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Lung cancer (LC) is the leading cause of cancer-related death worldwide [], with approximately 2.5 million new cases diagnosed each year []. Although most patients are diagnosed at an advanced stage (stage III or IV), where 5-year survival rates remain poor [], recent treatment advances have led to meaningful improvements in survival []. As such, a growing number of individuals are living with LC for longer periods, which has important implications for ongoing supportive care into survivorship. A diagnosis of LC may have a substantial impact on a patient’s psychological well-being, as well as their overall quality of life (QoL). While these are related concepts, in the context of this report (and in line with the literature that distinguishes between them []), we take QoL to represent the cognitive appraisal a patient makes about their situation in life, while psychological well-being refers more to a patient’s emotional experience or mental health symptoms. However, it is important to consider these 2 concepts in parallel when considering how best to support patient well-being, as psychological symptoms and QoL are closely interlinked in a bidirectional manner in patients with LC [].
In addition to the distress caused by the diagnosis itself, there are several cancer and treatment-related factors that might negatively impact well-being and QoL in patients with LC. For example, the physical symptoms of LC (eg, breathlessness, pain, and fatigue) and associated paraneoplastic syndromes [] may functionally impact patients and directly adversely affect their QoL and mental health []. Furthermore, both tumors and cancer treatments (including chemotherapeutic agents, immunotherapy, targeted treatments, surgical procedures, and radiotherapy) may cause inflammatory responses and interfere with neuronal function or neurotransmission, which are both biological mechanisms recognized to play a role in the development of psychological disorders [-]. Indeed, patients with LC have been found to have significantly higher levels of clinical anxiety and depression [] and psychological distress [,] than both the general population and patients with other types of cancer.
Thus, there is a clear need to support the well-being and QoL needs of this patient group. However, research into well-being–related interventions for patients with LC is lacking. This is problematic, as poor psychological health and well-being may negatively impact patient outcomes. For example, research has found that persistent psychological symptoms are associated with low QoL and poor adherence to anticancer treatments [,], which may contribute to high symptom burden [] and increased mortality []. Furthermore, 2 meta-analyses have found a predictive relationship between depression (and, to a lesser extent, anxiety) and mortality in cancer patients [,]. Additionally, Andersen et al [] examined the trajectories of psychological symptoms over 2 years from diagnosis and found that remission of depression (and in 1 model, anxiety) was associated with improved survival, illustrating the potential importance of treating psychological symptoms and supporting well-being in these patients. Similarly, QoL at diagnosis [] and changes in QoL scores over time [] have been shown to predict response to treatment, symptom burden, and patient survival.
Despite the apparent importance of well-being and QoL in terms of patient outcomes, this area remains underresearched and undersupported. Across various cancer populations, 2 systematic reviews have found that patients frequently report having unmet psychological needs and a wish for support [,]. In LC populations, few receive adequate and readily accessible mental health support, and many are unaware of what well-being–related support is available []. As such, it is imperative that we identify better strategies to support them.
eHealth offers a potentially scalable solution that can increase timely access to health-related information, psychoeducation, and support, while also supporting treatment. The World Health Organization [] defines eHealth as “the use of information and communications technology in support of health and health-related fields.” As such, the modality and content of these types of interventions are extremely heterogeneous. They can include telehealth or telecommunication approaches, use of electronic health records, delivery of online health information and communication (including social media or forum peer-based support), the use of patient-reported outcomes (PROs) to monitor symptoms, health-related video games, and virtual reality platforms. Increasingly, this also includes app or web-based therapies (such as internet-based cognitive behavioral therapy [CBT] and mindfulness) or behavioral change programs, and electronic data capture of objective behavioral or physiological measures via wearable devices (eg, activity trackers such as FitBit [Google LLC], Apple Watch, etc). Across all platforms, eHealth interventions can be guided or unguided and used remotely or in person.
Over the last decade, a significant number of eHealth programs have been developed for various mental health conditions [] and in an array of different physical health contexts [,]. In the general population, eHealth programs designed to support well-being and psychological health have good acceptance [] and have demonstrated positive outcomes for common mental health disorders []. Furthermore, systematic reviews have also produced evidence to suggest that the use of eHealth programs is beneficial in other cancer populations []. While several well-being–related eHealth strategies and interventions have been developed to directly help patients with LC [-], these have not always been evaluated. One recent small-scale systematic review with patients with LC did show that eHealth is likely to be both acceptable and potentially efficacious with this patient group []; however, this review only focused on interventions designed to improve physical functioning and did not include psychological health outcomes alongside QoL. Thus, it is still unclear what types of eHealth interventions are most beneficial to patients with LC and to what extent they can impact QoL and emotional well-being.
The present study aimed to address this gap in knowledge and understanding by evaluating the potential impact of eHealth interventions on QoL and psychological well-being of LC populations. Furthermore, given the heterogeneous nature of eHealth approaches, this systematic review sought to extend previous work by exploring and describing all reported avenues and strategies for eHealth support. Thus, the secondary aim of the review was to characterize the nature of the eHealth interventions and strategies identified and explore their acceptability.
This systematic review was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42024509607). A systematic review of eHealth interventions and programs to support the psychological well-being and QoL of patients with LC was conducted between December 2023 and February 2024 following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines []. The generation and selection of search terms was informed by preliminary scoping searches to identify commonly used terminology in the LC, eHealth, QoL, and psychological literature, and variant spellings and acronyms were taken into account. A librarian with expertise in systematic review methodology was consulted, and the draft strategies were reviewed by all authors and subject specialists. The literature search was performed using 6 databases: PubMed, PsycINFO, MEDLINE, Scopus, Web of Science, and CINAHL, using combinations of search terms designed to capture a broad range of eHealth resources, including digital and telemedicine or therapy ( and ). As we wanted to capture all eHealth studies that measured QoL or psychological well-being outcomes, regardless of whether the impact on well-being was a primary aim, “all text” searches were performed in all cases. Reference lists of identified studies were also examined, and a cited-by search was conducted using Google Scholar to identify further high-quality studies. No restrictions were placed on the publication date.
Table 1. Search strategy.SearchTerms and Boolean operators1eHealth OR e-health OR mHealth OR m-health OR digital health OR web-based therapy OR telemedicine OR telehealth OR telepsychiatry OR teletherapy OR health informatics OR electronic OR iCBT OR ccbt OR e-therapy OR e-psychotherapy OR e-counsel* OR cyber-counsel* OR web-counsel* OR e-mental health2Internet OR online OR electronic OR cyber OR web OR mobile OR tele OR app OR application3Counselling OR counseling OR therapy OR psychiatry OR psychotherapy OR support OR health4Wellbeing OR well-being OR quality of life OR anxi* OR depress* OR psychological distress OR mood5Lung neoplasm* OR lung carcinoma OR lung cancer OR lung tumour OR lung tumorCombined search strategy = (1 OR (2 AND 3)) AND 4 AND 5Inclusion or Exclusion CriteriaWe included papers based on the Population, Intervention, Comparator, Outcomes, and Settings criteria outlined in . Studies that included participants with various cancers were eligible for inclusion, provided they broke down outcomes by cancer type and the impact on patients with LC could be determined.
Nonexperimental studies (eg, letters, reviews, case reports, guidelines, or protocols), those published outside of scientific journals, and those not published in the English language were excluded. Studies that only focus on the feasibility or technical properties of eHealth tools, or health care management using e-records or health care analytics with no reference to QoL or well-being outcomes were also excluded.
Textbox 1. Inclusion criteria.Inclusion criteria
Population: adults (aged older than 18 years) diagnosed with lung cancer.Intervention: interventions of any type (including informational, behavioral, therapeutic, self-guided, and clinician-guided interventions, using either individual or group approaches) aimed at patients with lung cancer who have at least 1 essential eHealth component. eHealth was defined as the use of digital systems or information and communication technologies to support health and well-being–related issues.Comparator: treatment as usual and waitlist groups (both active and inactive).Outcomes: validated scales measuring psychological well-being (eg, depression, anxiety, and psychological distress) and quality of life, including (but not limited to): Quality of Life: EQ-5D, World Health Organization Quality of Life Scale, Functional Assessment of Cancer Therapy, European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire; Anxiety: Depression, Anxiety and Stress Scales, Beck Anxiety Inventory, Hospital Anxiety and Depression Scales; Depression: Depression, Anxiety and Stress Scales, Beck Depression Inventory, Hospital Anxiety and Depression Scales; Psychological distress: Kessler Psychological Distress Scale; Clinical Outcomes in Routine Evaluation; distress thermometer.Settings: experimental and quantitative intervention studies (including randomized controlled trials, quasi–randomized controlled trials, and single-arm pre- or poststudies) in any setting.Selection ProcessFollowing the removal of duplicates using review management software Rayyan.ai (Rayyan Systems, Inc), all 3 authors conducted the initial screening of titles and abstracts based on the inclusion and exclusion criteria. Given the large number of studies initially identified, each author independently screened one-third of the studies, and a random subset of 10% was reviewed by at least 1 other author. Where there was uncertainty around eligibility, the full text was reviewed by all 3 authors and discussed.
The full texts of potentially eligible studies were then retrieved and assessed by 2 independent reviewers (KJ and VH). Papers were rejected if they did not meet all inclusion criteria or if any exclusion criteria were identified. Any uncertainties or disagreements that arose were resolved through discussion. A PRISMA flow diagram illustrates the study selection process, including the number of studies identified, screened, eligible, and included in the final review ().
Figure 1. PRISMA systematic review flow diagram. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; QoL: quality of life. Data ExtractionData from the included studies were extracted independently by 2 reviewers (VH and KJ) using a bespoke standardized data extraction form prepared in Excel (Microsoft Corp). The extracted data included: study characteristics (authors, year of publication, country, study design, study aim, sample size, setting, and data collection points); participant characteristics (age, gender, ethnicity, and cancer type and stage); eHealth program details (nature of the program, mode of delivery [eg, mobile app, web-based platform, telemedicine], LC specificity, degree of clinician input, number of sessions, and duration); outcomes (measures used); acceptability and engagement (patient satisfaction, fidelity, and attrition rates) and key findings. Data extraction was shared evenly, with each reviewer extracting data from 50% of the included studies. To ensure reliability, a random sample (10%) from each reviewer was extracted in duplicate. No conflicts occurred.
Quality AssessmentThe methodological quality of the included studies was independently assessed by 2 reviewers (VH and KJ) using a modified version of the Downs and Black checklist []. This method was chosen as it can be used to evaluate both randomized and nonrandomized studies of health care interventions, and it has been ranked in the top 6 quality assessment tools suitable for use in systematic reviews []. It has good criterion validity (r=0.90) and good interrater reliability (r=0.75). The checklist comprises 27 items that address several methodological issues, including reporting, external validity, internal validity (bias and confounding), and power. In line with previous studies, we used a modified version of the scale that simplifies the scoring associated with statistical power. While this question carried 5 points in the original scale, more recent studies have awarded this a single point to signify whether a study had sufficient power to detect a clinically significant effect (1) or not (0) [,]. Thus, 26 items on the scale are rated as either yes (1), no (0), or unable to determine (0), and 1 (“Are the distributions of principal confounders in each group of subjects to be compared clearly described?”) is rated on a 3-point scale (yes=2, partially=1, and no=0). Item scores are summed to produce a total score ranging from 0-28, with higher values representing better study quality. Using these scores, studies were rated as having poor (≤14), fair (15-19), good (20-25), or excellent (≥26) methodological quality.
Data Evaluation or SynthesisA narrative synthesis of the findings was conducted in line with the “synthesis without meta-analysis” guidelines []. Due to the heterogeneity in study designs, eHealth programs, and outcome measures, a meta-analysis was not performed. Instead, following recommendations in the Cochrane Handbook for Systematic Reviews of Interventions, vote counting based on direction of effect was used for the quantitative synthesis of results, as synthesizing P values was not possible with the available data [,]. For the purpose of quantitative synthesis, outcome domains of interest were defined as: QoL (including total QoL scale scores, global QoL measures, and functioning-related QoL subscales), anxiety, depression, and distress. Note that specific physical symptom burden items or subscales (eg, pain, fatigue, or nausea) were not included as independent QoL measures in the effect direction synthesis, as these are often more influenced by disease progression or treatment toxicity than by behavioral or psychosocial interventions. For each outcome domain, we compared the number of studies showing a beneficial effect of the intervention (for pre-post study designs) or a relatively beneficial effect of intervention group (IG) membership (for randomized controlled trials [RCTs] or other studies looking at between group differences in scores over time), with those showing a negative effect. In line with the guidance, we did not take statistical significance or magnitude of effect into account within this quantitative synthesis [,]. If a study assessed the same outcome (eg, QoL) using multiple measures (eg, across several subscales), we determined the overall direction of effect based on consistency across those measures. Specifically, when 66% or more showed the same direction of effect (eg, all indicating benefit or all indicating harm), we classified this as the overall effect direction. If fewer than 66% of the measures yielded outcome effects in the same direction, we labeled the effect as mixed. Across all studies, a sign test was used to determine whether the observed pattern of results (ie, the proportion of positive versus negative outcomes) was significantly different from chance.
Results were also grouped thematically according to the type of eHealth intervention under investigation. Unfortunately, statistically investigating heterogeneity through subgroup analyses based on intervention type was not possible due to a lack of sufficient data. Likewise, it was deemed inappropriate to perform separate sign tests for each intervention type due to the lack of power that the relatively small number of studies in each group affords. As such, the impact of the different types of eHealth interventions on patient outcomes, adherence, and QoL was explored more narratively. Greater emphasis was placed on studies that were rated as having higher quality according to the criteria above, and concerns regarding the quality of evidence were reported where necessary.
After excluding duplicates, 7065 records were screened against the inclusion and exclusion criteria. This resulted in 7022 exclusions. A total of 43 full-text reports were reviewed in greater depth for additional information, of which 31 were deemed eligible for inclusion. Two additional studies were identified as being eligible from the reference lists of the included reports, resulting in 33 studies in the final review ().
Study Characteristicsprovides an overview of the studies included in this review, including information about the design of each study and the nature of the eHealth programs under investigation. Recruitment methods were not always reported, but where described, typically occurred in clinical settings as part of routine diagnostic or treatment appointments. Countries that contributed multiple studies to the review were the United States (n=12, 36%), China (n=9, 27%), South Korea (n=3, 9%), and the Netherlands (n=2, 6%). Sample sizes ranged from 16 to 515 participants. The included literature was published between 2014 and 2024, with the majority (n=19, 58%) being published in 2020 or later.
Table 2. Study characteristics.Authors (year) countryStudy designStudy aimSample sizeRelevant outcome measuresData collection pointsQuality scorePatient education or health literacy interventionsaIG: intervention group.
bTAU: treatment as usual.
cCG: control group.
dQoL: quality of life.
eFACT-G: Functional Assessment of Cancer Therapy—General.
fCOH-QOL-FCG: City of Hope Quality of Life Tool—Family Caregivers.
gFACT-L: Functional Assessment of Cancer Therapy—Lung.
hRCT: randomized controlled trial.
iPRO: patient-reported outcomes.
jEORTC QLQ-C30: European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire.
kLC: lung cancer.
lSIQOL: Single Item Quality of Life Scale.
mΨ: psychological.
nMDASI-LC: MD Anderson Symptom Inventory—Lung Cancer.
oHCP: health care professional.
pSDS: Symptom Distress Scale.
qFACT: Functional Assessment of Cancer Therapy.
rSTAI: State-Trait Anxiety Inventory.
sESAS: Edmonton Symptom Assessment System.
tSGRQ: St. George’s Respiratory Questionnaire.
uGAD-7: Generalized Anxiety Disorder Questionnaire.
vPHQ-9: Patient Health Questionnaire.
wNSCLC: non-small cell lung cancer.
xHADS: Hospital Anxiety and Depression Scales.
yZSDS: Zung Self-Rating Depression Scale.
zPHQ: Patient Health Questionnaire.
aaMBSR: mindfulness-based stress reduction.
abACT: Acceptance and Commitment Therapy.
acPROMIS: Patient-Reported Outcomes Measurement Information System.
adICMT: individual computer-based magnanimous therapy.
aeGCMT: group computer-based magnanimous therapy.
afPSSCP: Psychosomatic Status Scale for Cancer Patients.
agSE: supportive expression.
ahCBM: couple-based meditation.
aiCES-D: Center for Epidemiological Studies Depression.
ajIES: Impact of Event Scale.
akFLIC: Functional Living Index-Cancer.
alSF-36: Short Form Health Survey.
All but 1 study [] included solely patients with LC, and they ranged in terms of the stage of disease that they included: 5 (15%) included only early stage [,,,,], 8 (24%) included only advanced disease [,,,-,,], 12 (36%) included all stages [,,-,,,,,,,], and 8 (24%) studies failed to report disease stage [,,,,,,,]. Studies also varied in terms of their methodology: 17 (52%) were described as pilot or feasibility studies or were single-arm pre-post studies; 13 (39%) were full-scale RCTs; 2 (6%) used retrospective case-control methods; and 1 (3%) used a nonrandomized, quasi-experimental design (). Additionally, 28 (85%) studies focused solely on patient participants, while 5 (15%) studies also included caregivers or loved ones (although only patient-related outcomes are included in this review).
Quality AppraisalA quality rating was assigned to all 33 studies and is provided in . Overall, in terms of methodological quality, none of the studies were rated as excellent, 12 (36%) studies were rated as good [-,,-,,,], 12 (36%) studies as fair [,-,,,,-], and 9 (27%) studies as poor [,,,,-,,].
PROsMost studies (n=26; 79%) focused on QoL, specific psychological symptoms (including depressive and anxiety symptoms), or psychological distress as primary outcomes [-,,-,-,-,,]. These measures were secondary outcomes in the remaining 7 (21.21%) studies [,-,,,].
As illustrated in , QoL was measured in 25 (76%) studies. Seven different validated QoL measures were used: 11 studies used the EORTC QLQ-C30 (European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire), 8 used the Functional Assessment of Cancer Therapy Scale, 2 used the Short Form Health Survey, 1 used both the EQ-5D and the EQ-VAS, 1 used the Functional Living Index-Cancer, 1 used St. George Respiratory Questionnaire, and 1 used the single item Quality of Life scale.
In terms of specific psychological symptoms, depression was measured in 12 (36%) studies, and anxiety in 13 (39%) studies. The most frequently used measure was the Hospital Anxiety and Depression Scales (n=4) []. Four further scales were used to assess depression: 3 used the Patient Health questionnaire [], 2 used Zung Self-Rating Depression Scale [], 1 used the Patient-Reported Outcomes Measurement Information System Depression items [], 1 used the depression item of the Edmonton Symptom Assessment System (ESAS) [], and 1 used the Center for Epidemiological Studies Depression []. A further 4 validated scales measured anxiety: 3 studies used the Generalized Anxiety Disorder Questionnaire [], 2 used the Zung Self-Rating Anxiety Scale [], 1 used the Patient-Reported Outcomes Measurement Information System Anxiety items [], and 1 used the State-Trait Anxiety Inventory []. Additionally, 2 studies used the anxiety item from the ESAS []; and 1 study used nonvalidated daily measures of anxiety and mood.
Psychological distress was measured in 10 (30%) studies. A total of 5 studies used the distress thermometer [] (1 in combination with the Impact of Event Scale to assess cancer-related stress) [], 2 studies used the Symptom Distress Scale [], 1 used the ESAS well-being subscale [], and 1 used the Psychosomatic Status Scale for Cancer Patients []. An additional study used the MD Anderson Symptom Inventory [] to infer affective interference.
Intervention TypesReflecting the heterogeneous nature of eHealth programs, the reviewed studies used interventions that varied significantly from 1 another in both scope and technological basis. Of the included studies, 14 aimed to improve health literacy via education about LC symptoms, treatment-related side effects, or symptom management [,,,,,,,-,,]; 13 provided psychological or well-being support [-,-,]; 13 were used for symptom monitoring [-,,,,-]; 12 aimed to improve physical ability, often as a form of pulmonary rehabilitation [-,,,,]; and 3 provided access to medical records or laboratory results [,,]. Categories were not mutually exclusive, with many studies using eHealth to deliver multiple avenues of support simultaneously. To better understand how different intervention characteristics related to patient outcomes, results for each study have been grouped according to the interventions’ main functions ( and and ).
Table 3. Description of interventions.AuthorseHealth typeIntervention descriptionOverall durationPatient education or health literacy interventions
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