Correction: A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

[This corrects the article DOI: 10.2196/48852.].

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

BACKGROUND: Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. OBJECTIVE: The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. METHODS: This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). RESULTS: The recruitment process started in May 2023 and is expected to conclude on December 2023. CONCLUSIONS: The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48852.

Heart Rate Variability Biofeedback in Cancer Patients: A Scoping Review.

Heart Rate Variability (HRV) Biofeedback (BFB) has been shown to improve autonomic balance and wellbeing in chronic diseases. As cardiac variability represents an index of cognitive and emotional regulation, HRV-BFB has been shown to lead to improvements in physiological and psychological adaptability and quality of life. However, knowledge of HRV-BFB in cancer patients is lacking, and available results are diversified according to methods and outcomes. The present paper undertakes a scoping review, exploring the use of HRV-BFB to modulate autonomic balance, cancer symptom management, and quality of life in cancer. This scoping review analyzes empirical evidence considering study designs, BFB methods, and psychophysiological outcomes. Research that focused on HRV-BFB effects in cancer patients was selected (79%). In addition, a systematic review and meta-analysis (31%) focusing on HRV, or BFB in chronic conditions, including cancer, were considered. The studies examined BFB treatment for thyroid, lung, brain or colon cancer, hematologic cancer, and survivors or terminal cancer patients. Retrieved studies reported physiological and psychological indices as primary outcomes: they included HRV values, sleep, pain, fatigue, depression, anxiety, and quality of life. Although the heterogeneity of publications makes it difficult to generalize the effectiveness of HRV-BFB, the training has been proven to improve cancer symptoms and well-being.

Mindfulness Effects on Mind Wandering and Autonomic Balance.

The natural tendency of the mind to wander (i.e., mind wandering), is often connected to negative thoughts and emotional states. On the other hand, mindfulness (i.e., the ability to focus one's attention on the present moment in a non-judgmental way) has acquired a growing interest in recent years given its beneficial role in improving awareness and self-regulation. Starting from previous evidence, this study aims to clarify the psychological, physiological, and affective impact of a mindfulness exercise on mind wandering. Twenty-eight non-expert female meditators were recruited for this study. Heart rate variability (HRV), state mindfulness, mind wandering manifestations, and affective states, were recorded during a baseline condition, a mindfulness breathing observation exercise, and a final rest condition. Subjects reported significant decreases in mind wandering comparing baseline and mindfulness. Changes in mind wandering were mirrored by changes in HRV, with higher HRV during the breathing observation exercise. Significant associations were found between scores of mindfulness, mind wandering, and affective states measured during the task. Our findings confirmed the role of mindfulness in reducing mind wandering and increasing HRV. Results are discussed considering mindfulness associations with self-regulation and well-being.

Do catastrophizing and autonomic-reduced flexibility mediate pain outcomes in chronic headache?

OBJECTIVES: Maladaptive cognitive strategies and reduced autonomic flexibility have been reported in chronic pain conditions. No study to date addressed the effects of maladaptive coping and reduced autonomic flexibility, as indexed by heart rate variability (HRV), in chronic headaches. The present study aimed to assess the mediating role of pain catastrophizing and HRV on pain outcomes in patients with chronic headache. METHODS: Thirty-two chronic headache patients and 28 healthy controls were recruited. Self-reported pain severity, pain interference on daily activity, and pain catastrophizing were assessed through the Multidimensional Pain Inventory and the Pain-Related Self Statements Scale. HRV was recorded at rest. Correlations and mediation analysis between self-report, HRV, and pain outcomes were run. RESULTS: Patients with chronic headache reported significantly higher pain severity (p < .001; d = - 1.98), pain interference on daily activity (p < .001; d = - 1.81), and pain catastrophizing (p < .001; d = - 0.96) compared to controls. They also presented significantly lower HRV (p < .05; d = 0.57). Both pain catastrophizing and HRV were associated with pain interference on daily activity. However, from mediation analysis, pain catastrophizing only emerged as the mediator for pain severity (p < .001; b = 0.30) and pain interference (p < .001; b = 0.14). CONCLUSION: Present results showed that chronic headache patients are characterized by high catastrophizing and lower physiological adaptability. Pain catastrophizing emerged as the only mediator of pain outcomes, suggesting that cognitive factors might have a major influence on the severity of pain and its interference on daily activities. Further studies are needed to evaluate these autonomic-cognitive interactions in chronic pain.