Using eHealth to Support Quality of Life and Well-Being in Patients With Lung Cancer: Systematic Review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».