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Heart rate variability biofeedback (HRVB) has been used for a number of years to treat depressive symptoms, a common mental health issue, which is often comorbid with other psychopathological and medical conditions. The aim of the present meta-analysis is to test whether and to what extent HRVB is effective in reducing depressive symptoms in adult patients. We conducted a literature search on Pubmed, ProQuest, Ovid PsycInfo, and Embase up to October 2020, and identified 721 studies. Fourteen studies were included in the meta-analysis. Three meta-regressions were also performed to further test whether publication year, the questionnaire used to assess depressive symptoms, or the interval of time between T0 and T1 moderated the effect of HRVB. Overall, we analysed 14 RCTs with a total of 794 participants. The random effect analysis yielded a medium mean effect size g  = 0.38 [95% CI  = 0.16, 0.60; 95% PI  =  − 0.19, 0.96], z  = 3.44, p  = 0.0006. The total heterogeneity was significant, Q T  = 23.49, p  = 0.03, I 2  = 45%, which suggested a moderate variance among the included studies. The year of publication ( χ 2 (1) = 4.08, p  = 0.04) and the questionnaire used to assess symptoms ( χ 2 (4) = 12.65, p  = 0.01) significantly moderated the effect of the interventions and reduced heterogeneity. Overall, results showed that HRVB improves depressive symptoms in several psychophysiological conditions in adult samples and should be considered as a valid technique to increase psychological well-being.

Despite the attempt to make decisions based on evidence, doctors still have to consider patients' choices which often involve other factors. In particular, emotions seem to influence the way that options and the surrounding information are interpreted and used. The objective of the present review is to provide a brief overview of research on decision making and cancer with a specific focus on the role of emotions. Thirty-nine studies were identified and analysed. Most of the studies investigated anxiety and fear. Worry was the other psychological factor that, together with anxiety, played a crucial role in cancer-related decision-making. The roles of fear, anxiety and worry were described for detection behaviour, diagnosis, choice about prevention and curative treatments and help-seeking behaviour. Results were inconsistent among the studies. Results stressed that cognitive appraisal and emotional arousal (emotion's intensity level) interact in shaping the decision. Moderate levels of anxiety and worry improved decision-making, while low and high levels tended to have no effect or a hindering effect on decision making. Moderating factors played an under-investigated role. Decision making is a complex non-linear process that is affected by several factors, such as, for example, personal knowledge, past experiences, individual differences and certainly emotions. Research studies should investigate further potential moderators of the effect of emotions on cancer-related choice. Big data and machine learning could be a good opportunity to test the interaction between a large amount of factors that is not feasible in traditional research. New technologies such as eHealth and virtual reality can offer support for the regulation of emotions and decision making.

ObjectiveSince its birth about 30 years ago, Narrative Medicine approach has increased in popularity in the medical context as well as in other disciplines. This paper aims to review Narrative Medicine research studies on patients' and their caregivers' illness experience.Setting and participantsMEDLINE, Psycinfo, EBSCO Psychological and Behavioural Science, The Cochrane Library and CINAHL databases were searched to identify all the research studies which focused on the Narrative Medicine approach reported in the title, in the abstract and in the keywords the words ‘Narrative Medicine’ or ‘Narrative-based Medicine’. Primary and secondary outcome measures: number of participants, type of disease, race and age of participants, type of study, dependent variables, intervention methods, assessment.ResultsOf the 325 titles screened, we identified 10 research articles fitting the inclusion criteria. Our systematic review showed that research on Narrative Medicine has no common specific methodology: narrative in Medicine is used as an intervention protocol as well as an assessment tool. Patients' characteristics, types of disease and data analysis procedures differ among the screened studies.ConclusionsNarrative Medicine research in medical practice needs to find clear and specific protocols to deepen the impact of narrative on medical practice and on patients' lives.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I–III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

A series of four studies explored how the ability to comprehend and transform probability numbers relates to performance on judgment and decision tasks. On the surface, the tasks in the four studies appear to be widely different; at a conceptual level, however, they all involve processing numbers and the potential to show an influence of affect. Findings were consistent with highly numerate individuals being more likely to retrieve and use appropriate numerical principles, thus making themselves less susceptible to framing effects, compared with less numerate individuals. In addition, the highly numerate tended to draw different (generally stronger or more precise) affective meaning from numbers and numerical comparisons, and their affective responses were more precise. Although generally helpful, this tendency may sometimes lead to worse decisions. The less numerate were influenced more by competing, irrelevant affective considerations. Analyses showed that the effect of numeracy was not due to general intelligence. Numerical ability appears to matter to judgments and decisions in important ways.

Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Background. Identifying and understanding modifiable factors for the well-being of cancer patients is critical in survivorship research. We studied variables associated with the exercise habits of breast cancer patients and investigated if the achievement of exercise recommendations was associated with enhanced quality of life and/or psychological well-being. Material and Methods. 311 women from Finland, Portugal, Israel, and Italy receiving adjuvant therapy for stage I–III breast cancer answered questions about sociodemographic factors and physical exercise. Quality of life was assessed by the EORTC C30 and BR23 questionnaires. Anxiety and depression were evaluated using the HADS scale. Results. At the beginning of adjuvant therapy and after twelve months, 32% and 26% of participants were physically inactive, 27% and 30% exercised between 30 and 150 minutes per week, while 41% and 45% exercised the recommended 150 minutes or more per week. Relative to other countries, Finnish participants were more likely to be active at baseline and at twelve months (89% vs. 50%, p<0.001 and 87% vs. 64%, p<0.001). Participants with stage I cancer were more likely to be active at twelve months than those with a higher stage (80% vs. 70%,p<0.05). The inactive participants reported more anxiety (p<0.05) and depression (p<0.001), lower global quality of life (p<0.001), and more side effects (p<0.05) than the others at twelve months. Accordingly, those who remained inactive or decreased their level of exercise from baseline to twelve months reported more anxiety (p<0.01) and depression (p<0.001), lower global quality of life (p<0.001), and more side effects (p<0.05) than those with the same or increased level of exercise. Conclusion. For women with early breast cancer, exercise was associated with a better quality of life, less depression and anxiety, and fewer adverse events of adjuvant therapy. Trial registration number: NCT05095675. Paula Poikonen-Saksela on behalf of Bounce consortium (https://www.bounce-project.eu/).

To identify trends of patients' urinary and sexual dysfunctions from a clinical and psychological perspective and understand whether sociodemographic and medical predictors could differentiate among patients following different one-year longitudinal trajectories. An Italian sample of 478 prostate cancer patients undergone Robot-Assisted Radical Prostatectomy completed the EPIC-26 survey between July 2015 and July 2016 at the pre-hospitalization (T0), 45 days (T1) and 3 (T2), 6 (T3), 9 (T4), and 12 months (T5) after surgery. Sociodemographic and clinical characteristics (age, BMI, diabetes, nerve-sparing procedure) were also collected. Latent Class Growth Analysis was conducted separately for sexual dysfunction and urinary incontinence EPIC-26 subscales. The association between membership in the two longitudinal trajectories of urinary and sexual dysfunctions was assessed by considering Chi-square test and its related contingency table. People who have a high level of urinary incontinence at T1 are likely to have a worse recovery. Age, BMI and pre-surgical continence may affect the level of incontinence at T1 and the recovery trajectories. Patients with low and moderate sexual problems at T1 can face a moderate linear recovery, while people with high level of impotence immediately after surgery may take a longer period to solve sexual dysfunctions. Age and the pre-surgical sexual condition may impact the recovery. Finally, a great proportion of patients reported both steady problems in sexual function and constant high levels of urinary incontinence over time. This study highlights different categories of patients at risk who may be important to know in order to develop personalized medical pathways and predictive models in a value-based healthcare.

Background This study aims to validate and evaluate the psychometric properties and reliability of the Italian version of the Expanded Prostate Cancer Index Composite – Short Form (EPIC-26), a measure of quality of life (QoL) for prostate cancer patients. Methods Two hundred and eighty-four prostate cancer patients completed the Italian version of the EPIC-26 questionnaire at 45 days (T1) and 3 months (T2) after robot-assisted radical prostatectomy (RARP). Psychometric properties were evaluated using structural equation modeling: the goodness of fit of the correlated five-factor model (CFFM) for the EPIC-26 was assessed using the confirmatory factor analysis (CFA), while longitudinal invariance was conducted to assess the ability of the EPIC-26 to measure QoL construct over time. Test-retest reliability was assessed as well by considering intraclass correlations. Results At T1, the CFFM model displayed a good fit to data. Similarly, the model showed an adequate fit also at T2. Results of the reliability analysis attested the acceptable internal consistency and test-retest reliability of each dimension: all Cronbach’s alphas could be classified as acceptable (i.e., above .65) except for low Cronbach’s alpha for hormonal dysfunction at T1 (i.e., .638) and urinary irritation at both waves. (i.e., respectively .585 and .518). Finally, psychometric properties were invariant over time and each of the five dimensions of QoL displayed from moderate (all ICCs above .500) to good test-retest reliability (i.e. ICC for urinary incontinence = .764). Conclusions Results of the CFA and the measurement invariance analysis demonstrated the validity of the Italian version of the EPIC-26 to assess QoL in prostate cancer patients. Its reliability and good psychometric qualities are well-supported, thus providing a valid tool to assess health-related quality of life and its change over time in prostate cancer patients.