Measurement and Evaluations in Cancer Care最新文献

{"title":"A comprehensive tool for evaluating patient satisfaction in mhealth: Development and validation of the digital health application satisfaction scale","authors":"G.Hari Prakash ,&nbsp;D. Sunil Kumar ,&nbsp;PK Kiran ,&nbsp;Vanishri Arun ,&nbsp;Deepika Yadav ,&nbsp;Arun Gopi ,&nbsp;Praveen Kulkarni ,&nbsp;M. Rakesh","doi":"10.1016/j.ymecc.2025.100017","DOIUrl":"10.1016/j.ymecc.2025.100017","url":null,"abstract":"<div><h3>Aim</h3><div>Digital health applications have emerged as vital tools in healthcare delivery, particularly for older cancer patients. However, there is a lack of validated tools to assess user satisfaction with these platforms. This study aimed to develop and validate the Digital Health Application Satisfaction Scale for Patients (DHASSP) to evaluate patient satisfaction across key domains such as ease of use, quality of life impact, and emotional engagement.</div></div><div><h3>Methods</h3><div>This mixed-methods study included expert consultations, item development, content validation using 28 experts, and pilot testing with 40 oncology patients. Exploratory factor analysis (EFA) and reliability tests were performed to evaluate the scale’s psychometric properties.</div></div><div><h3>Results</h3><div>The DHASSP exhibited strong content validity (S-CVI/Ave = 0.857) and excellent reliability (Cronbach’s α = 0.907). EFA revealed a four-factor structure, accounting for 67.35 % of the variance. The Quality-of-Life domain demonstrated the highest reliability (α = 0.795), while technical aspects scored lower (α = 0.551).</div></div><div><h3>Conclusions</h3><div>The DHASSP provides a comprehensive framework for evaluating satisfaction with digital health platforms, addressing usability, emotional engagement, and impact on quality of life. Its validation contributes to advancing the use of ICT in clinical care, particularly in oncology settings.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100017"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Subjective Global Nutrition Assessment (SGNA) in predicting nutritional status among children with cancer: A cross-sectional study","authors":"Maira Razzaq ,&nbsp;Qaisar Raza ,&nbsp;Muniba Khaliq ,&nbsp;Sajid Khan Tahir ,&nbsp;Mahwish Faizan","doi":"10.1016/j.ymecc.2025.100013","DOIUrl":"10.1016/j.ymecc.2025.100013","url":null,"abstract":"<div><h3>Background</h3><div>Children diagnosed with cancer are more susceptible to malnutrition and should receive extra consideration when it comes to nutritional evaluation. It is difficult to analyze nutritional status in children undergoing cancer therapy because there isn't a single, reliable tool. Anthropometric measurements are the most widely used objective tool for evaluating malnutrition in children; but it can be easily affected by disease and treatment, reducing its accuracy for evaluating body composition. The aim of this study was to evaluate the effectiveness of Subjective Global Nutrition Assessment (SGNA) in predicting nutritional status as compared to anthropometric measurements among children with cancer in Pakistan.</div></div><div><h3>Methods</h3><div>This cross-sectional study was conducted in oncology department of hospitals in Lahore, Pakistan. SGNA along with anthropometric were conducted on children aged 2–18 years receiving oncological treatment. To evaluate agreement between SGNA and anthropometric indicators in identifying malnutrition status, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) was performed.</div></div><div><h3>Results</h3><div>In 135 children 68.1 % were identified as having moderate or severe malnutrition according to SGNA. SGNA demonstrated the best performance with MUAC/A, showing a sensitivity of 85.7 %, specificity of 47.2 %, PPV of 58.7 %, and NPV of 88.1 %. But the agreement between SGNA and objective measures was only fair.</div></div><div><h3>Conclusion</h3><div>The SGNA proved useful for assessing the nutritional status of children with cancer and was effective in monitoring the prevalence of malnutrition when compared to objective nutritional evaluation methods.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100013"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiogenomic insights from a Portuguese lung cancer cohort: Foundations for predictive modeling","authors":"Inês Neves ,&nbsp;Cláudia Freitas ,&nbsp;Carolina Lemos ,&nbsp;Hélder P. Oliveira ,&nbsp;Venceslau Hespanhol ,&nbsp;Manuela França ,&nbsp;Tania Pereira","doi":"10.1016/j.ymecc.2025.100025","DOIUrl":"10.1016/j.ymecc.2025.100025","url":null,"abstract":"<div><div>This study aimed to investigate the relationships between radiologic, clinical, and molecular characteristics in patients with lung cancer. A retrospective analysis was conducted using the Lung Cancer Mutation Database (LCMutationDB), which includes integrated clinical and computed tomography (CT) imaging data from 256 patients. The study focused on identifying associations between imaging features, clinical variables, and key oncogenic mutations (EGFR, KRAS). Significant correlations were observed between CT imaging characteristics and molecular alterations. Features such as ground-glass attenuation and pleural involvement were associated with poorer prognosis, while distinct imaging and clinical profiles corresponded to specific mutation subtypes. These findings enhance the current understanding of genotype–phenotype associations in lung cancer and underscore the value of integrating imaging, clinical, and molecular data for patient stratification. The results also provide a foundation for developing artificial intelligence–based diagnostic and prognostic models to improve early detection and personalized treatment strategies in lung cancer care.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100025"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring mobile health engagement in cancer care: Development and psychometric validation of the CmHEAQ scale","authors":"G. Hari Prakash ,&nbsp;D. Sunil Kumar ,&nbsp;PK Kiran ,&nbsp;Vanishri Arun ,&nbsp;Deepika Yadav ,&nbsp;Arun Gopi ,&nbsp;Rakesh M","doi":"10.1016/j.ymecc.2025.100022","DOIUrl":"10.1016/j.ymecc.2025.100022","url":null,"abstract":"<div><h3>Background</h3><div>Mobile health (mHealth) applications show promise in cancer care, but sustained patient engagement remains poorly understood due to a lack of validated measurement instruments. Existing tools fail to capture cancer-specific engagement dimensions and multidimensional engagement patterns.</div></div><div><h3>Methods</h3><div>We developed the Cancer Mobile Health Engagement and Adherence Questionnaire (CmHEAQ) through a systematic three-phase methodology. Phase 1 involved literature review and expert consultation, identifying six theoretical domains: Initial Adoption, Consistency, Duration, Dropout/Continuation Intent, Treatment &amp; Symptom Management, and Emotional/Support Use. Phase 2 established content validity through expert panel review (n = 10) and face validity via patient cognitive interviews (n = 10), yielding 24 items across six domains. Phase 3 included pilot testing (n = 46) and confirmatory validation (n = 218) in cancer patients using mHealth applications at a tertiary oncology centre in Mysore, India. Psychometric evaluation employed reliability analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA).</div></div><div><h3>Results</h3><div>The CmHEAQ demonstrated excellent psychometric properties. Content validity was exceptional (Scale-Content Validity Index=0.94). Internal consistency reliability was excellent in both pilot (Cronbach's α=0.970) and validation samples (α=0.973), with domain-specific reliability ranging from 0.782 to 0.898. EFA revealed six factors explaining 69.09 % variance (Kaiser-Meyer-Olkin=0.91); however, empirical analysis revealed that Initial Adoption and Consistency items loaded together on Factor 1. Confirmatory factor analysis of both the theoretical six-factor and empirically-refined five-factor models showed that the five-factor model (combining Initial Adoption and Consistency into \"Engagement Initiation &amp; Maintenance\") demonstrated superior fit indices (CFI = 0.903, TLI = 0.889, RMSEA = 0.066 [90 % CI: 0.059–0.073]) compared to the six-factor model (CFI = 0.088, RMSEA = 0.200). All factor loadings ranged from 0.67 to 0.81 (mean = 0.72), demonstrating strong convergent validity. The five-factor structure identified three engagement levels: high engagement (61.5 %), moderate engagement (32.6 %), and low engagement (6.0 %).</div></div><div><h3>Conclusions</h3><div>The CmHEAQ represents the first validated, comprehensive instrument specifically designed to assess multidimensional mHealth engagement in cancer populations across five empirically-derived domains: Engagement Initiation &amp; Maintenance, Duration, Dropout/Continuation Intent, Treatment &amp; Symptom Management, and Emotional/Support Use. The scale enables standardised measurement for research and clinical practice.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100022"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning models for predicting surgical intervention in colorectal cancer","authors":"Felipe Mendes Delpino,&nbsp;Francisco Tustumi,&nbsp;Marina Martins Siqueira,&nbsp;Gabriely Rangel Pereira,&nbsp;Marcelo Passos Teivelis,&nbsp;Vanessa Damazio Teich,&nbsp;Sergio Eduardo Alonso Araujo,&nbsp;Lucas Hernandes Corrêa,&nbsp;Nelson Wolosker","doi":"10.1016/j.ymecc.2025.100018","DOIUrl":"10.1016/j.ymecc.2025.100018","url":null,"abstract":"<div><h3>Aim</h3><div>We aimed to develop and validate a machine learning (ML) model to predict surgical intervention in colorectal cancer (CRC) patients in the state of São Paulo, Brazil, using clinical and sociodemographic data as predictors.</div></div><div><h3>Methods</h3><div>We conducted a longitudinal analysis using data from the <em>Fundação Oncocentro de São Paulo</em> (FOSP) database, which included CRC cases diagnosed between 2000 and 2023. We defined the primary outcome as surgical intervention and analyzed 29 predictor variables, including clinical, demographic, and socioeconomic factors. We evaluated six ML algorithms (Random Forest, Gradient Boosting, LightGBM, CatBoost, Logistic Regression, and Decision Trees). Data was divided into training (70 %) and test (30 %) sets and preprocessing steps were applied, including normalization, one-hot encoding, and addressing class imbalance. We assessed model performance using AUC-ROC, accuracy, precision, recall, F1-score, and specificity. SHAP was used to interpret variable importance.</div></div><div><h3>Results</h3><div>The dataset comprised 72,038 participants, 17,852 in the group that did not undergo surgery and 54,186 in the group that did. The Random Forest model achieved the highest performance, with an AUC of 0.94, accuracy of 0.82, and F1-score of 0.87. Key predictors included treatment-related factors (e.g., time between diagnosis and treatment), tumor stage, age, and socioeconomic indicators (e.g., municipal human development index). Geographic accessibility, such as travel time to healthcare facilities, also significantly influenced predictions.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of ML models, particularly Random Forest, to predict surgical necessity in CRC patients by integrating clinical and sociodemographic data.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100018"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing needs at the workplace: The development of a questionnaire for oncology professionals","authors":"Francesca Chiesi ,&nbsp;Georgia Marunic ,&nbsp;Aldo Chioni ,&nbsp;Lucia Caligiani ,&nbsp;Laura Belloni ,&nbsp;Monica Giuli ,&nbsp;Guido Miccinesi ,&nbsp;Andrea Bonacchi","doi":"10.1016/j.ymecc.2024.100010","DOIUrl":"10.1016/j.ymecc.2024.100010","url":null,"abstract":"<div><h3>Background and aim</h3><div>The unmet needs of patients with cancer have been largely documented, but there is a lack of investigations into the needs of people who care for them. The current study aimed to fill this gap by developing a valid, reliable, easy-to-use questionnaire to assess the work-related needs of oncology professionals.</div></div><div><h3>Methods</h3><div>The instrument was developed following several phases. Different groups of experts worked to identify the relevant needs and to formulate a provisional pool of items and the relative response mode. Content validity was tested, and some adjustments were made excluding some items and changing the response mode. The psychometric properties of the resulting questionnaire were analyzed collecting data on a large sample of oncology professionals (N= 380; 80% females; age: M = 48.25, SD = 10.64 [range: 23-77], 35.8% physicians, 42.6% nurses, 8.4% socio-health workers, 7.9% medical diagnostic technicians, 5.3% psychologists).</div></div><div><h3>Results</h3><div>During different steps of exploratory factor analyses, several items were removed, and four factors emerged. Exploratory graph analysis confirmed the presence of four clusters consisting of the same items. Construct and criterion validity were tested founding evidence of relationships with resilience, job satisfaction, psychological, relational and general well-being, work-related burnout, depression, and stress. Incremental validity was also proved.</div></div><div><h3>Conclusion</h3><div>The present study provides a reliable and valid questionnaire to evaluate the needs of oncology professionals. Surveying these needs could be done to prevent psycho-physical discomfort, promote well-being, and, eventually, improve the quality of healthcare service delivery.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychometric evaluation of the Chinese version of Templer’s death anxiety scale using item response theory","authors":"Furong Chen ,&nbsp;Yiguo Deng ,&nbsp;Siyu Li ,&nbsp;Qihan Zhang ,&nbsp;M. Tish Knobf ,&nbsp;Zengjie Ye","doi":"10.1016/j.ymecc.2024.100012","DOIUrl":"10.1016/j.ymecc.2024.100012","url":null,"abstract":"<div><h3>Objective</h3><div>Recently, the importance of assessing death anxiety (DA) has gained increasing recognition. The Chinese version of Templer's Death Anxiety Scale (C-T-DAS) is one of the most commonly used tools to evaluate death anxiety in cancer patients. This study is the first to examine the C-T-DAS in cancer patients using both non-parametric and parametric item response theory (IRT) methods.</div></div><div><h3>Methods</h3><div>This study included cancer patients from the \"Be Resilient to Cancer\" project in Guangdong, China, who completed the C-T-DAS after recruitment. The data collected were then randomly divided into Dataset 1 and Dataset 2 at a 1:1 ratio. Unidimensionality, monotonicity and local independence was estimated by non-parameter IRT of Mokken scale analysis (MSA) in Dataset 1. Parameter Item Response Theory (IRT) was performed in Dataset 2. Differential Item Functioning (DIF) analysis was used to compare the gender differences in all samples.</div></div><div><h3>Results</h3><div>A total of 462 patients participated in the study. Through MSA, three items were removed, and two factors, \"Fear of Death\" and \"Acceptance of Death,\" were retained. IRT analysis showed strong discrimination, moderate difficulty, and low guessing probabilities for the items. Additionally, DIF analysis revealed non-uniform gender differences in one item from the \"Fear of Death\" dimension.</div></div><div><h3>Conclusion</h3><div>The revised C-T-DAS exhibits robust validity and reliability for evaluating death anxiety in Chinese cancer patients.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100012"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Educational differences in anxiety–depression symptom networks among Chinese women with breast cancer: A network comparison with simulation-guided intervention targets","authors":"Ying Xiong ,&nbsp;Hongman Li ,&nbsp;Miao Yu ,&nbsp;Jiaying Li ,&nbsp;Zengjie Ye","doi":"10.1016/j.ymecc.2025.100021","DOIUrl":"10.1016/j.ymecc.2025.100021","url":null,"abstract":"<div><h3>Objectives</h3><div>To examine whether anxiety-depression symptom networks differ by education levels among Chinese women with breast cancer and to identify subgroup-specific intervention targets.</div></div><div><h3>Methods</h3><div>Using cross-sectional data from 414 patients with breast cancer, we estimated Gaussian graphical models (GGMs) separately for lower- and higher-education groups. We evaluated central and bridge symptoms, compared networks using permutation-based Network Comparison Tests, and conducted simulation-based intervention analyses to identify symptoms whose hypothetical improvement would most reduce overall symptoms.</div></div><div><h3>Results</h3><div><strong>“</strong>Cheerful” (H6) was the most central symptom in both education groups. Bridge symptoms diverged: the lower-education network was bridged primarily by “worried” (H5), whereas in the higher-education network “cheerful” (H6) served a dual core-bridge role. Global network strength did not differ significantly between groups, yet 11 individual edges did, including 8 cross-construct (anxiety-depression) connections. Simulation analyses suggested different leverage points by education: improving “optimistic” (H12) produced the largest downstream symptom reductions in the lower-education group, while targeting “frightened” (H3) was most effective in the higher-education group.</div></div><div><h3>Conclusions</h3><div>Although core anxiety-depression architecture is similar across education levels, bridge structure and optimal intervention targets differ. Tailoring psychosocial care by educational backgrounds, and emphasizing optimism-building in lower-education patients and fear-reduction in higher-education patients, may enhance treatment efficacy.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100021"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of health care professionals on the value of physical examinations for early detection of breast cancer recurrences: More than just a detection method?","authors":"A. Dekker-Klaassen ,&nbsp;L.T.H. Godding ,&nbsp;M. van Hezewijk ,&nbsp;J.C. Korevaar ,&nbsp;J. Wiegersma ,&nbsp;C.H.C. Drossaert ,&nbsp;S. Siesling ,&nbsp;on behalf of the NABOR project group","doi":"10.1016/j.ymecc.2025.100016","DOIUrl":"10.1016/j.ymecc.2025.100016","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Physical examinations (PE) detect relatively few recurrences after breast cancer treatment, but are still recommended in surveillance guidelines. This study explores Health Care Professionals (HCPs) perspectives on the frequency and value of PE.</div></div><div><h3>Methods</h3><div>Semi-structured interviews were conducted in 11 Dutch hospitals with 22 HCPs involved in breast cancer follow-up. Interviews were coded by the framework methodology using the software Atlas.ti 23.</div></div><div><h3>Results</h3><div>Most HCPs occasionally deviated from the guideline, giving some patients more and others less PE than recommended. The majority attributed rather limited value to PE for detecting recurrences and all performed PE more often than they perceived valuable. More PE was performed to meet patients’ wishes, to evaluate treatment effects or to detect signals of recurrences which imaging cannot assess. Also the organization of consultations determined PE’s frequency. Performing less PE was mainly because of PE’s low detection rates of recurrences.</div></div><div><h3>Conclusions</h3><div>HCPs seem aware of the low detection rates but still perform more PE than perceived as valuable to detect recurrences because of reasons other than detecting recurrences. Better information provision on PE’s limited value in detecting recurrences may prevent unrealistic expectations and unnecessary PE. Further evidence on the cost-effectiveness of PE is warranted to revise guidelines.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100016"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mediation analysis of the relationship between fatigue, sleep disturbance, and quality of life among cancer patients","authors":"Mohammed Al Maqbali ,&nbsp;Rafiq Hijazi ,&nbsp;Mohammed Al Sinani","doi":"10.1016/j.ymecc.2025.100024","DOIUrl":"10.1016/j.ymecc.2025.100024","url":null,"abstract":"<div><h3>Background</h3><div>Cancer-related fatigue (CRF) and sleep disturbance are common and debilitating symptoms among cancer patients, often leading to reduced quality of life (QoL). This study investigated the interrelationships among fatigue, sleep disturbance, and QoL in cancer patients, and examined whether sleep disturbance mediates the relationship between fatigue and QoL.</div></div><div><h3>Methods</h3><div>A cross-sectional study was conducted involving 369 adult cancer patients attending the National Oncology Centre in Oman. Participants completed validated Arabic versions of the Functional Assessment of Cancer Therapy–Fatigue (FACIT-Fatigue), the Pittsburgh Sleep Quality Index (PSQI), and the Functional Assessment of Cancer Therapy–General (FACT-G). Descriptive statistics, Spearman’s correlation coefficients, and Structural Equation Modeling (SEM) with bootstrapping were used for data analysis.</div></div><div><h3>Results</h3><div>The mean global PSQI score was 9.2 (SD = 4.2), indicating poor sleep quality. The average fatigue score was 22.7 (SD = 13.0), and the mean overall QoL score was 69.0 (SD = 18.5). Fatigue and sleep disturbance were significantly negatively correlated with all QoL domains. SEM analysis demonstrated a significant direct effect of fatigue on QoL (Estimate = 0.312, p &lt; 0.001) and an indirect effect mediated by sleep disturbance (Estimate = 0.085). Sleep disturbance also had a strong negative effect on QoL (Estimate = –4.689, p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>These findings highlight the need for integrated, symptom-focused care strategies to manage fatigue and sleep disturbance and improve QoL in cancer patients. Implementing early screening and targeted interventions may enhance clinical outcomes and promote long-term patient well-being.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}