Using eHealth to Support Quality of Life and Well-Being in Patients With Lung Cancer: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide and has a substantial impact on patients' quality of life (QoL) and psychological well-being, due to complex physical, emotional, and social challenges. Addressing these needs is critical; yet, many patients go unsupported. eHealth (using information and communication technology to deliver health-related services) offers a scalable way to provide timely, personalized care for people living with LC. OBJECTIVE: This review aimed to evaluate the impact of eHealth interventions on QoL and psychological well-being in patients with LC, and characterize the different strategies used. METHODS: A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Literature searches across 6 databases (PubMed, PsycINFO, MEDLINE, Scopus, Web of Science, and CINAHL) were performed between December 2023 and February 2024. Eligible studies included experimental and quantitative designs involving adults (≥18 years) diagnosed with LC. Interventions were required at least 1 eHealth component, and studies had to report outcomes on QoL or psychological well-being. Data extraction focused on study characteristics, intervention details, outcomes, engagement and acceptability metrics. Study quality was assessed using a modified Downs and Black checklist, and a synthesis without meta-analysis was conducted due to study heterogeneity. RESULTS: A total of 7065 records were screened, with 33 studies meeting inclusion criteria; of these, 30 were suitable for quantitative synthesis, comprising 2654 individual participants and 231 patient-caregiver dyads. eHealth strategies included: patient education (n=2), digital symptom monitoring (n=6), physical activity programs (n=8), psychological support (n=5), nurse-led interventions (n=5), and multicomponent portals or platforms (n=7). For QoL, the most consistent benefits were observed in multicomponent (5/5) and nurse-led (3/3) interventions, followed by physical activity (4/6) and symptom monitoring (4/6) approaches. For psychological well-being, multicomponent (4/4), nurse-led (2/2), and physical activity (6/6) interventions all demonstrated consistent positive effects. Psychological interventions showed mixed effects overall, although mindfulness-based programs (2/2) consistently reduced psychological symptoms. Key factors linked to positive outcomes included personalization, delivery via apps or web-based platforms, longer intervention duration, and clinician involvement. User acceptability was generally high, and engagement was variable, although both were rarely measured. CONCLUSIONS: eHealth interventions can have a positive effect on QoL and psychological well-being for people with LC. Multifaceted programs addressing diverse patient needs were found to be particularly effective. However, variation in study quality, small sample sizes, and inconsistent measurement of engagement and acceptability limit the strength of conclusions. Thus, while eHealth solutions have the potential to address significant gaps in LC care and improve patient outcomes, further research is needed. To realize their full potential, future research should prioritize developing and evaluating tailored, scalable eHealth solutions with robust designs, standardized outcomes, and strategies to enhance patient engagement and implementation in routine care. TRIAL REGISTRATION: PROSPERO CRD42024509607; https://tinyurl.com/ycst2r8k.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".