Measurement and Evaluations in Cancer Care最新文献

{"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":"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":"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}

{"title":"Clinical data-driven modeling of disease-specific survival in lung cancer: Insights from the national lung screening trial dataset","authors":"Maria Amaro ,&nbsp;Joana Vale Sousa ,&nbsp;Margarida Gouveia ,&nbsp;Hélder P. Oliveira ,&nbsp;Tania Pereira","doi":"10.1016/j.ymecc.2025.100023","DOIUrl":"10.1016/j.ymecc.2025.100023","url":null,"abstract":"<div><h3>Purpose</h3><div>Lung cancer remains the leading cause of cancer-related mortality worldwide, highlighting the importance of accurate disease-specific survival (DSS) prediction to support clinical decision making. DSS, which isolates outcomes directly attributable to lung cancer, provides a precise and clinically relevant endpoint for prognostic evaluation and treatment planning.</div></div><div><h3>Methods</h3><div>In this study, we developed and evaluated a clinical modeling framework for DSS prediction using data from the National Lung Screening Trial (NLST), incorporating detailed clinical information from 2058 patients. Clinical features were categorized into three temporal sets: pre-diagnosis, diagnosis-informed, and full clinical information, and subjected to rigorous preprocessing, including LASSO-based feature selection and Variance Inflation Factor filtering to reduce redundancy and multicollinearity. Machine learning models, including Random Forest, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, were trained using these features and were evaluated for predictive performance.</div></div><div><h3>Results</h3><div>Models trained on full clinical information achieved high discriminative performance (AUC = 0.919), while diagnosis-informed models still demonstrated strong predictive capability (AUC = 0.873). Explainability analysis consistently identifies the TNM (tumor, node, metastasis) staging as the most predictive variable in all clinical models. Kaplan–Meier survival analysis further confirmed that clinical models effectively stratify patients into statistically distinct survival groups.</div></div><div><h3>Conclusions</h3><div>These findings highlight the dominant role of clinical data in lung cancer DSS prediction and demonstrate the potential of clinically grounded, interpretable models to inform personalized prognosis and treatment planning.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100023"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692991","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":"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":"2025-10-14","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":"Development and psychometric testing of a personal health-related resources instrument for working-age women with breast cancer","authors":"Marika Skyttä ,&nbsp;Lauri Sillanmäki ,&nbsp;Minna Stolt ,&nbsp;Mari Kangasniemi","doi":"10.1016/j.ymecc.2025.100020","DOIUrl":"10.1016/j.ymecc.2025.100020","url":null,"abstract":"<div><h3>Background</h3><div>Personal health-related resources are essential for women with breast cancer, as these help them to maintain their own health and well-being during cancer care. Nurses play an important role in providing support to identify personal health-related resources of women during cancer treatments.</div></div><div><h3>Purpose</h3><div>To develop and test the psychometric properties of a new self-assessment personal health-related resources (PHRR) instrument for working-age women receiving breast cancer care.</div></div><div><h3>Methods</h3><div>A four-phase instrument development process was used. The data were analysed using the content validity index, content validity ratio and exploratory factor analysis. Reliability was tested using Cronbach's α coefficients.</div></div><div><h3>Results</h3><div>The instrument’s content validity index was 0.93. Exploratory factor analysis showed that the instrument version 3.0 comprised three main factors, 12 sub-factors and 46 items, which explained 67.4 % of the total variance in the measured variable. The instrument’s internal consistency was high, with Cronbach’s α of 0.92.</div></div><div><h3>Conclusion</h3><div>The instrument showed acceptable psychometric properties and was suitable for measuring PHRR in women aged 18–65 years with breast cancer. The instrument provides multidimensional understanding of the PHRR of women with breast cancer. The PHRR instrument can be used to achieve a comprehensive understanding of the personal health-related resources for women with breast cancer during cancer care. This instrument can provide information how to support women and development psychosocial support in different phases of illness.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100020"},"PeriodicalIF":0.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227105","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":"Reliability, validity, and clinical use of the newly developed Research and Clinical Assessment Tool-Fatigue (ReACT-F)","authors":"Kristin Dickinson ,&nbsp;Andrew Lim ,&nbsp;Bunny Pozehl ,&nbsp;Debra Lynch Kelly ,&nbsp;Kevin Kupzyk","doi":"10.1016/j.ymecc.2025.100019","DOIUrl":"10.1016/j.ymecc.2025.100019","url":null,"abstract":"<div><h3>Background</h3><div>Cancer-related fatigue (CRF) remains one of the most common and debilitating symptoms reported by individuals with cancer. Successful management of CRF has been substantially hindered by the lack of efficient and comprehensive tools to assess its multidimensional nature in clinical settings. The Research and Clinical Assessment Tool-Fatigue (ReACT-F) was created to address this need. The purpose of the current study was to document the reliability, validity, and clinical use of our newly developed ReACT-F questionnaire for use in oncology clinical settings.</div></div><div><h3>Methods</h3><div>Adults receiving treatment for cancer were enrolled between February 2019 and August 2022. Two study visits were conducted, during which participants completed three self-report CRF questionnaires (MFSI-SF, MFI-20, and the ReACT-F). Reliability and validity were examined. The clinical use of the ReACT-F questionnaire was evaluated by clinicians using a Likert scale.</div></div><div><h3>Results</h3><div>The ReACT-F demonstrated acceptable internal consistency (.92 reliability coefficient) and test-retest (<em>r</em> = .60 −.67, <em>p</em> &lt; .001) reliability. The ReACT-F demonstrated acceptable validity when compared with the two well established and validated measures, with all correlations significant (p &lt; .001) and nearly all were at least moderate (r &gt; .50). In terms of clinical use, all clinicians rated the ReACT-F as valuable for assessment of CRF, and nearly all would use the tool in practice.</div></div><div><h3>Conclusions</h3><div>The ReACT-F is both a reliable and valid tool for assessment of multidimensional CRF in adults receiving cancer-related treatment. Data from the ReACT-F questionnaire may guide clinicians to focused assessments and effective personalized management strategies targeting specific fatigue dimensions.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893106","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":"2025-07-12","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":"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":"2025-07-11","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":"Psychometric assessment of the Traditional Chinese Version of patient reported outcome measurement for breast surgery (BREAST-Q) – Reconstruction module","authors":"Ting-Yu Chang ,&nbsp;Tongyao Wang ,&nbsp;Chia-Chin Lin","doi":"10.1016/j.ymecc.2025.100015","DOIUrl":"10.1016/j.ymecc.2025.100015","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Patient-reported outcomes, as important indicators of quality of life, was utilized in a widely used breast surgery measurement (BREAST-Q) for assessing patient's physical, psychosocial wellbeing and satisfaction. However, the lack of a Traditional Chinese version has limited its use in patient management in Taiwan. The study aims to psychometrically evaluate the Traditional Chinese BREAST-Q reconstruction module for Taiwanese patients undergoing breast cancer related reconstruction.</div></div><div><h3>Methods</h3><div>Forward and backward translations of the BREAST-Q was followed by expert reviews and pilot testing. Patients undergoing silicon breast reconstruction surgery were recruited from the inpatient and outpatient clinic at a comprehensive medical center. 155 and 96 participants completed the BREAST-Q and the Functional Assessment of Cancer Therapy–Breast (FACT-B) before and after surgery, respectively. Psychometric properties were analyzed with internal consistency, test-retest reliability, content, construct and criterion validity.</div></div><div><h3>Results</h3><div>The translated BREAST-Q reconstruction module had Cronbach's alpha values of 0.87 and 0.92 before and after surgery, respectively. The Pearson r values between the baseline and the two-weeks retest were.85 and.73, indicating high test-retest reliability. Expert validity measured by the Content Validity Index were 0.96 and 0.98. Concurrent validity measured by the Pearson correlation coefficients between the BREAST-Q and the FACT-B were.26 and.38, indicating good criterion-related validity.</div></div><div><h3>Conclusions</h3><div>This study confirms reliability and clinical validity of the Traditional Chinese BREAST-Q reconstruction module in Taiwan for measuring the satisfaction with breasts, overall outcome, process of care, and psychosocial, physical and sexual well-being before and after patients’ breast reconstruction surgery.</div></div>","PeriodicalId":100896,"journal":{"name":"Measurement and Evaluations in Cancer Care","volume":"3 ","pages":"Article 100015"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636882","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}