Avoidant coping as mediator of the relationship between rumination and mental health among family caregivers of Chinese breast cancer patients

2021 ◽  
Author(s):  
Xing Tan ◽  
Yuanyuan An ◽  
Chen Chen
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Cancer Patients ◽  
Family Caregivers ◽  
Avoidant Coping ◽  
Breast Cancer Patients ◽  
The Relationship

Understanding the Relationship between Mood Symptoms and Mobile App Engagement Among Breast Cancer Patients: A Machine Learning Process (Preprint)

2021 ◽  
Author(s):  
Anna N Baglione ◽  
Lihua Cai ◽  
Aram Bahrini ◽  
Isabella Posey ◽  
Mehdi Boukhechba ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Random Forest ◽  
Cancer Patients ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Self Report ◽  
Breast Cancer Patients ◽  
The Relationship

BACKGROUND Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success, yet the relationship between mood and engagement among cancer patients remains poorly understood. One reason is the lack of a data-driven process for analyzing mood and app engagement data for cancer patients. OBJECTIVE The purpose of this study is to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in breast cancer patients. We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. METHODS We describe the steps of data preprocessing, feature extraction, and data modeling and prediction. We then apply this process as a case study to data collected from breast cancer patients who engaged with a mobile mental health app intervention (IntelliCare) over 7-weeks. We compare engagement patterns over time (e.g., frequency, days of use) between high- and low-anxious and high- and low-depressed participants. We then use a Linear Mixed Model to identify significant effects and evaluate the performance of Random Forest and XGBoost classifiers in predicting weekly state mood from baseline affect and engagement features. RESULTS We observed differences in engagement patterns between high- and low-anxious and depressed participants. Linear Mixed Model results varied by the featureset; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. Accuracy of predicting state mood varied according to classifier and featureset. The XGBoost classifier achieved the highest accuracy for state anxiety prediction when self-report scores and engagement features were used for only the most highly-used apps. The Random Forest classifier achieved the highest accuracy for state depression prediction when self-report scores and engagement features were used from all apps. CONCLUSIONS The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in breast cancer patients. The ability to leverage both self-report and engagement features to predict state mood during an intervention could be used to enhance decision-making for researchers and clinicians, as well as assist in developing more personalized interventions for breast cancer patients.

The relationship of self-acceptance with decision making duration in cancer patient to perform mastectomy

2021 ◽  
pp. 1-5
Author(s):  
Ayu Ratuati Setiawan ◽  
Feny Tunjungsari ◽  
Mochamad Aleq Sander
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Cross Sectional ◽  
Abnormal Growth ◽  
Self Acceptance ◽  
Observation Method ◽  
Relationship Of ◽  
The Relationship

BACKGROUND: Cancer is a disease caused by abnormal growth of body cells that turn malignant and continue to grow uncontrollably. One of the treatments for breast cancer is mastectomy. The quickness of decision-making determines the survival rate of prognosis patients. OBJECTIVE: This study aimed to determine the relationship of self-acceptance with decision-making duration in cancer patients to perform a mastectomy. METHODS: An analytic observation method with cross-sectional design. The samples were taken by purposive sampling method with 50 samples of breast cancer patients. Data collected include age, last level of education, marital status, profession, stage of cancer during mastectomy, self-acceptance score, and decision-making duration to perform a mastectomy. RESULTS: The data analyzed with the Kruskal–Wallis test. The test showed the relationship of self-acceptance (p = 0.027) with decision-making duration in breast cancer patients to perform a mastectomy. CONCLUSION: In Conclusion, there is a relationship of self-acceptance with decision-making duration in breast cancer patients to perform a mastectomy.

The relationship of plasma 7α-hydroxy dehydroepiandrosterone to disease stage and adrenal androgens in breast cancer patients

1980 ◽  
Vol 16 (2) ◽  
pp. 223-228 ◽  
Author(s):  
S.J.M. Skinner ◽  
R.A.F. Couch ◽  
S. Thambyah ◽  
R.J. Dobbs ◽  
S.M. Jordan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Disease Stage ◽  
Breast Cancer Patients ◽  
Adrenal Androgens ◽  
Relationship Of ◽  
The Relationship

scholarly journals Assessment of Breast Cancer Risk Based on Mammary Gland Volume Measured with CT

2010 ◽  
Vol 4 ◽  
pp. BCBCR.S5248 ◽  
Author(s):  
Megumi Kuchiki ◽  
Takaaki Hosoya ◽  
Akira Fukao
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Patients ◽  
Odds Ratio ◽  
Premenopausal Women ◽  
Breast Cancer Patients ◽  
History Of ◽  
The Relationship

We investigated the relationship between mammary gland volume (MGV) of the breast as measured with three-dimensional chest computed tomography (CT) and breast cancer risk. Univariate analysis was used to assess the relationship between MGV and known risk factors in 427 healthy women. A case control study (97 cases and 194 controls) was conducted to assess breast cancer risk. MGV was significantly smaller for postmenopausal women than for premenopausal women, and was significantly larger for women with a family history of breast cancer than for women without. MGV, body mass index (BMI), and rate of family history of breast cancer were significantly higher among breast cancer patients than among healthy women, and number of deliveries was significantly lower among breast cancer patients. In postmenopausal women, age at menarche was significantly younger for breast cancer patients. MGV correlated well with breast cancer risk factors. The highest odds ratio was 4.9 for premenopausal women with the largest MGV. Regardless of menopausal status, the greater the MGV, the higher the odds ratio. Our results constitute the first reliable data on the relationship between MGV and breast cancer obtained through exact volume analysis.

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