Connected device and therapeutic patient education to promote physical activity among women with localised breast cancer (DISCO trial): protocol for a multicentre 2×2 factorial randomised controlled trial

An Integrated Cancer Prevention Strategy: the Viewpoint of the Leon Berard Comprehensive Cancer Center Lyon, France

This article describes some of the key prevention services in the Leon Berard Comprehensive Cancer Center (CLB) Lyon, France, which are based on clinical prevention services, outreach activities, and collaboration with professional and territorial health communities. In addition, research is embedded at all stages of the prevention continuum, from understanding cancer causes through to the implementation of prevention interventions during and after cancer. Health promotion activities in the community and dedicated outpatient primary cancer prevention services for individuals at increased risk have been implemented. The CLB's experience illustrates how prevention can be integrated into the comprehensive mission of cancer centers, and how in turn, the cancer centers may contribute to bridging the current fragmentation between cancer care and the different components of primary, secondary, and tertiary prevention. With increasing cancer incidence, the shift toward integrated prevention-centered cancer care is not only key for improving population health, but this may also provide a response to the shortage of hospital staff and overcrowding in cancer services, as well as offer opportunities to reduce carbon emissions from cancer care.

Non-medical interventions to enhance return to work for people with cancer

BACKGROUND

People with cancer are 1.4 times more likely to be unemployed than people without a cancer diagnosis. Therefore, it is important to investigate whether programmes to enhance the return-to-work (RTW) process for people who have been diagnosed with cancer are effective. This is an update of a Cochrane review first published in 2011 and updated in 2015.

OBJECTIVES

To evaluate the effectiveness of non-medical interventions aimed at enhancing return to work (RTW) in people with cancer compared to alternative programmes including usual care or no intervention.

SEARCH METHODS

We searched CENTRAL (the Cochrane Library), MEDLINE, Embase, CINAHL, PsycINFO and three trial registers up to 18 August 2021. We also examined the reference lists of included studies and selected reviews, and contacted authors of relevant studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and cluster-RCTs on the effectiveness of psycho-educational, vocational, physical or multidisciplinary interventions enhancing RTW in people with cancer. The primary outcome was RTW measured as either RTW rate or sick leave duration measured at 12 months' follow-up. The secondary outcome was quality of life (QoL).

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed RCTs for inclusion, extracted data and rated certainty of the evidence using GRADE. We pooled study results judged to be clinically homogeneous in different comparisons reporting risk ratios (RRs) with 95% confidence intervals (CIs) for RTW and mean differences (MD) or standardised mean differences (SMD) with 95% CIs for QoL.

MAIN RESULTS

We included 15 RCTs involving 1477 people with cancer with 19 evaluations because of multiple treatment groups. In this update, we added eight new RCTs and excluded seven RCTs from the previous versions of this review that were aimed at medical interventions. All included RCTs were conducted in high-income countries, and most were aimed at people with breast cancer (nine RCTs) or prostate cancer (two RCTs). Risk of bias We judged nine RCTs at low risk of bias and six at high risk of bias. The most common type of bias was a lack of blinding (9/15 RCTs). Psycho-educational interventions We found four RCTs comparing psycho-educational interventions including patient education and patient counselling versus care as usual. Psycho-educational interventions probably result in little to no difference in RTW compared to care as usual (RR 1.09, 95% CI 0.96 to 1.24; 4 RCTs, 512 participants; moderate-certainty evidence). This means that in the intervention and control groups, approximately 625 per 1000 participants may have returned to work. The psycho-educational interventions may result in little to no difference in QoL compared to care as usual (MD 1.47, 95% CI -2.38 to 5.32; 1 RCT, 124 participants; low-certainty evidence). Vocational interventions We found one RCT comparing vocational intervention versus care as usual. The evidence was very uncertain about the effect of a vocational intervention on RTW compared to care as usual (RR 0.94, 95% CI 0.78 to 1.13; 1 RCT, 34 participants; very low-certainty evidence). The study did not report QoL. Physical interventions Four RCTs compared a physical intervention programme versus care as usual. These physical intervention programmes included walking, yoga or physical exercise. Physical interventions likely increase RTW compared to care as usual (RR 1.23, 95% CI 1.08 to 1.39; 4 RCTs, 434 participants; moderate-certainty evidence). This means that in the intervention group probably 677 to 871 per 1000 participants RTW compared to 627 per 1000 in the control group (thus, 50 to 244 participants more RTW). Physical interventions may result in little to no difference in QoL compared to care as usual (SMD -0.01, 95% CI -0.33 to 0.32; 1 RCT, 173 participants; low-certainty evidence). The SMD translates back to a 1.8-point difference (95% CI -7.54 to 3.97) on the European Organisation for Research and Treatment of Cancer Quality of life Questionnaire Core 30 (EORTC QLQ-C30). Multidisciplinary interventions Six RCTs compared multidisciplinary interventions (vocational counselling, patient education, patient counselling, physical exercises) to care as usual. Multidisciplinary interventions likely increase RTW compared to care as usual (RR 1.23, 95% CI 1.09 to 1.33; 6 RCTs, 497 participants; moderate-certainty evidence). This means that in the intervention group probably 694 to 844 per 1000 participants RTW compared to 625 per 1000 in the control group (thus, 69 to 217 participants more RTW). Multidisciplinary interventions may result in little to no difference in QoL compared to care as usual (SMD 0.07, 95% CI -0.14 to 0.28; 3 RCTs, 378 participants; low-certainty evidence). The SMD translates back to a 1.4-point difference (95% CI -2.58 to 5.36) on the EORTC QLQ-C30.

AUTHORS' CONCLUSIONS

Physical interventions (four RCTs) and multidisciplinary interventions (six RCTs) likely increase RTW of people with cancer. Psycho-educational interventions (four RCTs) probably result in little to no difference in RTW, while the evidence from vocational interventions (one RCT) is very uncertain. Psycho-educational, physical or multidisciplinary interventions may result in little to no difference in QoL. Future research on enhancing RTW in people with cancer involving multidisciplinary interventions encompassing a physical, psycho-educational and vocational component is needed, and be preferably tailored to the needs of the patient.

Analysing breast cancer survivors’ acceptance profiles for using an electronic pillbox connected to a smartphone application using Seintinelles, a French community-based research tool

Introduction: Up to 50% of breast cancer (BC) survivors discontinue their adjuvant endocrine therapy (AET) before the recommended 5 years, raising the issue of medication non-adherence. eHealth technologies have the potential to support patients to enhance their medication adherence and may offer an effective way to complement the healthcare. In order for eHealth technologies to be successfully implemented into the healthcare system, end-users need to be willing and accepting to use these eHealth technologies.

Aim: This study aims to evaluate the current usability of eHealth technologiesin and to identify differences in BC SURVIVORS BC survivors accepting a medication adherence enhancing eHealth technology to support their AET to BC survivors that do not accept such a medication adherence enhancing eHealth technology.

Methods: This study was conducted in 2020 including volunteering BC survivors belonging to the Seintinelles Association. Eligible participants were women, diagnosed with BC within the last 10 years, and been exposed to, an AET. Univariable and multivariable logistic regression analyses were performed to investigate medication adherence enhancing eHealth technology acceptance profiles among BC survivors. The dependent variable was defined as acceptance of an electronic pillbox connected to a smartphone application (hereafter: medication adherence enhancing eHealth technology).

Results: Overall, 23% of the participants already use a connected device or health application on a regular basis. The mean age of the participants was 52.7 (SD 10.4) years. In total, 67% of 1268 BC survivors who participated in the survey declared that they would accept a medication adherence enhancing eHealth technology to improve their AET. BC survivors accepting a medication adherence enhancing eHealth technology for their AET, are younger (OR = 0.97, 95% CI [0.95; 0.98]), do take medication for other diseases (OR = 0.31, 95% CI [0.13; 0.68]), already use a medication adherence enhancing eHealth technology or technique (OR = 1.74, 95% CI [1.06; 2.94]) and are willing to possess or currently possess one or more connected devices or health applications (OR = 2.89, 95% CI [2.01; 4.19]).

Conclusion: Understanding acceptance profiles of BC survivors is fundamental for conceiving an effective eHealth technology enhancing AET among BC survivors. Hence, such profiling will foster the development of personalized medication adherence enhancing eHealth technology.

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Objective

The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients.

Methods

Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored.

Results

A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis.

Conclusion

Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.