Background A strategy for maintaining and/or improving cardiorespiratory fitness (CRF) in the growing population of cancer survivors is of major clinical importance, especially in the COVID-19 era. The effect of unsupervised high-intensity interval training (HIIT) on increasing CRF in breast cancer survivors is unknown. Purpose The purpose of this study was to determine whether the newly developed habit-B programme, which involves home-based smartphone-supported HIIT using body weight exercises, improves CRF in early-stage breast cancer survivors. Methods This single-centre, 12-week, parallel-group, single-blind, randomised controlled trial involved 50 women with stage I–IIa breast cancer, aged 20–59 years, who had completed initial treatment except for hormone therapy. Participants were randomised to either the exercise or control group. The primary outcome was the 12-week change in peak oxygen uptake . Other outcomes included muscle strength, 6 min walk test, resting heart rate, physical activity, fatigue, safety and quality of life. Results The change in and leg strength increased significantly in the exercise group compared with the control group (p<0.01). Changes in other outcomes were not significantly different between the groups. Conclusion A home-based HIIT intervention can lead to improve CRF and muscle strength in early-stage breast cancer survivors.

The aim of this study was to clarify the relationship of nursing autonomy and other factors related to attitudes toward caring for dying patients. A cross-sectional survey of nurses was conducted in November 2003 using a self-administered questionnaire. We collected demographic data from 178 (75%) participants and used the Frommelt Attitude Toward Care of the Dying scale, Form B, Japanese version (FATCOD-Form B-J), the Pankratz Nursing Questionnaire (PNQ), and the Death Attitude Inventory (DAI). FATCOD-Form B-J measures nurse's attitude toward caring for dying patients. It includes two subscales: positive attitude toward caring for the dying patient and perception of patient- and family-centered care. The PNQ measures nursing autonomy of individual nurses and has three subscales: nursing autonomy and advocacy, patients' rights, and rejection of traditional role limitations. The DAI measures attitudes toward death in context of Japanese cultural characteristics. It includes seven subscales: afterlife beliefs, death anxiety, death relief, death avoidance, life purpose, death concern, and supernatural beliefs. We investigated the factors associated with the FATCOD-Form B-J. Support of a mentor regarding end-of-life issues (β = .19, P = .001), death avoidance domain of the DAI (β = —.14, P = 0.03), life purpose domain of the DAI (β = .23, P = .001), and rejection of traditional role limitations domain of the PNQ (β = .51, P = .001) were selected as significant independent variables by multivariate analysis to evaluate nurses' positive attitudes toward caring for dying patients. Death anxiety domain of the DAI (β = —.17, P = .02), patients' rights domain of the PNQ (β =.46, P = .001), and rejection of traditional role limitations domain of the PNQ (β = .34, P = .001) were selected as significant independent variables by multivariate analysis to evaluate the nurses' perception of patient-and family-centered care. In conclusion, nursing autonomy plays an important role in the attitudes of Japanese nurses who care for dying patients. Educational and administrative efforts to strengthen nursing autonomy are necessary.

Purpose: To identify nursing support for caregiver burden in family caregivers of patients with cancer. Methods: This scoping review was guided by Arksey and O’Malley’s six-stage scoping review framework. All available published articles from database inception to July 31, 2023 were systematically searched through PubMed, CINAHL, CENTRAL, and Ichushi-Web of the Japan Medical Abstract Society databases with additional relevant studies from the article list. Each key journal was manually searched. Results: Overall, 502 articles were screened, and 34 were finally included. The results of the qualitative thematic analysis were categorized into 7 components of nursing support: psychological and educational support, psychological and educational support using mainly non-face-to-face (Information and Communication Technology), psychological and educational support mainly using non-face-to-face (telephone) methods, mindfulness to support, support aimed at reducing caregiver stress, support for both patients and caregivers, and others. Of the 34 studies, 23 were randomized controlled trials (RCT), and the remaining 11 were non-RCTs. Conclusion: The results of the scoping review categorized nursing support for caregiver burden in the family caregivers of patients with cancer into 7 components. Future research should examine the feasibility of implementing these components.

Objective: Insomnia is an increasingly recognized major symptom of breast cancer which can seriously disrupt the quality of life during and many years after treatment. Sleep problems have also been linked with survival in women with breast cancer. The aims of this study were to estimate the prevalence of insomnia in breast cancers survivors, clarify the clinical characteristics of their sleep difficulties and use machine learning techniques to explore clinical insights.Methods: Our analysis of data, obtained in a nationwide questionnaire survey of breast cancer survivors in Japan, revealed a prevalence of suspected insomnia of 37.5%. With the clinical data obtained, we then used machine learning algorithms to develop a classifier that predicts comorbid insomnia. The performance of the prediction model was evaluated using 8-fold cross-validation.Results: When using optimal hyperparameters, the L2 penalized logistic regression model and the XGBoost model provided predictive accuracy of 71.5 and 70.6% for the presence of suspected insomnia, with areas under the curve of 0.76 and 0.75, respectively. Population segments with high risk of insomnia were also extracted using the RuleFit algorithm. We found that cancer-related fatigue is a predictor of insomnia in breast cancer survivors.Conclusions: The high prevalence of sleep problems and its link with mortality warrants routine screening. Our novel predictive model using a machine learning approach offers clinically important insights for the early detection of comorbid insomnia and intervention in breast cancer survivors.

Literature review of guidelines for cancer survivorship indicated the necessity for guidelines on optimal care coordination of oncologists with other health care providers and promotion of a healthy lifestyle.