlands [39], South Korea [32,40,41], the United Kingdom [31], and Canada [42].

The studies were undertaken between 2010 [30] and 2018 [40]. The aim of the included studies was to develop a technology [31] and test its usability [39], feasibility [31,39,41,42], acceptance [30,31], and efficacy [30,32,38,41]. The studies were primarily quasi-experimental and nonrandomized experimental studies [30,32,39,41,42]. Only 1 randomized controlled trial [40] and 1 cohort study [31] were included. The research methods used were predominantly quantitative (5/7, 71%) [30,32,40-42], with the remaining studies using mixed methods (2/7, 29%) [31,39].

Participants were identified from secondary care settings. The number of study participants ranged from 5 [42] to 100 [41], and their mean ages ranged from 55.1 (SD 8.7) years [41] to 64.6 (SD 6.5) years [30,38]. Overall, there were more female participants (195/340, 57.4%) than male participants (145/340, 42.6%) across 4 (57%) of the 7 studies [30,39-41]; the study by Kadiri et al [31] did not report the sex breakdown of the participants. The intervention duration ranged from 6 to 12 weeks; the study by Kadiri et al [31] did not report the intervention duration, but it was clear that the intervention was delivered pre- and postoperatively with a study duration of ≤18 months. The characteristics of the included studies are detailed in Table 1.

Quality Appraisal

The risk of bias for all studies was low, and quality appraisal scores were moderate (Table 1).

Intervention Characteristics

The review identified 7 technologies that had been studied in people with lung cancer: 4 (57%) mobile apps [31,32,40,41] and 3 (43%) web applications [30,38,39,42]. Of the 3 web applications, 2 (67%) used a gaming console to deliver part of the exercise prescription [30,38,42].

The interventions were delivered or accessed from various settings: web based [31,32,39,40], home based [30,38,42], or a combination of outpatient department and home based [41].

The frequency, intensity, time, and type formula [43] was used to extract key components of the exercise prescription of each study. Only 5 (62%) of the 8 papers reported the full details of the exercise prescription according to the frequency, intensity, time, and type formula [22,30,31,38,41].

The papers that did not provide a detailed exercise prescription provided more general information and recommendations regarding physical activity [32,39]; alternatively, the interactive app would support the participant to edit the frequency, intensity, and duration of the exercise [40]. Where the technology included an interactive patient portal, it was noted that the physical activity support program that was incorporated in the portal was only used by one-third of the participants [39].

The intervention details are summarized in Table 2.

Outcome Measures

Primary Outcome Measures

Physical Functioning

All included studies demonstrated some improvement in physical functioning, but the methods of assessment varied. The most common assessment was walking time and walking distance. In the studies by Ji et al [40] and Coats et al [42], participants demonstrated an improvement in the 6-minute walk test and the 6-minute walk distance test, while Yang et al [32] noted an improvement in the 2-minute walk distance test. An increase in walking duration and step count was also observed by Hoffman et al [30], while Kadiri et al [31] noted an increase in the distance covered in the shuttle walk test.

No improvement in muscle strength was noted by Coats et al [42], while Yang et al [32] noted an improvement in lower extremity muscle strength but not in upper extremity muscle strength. Only the study by Hoffman et al [30,38] showed an improvement in balance and cancer-related fatigue, while the study by Ji et al [40] showed an improvement in dyspnea grade.

The study by Groen et al [39] identified no significant improvement in physical activity, but an improvement in vigorous activity over time was noted. Finally, the study by Park et al [41] found that although exercise capacity improved in stable patients, this was not the case in patients with progressive disease.

Impact on HRQoL

Almost all studies (6/7, 86%) included in our review reported the impact of the digital health intervention on HRQoL [31,32,39-42].

The tool most commonly used to assess HRQoL was the European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire-30. Improvements in symptom scales were observed by Kadiri et al [31] and Park et al [41], improvements in functional scales were observed by Yang et al [32] and Park et al [41], while Coats et al [42] did not observe any significant changes.

Ji et al [40] used the EuroQol visual analog scale score to demonstrate a significant improvement between visit 1 and visit 3, but the EQ-5D scores did not differ between the same time points. The Short Form Health Survey-36 was used in the study by Groen et al [39], but no significant changes in the scores were noted.

Secondary Outcome Measures

Demand on Clinician Time

The technologies varied with regard to the level of clinician involvement required for implementation and ongoing patient management. The authors attempted to assess the impact on clinician time across the included studies (Table 3). Only the study by Kadiri et al [31] explicitly reported the impact of adopting the intervention on health care professional time: 60 minutes.

Acceptability and User Satisfaction

All included papers reported either user satisfaction [40-42] or both acceptability and user satisfaction [30-32,39], as summarized in Table 3, and demonstrated high levels of acceptability and user satisfaction.

Safety Features

The studies reported primarily on clinical safety features of the technology (eg, access to a mobile phone while exercising to contact the study nurse [30,38], heart rate monitoring [30,31,38,40], and monitoring of peripheral oxygen saturation levels [31], as noted in Table 3). Of the 8 studies, 2 (25%) also reported on the technology’s security features to ensure limited authorized access to the app [40] or database [39].

Cost-Effectiveness

None of the studies evaluated the cost-effectiveness of the digital health interventions in terms of clinical outcomes within a health economic context. Only the single-center cohort study [31] reported on the cost of implementing the technology, which was estimated to be between £16 (US $20.8) and £34 (US $44.2) per patient.

Discussion

eHealth: Benefits and Concerns

eHealth, which encompasses information and communication technology, facilitates remote care delivery and health information transmission, promoting patient involvement, improving care quality, and enhancing accessibility, particularly in remote areas. Despite benefits such as convenience and reduced travel, concerns exist, such as privacy issues, fears of diminished human interaction, and lack of access to technology and internet infrastructure [44]. eHealth holds promise in supporting patient-centered care and empowers patients by enhancing their involvement in their health care [45].

Principal Findings

To our knowledge, this is the first review that has looked specifically at digital technologies developed for people with lung cancer and their impact on physical functioning and HRQoL. Despite searching across a wide range of databases, we were only able to identify 7 studies for inclusion in our review.

The findings of this review suggest that digital health interventions with an exercise component are acceptable to people with lung cancer because the interventions help them play a more active role in their health care and can positively impact their physical functioning and HRQoL. Various tools were used in the studies to assess HRQoL. The EQ-5D, a questionnaire conventionally used for economic cost analysis, was used in the study by Coats et al [42] to assess HRQoL, and the authors noted that, despite an observed increase in the EuroQol visual analog scale score, the EQ-5D score did not improve. The evidence to support the use of digital exercise interventions remains sparse, and we have drawn on the findings of only a few studies that aimed primarily to develop and assess the feasibility of the intervention used.

Nevertheless, the quality of the studies was high, thereby strengthening the credibility of the review findings. Further research is required to observe the effects of the interventions over a longer period and to explore the potential cost savings associated with using a remote health management mobile app, including a reduction in clinician time or the number of consultations, improved disease management, and the costs of implementing the technologies in routine care, as well as the safety and security risks of the technologies.

Results in Context

In recent years, there has been a growth in the number of digital health technologies, including mHealth and eHealth, as well as wearable devices that are becoming an integral part of modern health care [46]. The promotion of patient self-management practices facilitated by digital technology has gained attention because it can improve patient engagement and health care delivery [47]. This has become particularly relevant and has intensified in the wake of the pressures placed on the health service during the COVID-19 pandemic [22]. It is anticipated that strategies to encourage patients to self-manage their health behaviors will continue to be integrated into care pathways in the future [48].

In the context of lung cancer, approximately two-thirds of the people with lung cancer have at least 1 other preexisting health condition, and up to half have ≥2 preexisting health conditions [6], making this group more complex to treat because they require a more tailored approach to rehabilitation [49]. We note that the technologies identified in our review either produced an exercise prescription relevant to the individual participant’s capability or gave the patient guidance on exercises they might be able to undertake. As the participants’ exercise tolerance improved, the prescription was updated, affording a bespoke approach to rehabilitation that might be more relevant to people with lung cancer.

This review highlights the benefits of a digital health intervention that included an exercise component and demonstrated improvements in cancer-related fatigue and balance [30,38], walking duration or distance or step count [30-32,38,42], general activity levels, dyspnea [40], and muscle strength for people living with lung cancer [32]. Nevertheless, there are still areas for development; for example, the study by Yang et al [32] found that while there was an increase in lower extremity muscle strength, no change in upper extremity muscle strength was noted, and, in another study [41], for participants with progressive disease, no improvement was observed in exercise capacity or dyspnea scale score. Furthermore, none of the included studies provided a bespoke exercise prescription and demonstrated a positive impact on exercise endurance, muscle endurance, or dyspnea symptoms. This reinforces the need to produce an exercise prescription that is relevant to the participants’ disease status and individual exercise goals. More consideration is required to develop technologies with intelligent algorithms or artificial intelligence that can support persons with lung cancer to input data on their current condition and capability before a safe, bespoke exercise prescription is recommended. This needs to be factored into app development and further research.

Improving HRQoL is a priority goal in supporting people with lung cancer [49]. All studies that assessed the impact of HRQoL reported a positive impact. The direction of the association between HRQoL and improved physical functioning was consistent across most of the studies (n=5, 71%) that reported on both outcomes [31,32,40-42]. Improvements were primarily noted in the global health score as well as the symptom and functional scales, but it is unclear whether these improvements are a direct result of the exercise intervention or improved disease management and control.

This review highlighted the high levels of acceptability and participant satisfaction with the digital intervention. Having a home-based exercise intervention or web-based intervention, such as the technologies highlighted in this review, is advantageous because participants can continue to exercise during chemotherapy treatment when they are usually advised to limit their contact with other people to reduce the risk of infection. Furthermore, the studies that used a game console as part of their exercise program incentivized their participants to use the technology, work out more often, and increase the intensity of the exercise [30,38,42]. Other useful features of the technologies that have been highlighted include the use of a virtual environment for exercising [30,38], dance-based workouts [42], dietary advice [32], and the value of incentivizing participants [30,38,42]. Hence, it is important for future app developers to consider what incentives can be built into the intervention to improve adherence, reduce clinician workload, and empower patients to be more independent regarding their disease management.

It is anticipated that health technologies, such as the ones identified in this review, will enable improved disease management, reduce hospital admissions, and save the UK National Health Service money [31]. However, limited cost analyses have been conducted to date to demonstrate the cost impact on health care services, the health care workforce, and people living with lung cancer. Our review identified only 1 cohort study [31] undertaken at a single research center that reported the cost of the intervention to the health care provider and the estimated time impact on health care professionals.

Limitations

We reported on evidence available within the published papers. We faced challenges in gathering details regarding missing study data. We made attempts to reach out to the corresponding authors to obtain the necessary information, but we did not receive any response. Furthermore, it is unclear whether any development information not included in the reports of the studies was excluded [30-32]; in other words, important details could have been left out of the study reports, and the reasons for their possible omission are not known.

The studies included in our review were primarily quasi-experimental and nonrandomized studies [30,32,39,41,42]. Only 1 randomized controlled trial [40] and 1 cohort study [31] were included. The studies focused on technology development and feasibility assessment, most frequently for patients undergoing surgery [30-32,39,40]. In addition, the small cell subtype of lung cancer has poorer outcomes than non–small cell subtypes, with a primary treatment focus being to improve HRQoL; this population was not well represented in the included studies. Furthermore, various tools were used to assess physical functioning and HRQoL, making it difficult to draw comparisons across the studies due to their heterogeneity.

Conclusions

There seems to be a consistent relationship between the use of digital technologies by people with lung cancer and a positive impact on physical functioning and HRQoL. The technologies reported in this review demonstrated high levels of acceptability or user satisfaction and have the potential to support people with lung cancer to manage their health more independently. However, most of the studies (n=5,71%) identified here report on the development and feasibility of the technology. Further multicenter, large-scale research studies using a randomized controlled trial design over an extended period and including a cost-effectiveness outcome measure are required to fully assess the true benefit of adopting eHealth technologies in standard care services for health care providers and people with lung cancer.