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.

"Interventions to increase adherence to oral therapies in breast cancer patients: A systematic review based on the behavior change technique taxonomy".

OBJECTIVE: High rates of non-adherence to oral medications in breast cancer (BC) patients have been reported. Here we provide an up-to-date systematic review of the interventions aimed at increasing adherence to oral medication in BC patients, with a particular focus on the content of the interventions. METHODS: PubMed, Scopus, Embase and Ovid databases and reference lists of relevant studies were searched through October 2022. Studies which (1) described an intervention aimed at increasing adherence to oral anticancer medication, (2) included (or planned to include) at least one sub-group of BC patients, (3) were written in English, and (4) with full-text available were included. The contents of the interventions were coded using the Behavior Change Technique Taxonomy. Quality assessment was conducted using Downs and Black scale. RESULTS: Thirty-six studies met the inclusion criteria and involved a total sample of 28,528 BC patients. Interventions were mainly delivered with eHealth devices (n = 21) and most of them used mobile app. Other studies used in-person modalities (e.g., CBT, relaxation technique) or written materials (e.g., psycho-educational booklet). The behavior change techniques most frequently implemented were "problem solving," "social support," "information about health consequences," and "prompts/cues". Quality assessment revealed that the higher risk of bias refers to the selection process. CONCLUSIONS: The use of reminders, monitoring patients' medication-taking behaviors and giving feedback were the most frequently implemented techniques in those interventions that resulted significant. If these preliminary observations were to be confirmed by future comparative studies, they should be taken into account when developing new interventions.