Prevalence and Associated Factors of Body Dissatisfaction in Patients With Head and Neck Cancer: A Systematic Review...

Body dissatisfaction represents a critical yet underprioritized quality-of-life determinant for head and neck cancer survivors. However, existing evidence remains fragmented and contradictory, necessitating methodologically robust systematic assessments to address these uncertainties.

This systematic review and meta-analysis aimed to quantify the pooled prevalence of body dissatisfaction and synthesize its associated factors, informing evidence-based psychosocial interventions.

Comprehensive searches were performed in PubMed, Embase, Web of Science, the Cochrane Library, CINAHL, PsycINFO, Sinomed, and CNKI from their inception to July 2025. Two researchers independently screened studies, extracted data, and assessed quality. Quantitative synthesis using Review Manager 5.3 software was applied for eligible data, with qualitative synthesis otherwise.

Analysis of 25 studies involving 4662 HNC patients revealed a pooled body dissatisfaction prevalence of 38.00% (95% confidence interval: 0.27-0.49). Key influencing factors identified in meta-analysis ( P <.05) included age, employment status, anxiety, depression, positive coping, and social support. Qualitative synthesis further categorized 36 significant factors into sociodemographic, disease/treatment-related, psychosocial, and health behavior factors.

Body dissatisfaction demonstrates a high prevalence in patients with head and neck cancer with numerous associated factors, necessitating early screening and multidimensional intervention strategies to mitigate long-term quality-of-life impairments.

Future research should prioritize prospective multicenter studies using standardized eligibility criteria and refined patient stratification to reduce methodological heterogeneity. Concurrently, developing predictive models integrating clinical data with patient-reported outcome measures and integrating digital monitoring into oncology nursing practice are essential for identifying high-risk individuals and enabling personalized interventions.