Support for Chronic Pain Management for Breast Cancer Survivors Through Novel Digital Health Ecosystems: Pilot Usability Study of the PainRELife Mobile App

BACKGROUND

Chronic pain is one of the most common and critical long-term effects of breast cancer. Digital health technologies enhance the management of chronic pain by monitoring physical and psychological health status and supporting pain self-management and patient treatment decisions throughout the clinical pathway.

OBJECTIVE

This pilot study aims to evaluate patients' experiences, including usability, with a novel digital integrated health ecosystem for chronic pain named PainRELife. The sample included patients with breast cancer during survivorship. The PainRELife ecosystem comprises a cloud technology platform interconnected with electronic health records and patients' devices to gather integrated health care data.

METHODS

We enrolled 25 patients with breast cancer (mean age 47.12 years) experiencing pain. They were instructed to use the PainRELife mobile app for 3 months consecutively. The Mobile Application Rating Scale (MARS) was used to evaluate usability. Furthermore, pain self-efficacy and participation in treatment decisions were evaluated. The study received ethical approval (R1597/21-IEO 1701) from the Ethical Committee of the European Institute of Oncology.

RESULTS

The MARS subscale scores were medium to high (range: 3.31-4.18), and the total app quality score was 3.90. Patients with breast cancer reported reduced pain intensity at 3 months, from a mean of 5 at T0 to a mean of 3.72 at T2 (P=.04). The total number of times the app was accessed was positively correlated with pain intensity at 3 months (P=.03). The engagement (P=.03), information (P=.04), and subjective quality (P=.007) subscales were positively correlated with shared decision-making. Furthermore, participants with a lower pain self-efficacy at T2 (mean 40.83) used the mobile app more than participants with a higher pain self-efficacy (mean 48.46; P=.057).

CONCLUSIONS

The data collected in this study highlight that digital health technologies, when developed using a patient-driven approach, might be valuable tools for increasing participation in clinical care by patients with breast cancer, permitting them to achieve a series of key clinical outcomes and improving quality of life. Digital integrated health ecosystems might be important tools for improving ongoing monitoring of physical status, psychological burden, and socioeconomic issues during the cancer survivorship trajectory.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/41216.

Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study

PURPOSE

A large proportion of patients with cancer suffer from breakthrough cancer pain (BTcP). Several unmet clinical needs concerning BTcP treatment, such as optimal opioid dosages, are being investigated. In this analysis the hypothesis, we explore with an unsupervised learning algorithm whether distinct subtypes of BTcP exist and whether they can provide new insights into clinical practice.

METHODS

Partitioning around a k-medoids algorithm on a large data set of patients with BTcP, previously collected by the Italian Oncologic Pain Survey group, was used to identify possible subgroups of BTcP. Resulting clusters were analyzed in terms of BTcP therapy satisfaction, clinical features, and use of basal pain and rapid-onset opioids. Opioid dosages were converted to a unique scale and the BTcP opioids-to-basal pain opioids ratio was calculated for each patient. We used polynomial logistic regression to catch nonlinear relationships between therapy satisfaction and opioid use.

RESULTS

Our algorithm identified 12 distinct BTcP clusters. Optimal BTcP opioids-to-basal pain opioids ratios differed across the clusters, ranging from 15% to 50%. The majority of clusters were linked to a peculiar association of certain drugs with therapy satisfaction or dissatisfaction. A free online tool was created for new patients’ cluster computation to validate these clusters in future studies and provide handy indications for personalized BTcP therapy.

CONCLUSION

This work proposes a classification for BTcP and identifies subgroups of patients with unique efficacy of different pain medications. This work supports the theory that the optimal dose of BTcP opioids depends on the dose of basal opioids and identifies novel values that are possibly useful for future trials. These results will allow us to target BTcP therapy on the basis of patient characteristics and to define a precision medicine strategy also for supportive care.

Breakthrough Cancer Pain: Preliminary Data of The Italian Oncologic Pain Multisetting Multicentric Survey (IOPS-MS)

Introduction

An ongoing national multicenter survey [Italian Oncologic Pain multiSetting Multicentric Survey (IOPS-MS)] is evaluating the characteristics of breakthrough cancer pain (BTP) in different clinical settings. Preliminary data from the first 1500 cancer patients with BTP enrolled in this study are presented here.

Methods

Thirty-two clinical centers are involved in the survey. A diagnosis of BTP was performed by a standard algorithm. Epidemiological data, Karnofsky index, stage of disease, presence and sites of metastases, ongoing oncologic treatment, and characteristics of background pain and BTP and their treatments were recorded. Background pain and BTP intensity were measured. Patients were also questioned about BTP predictability, BTP onset (≤10 or >10 min), BTP duration, background and BTP medications and their doses, time to meaningful pain relief after BTP medication, and satisfaction with BTP medication. The occurrence of adverse reactions was also assessed, as well as mucosal toxicity.

Results

Background pain was well controlled with opioid treatment (numerical rating scale 3.0 ± 1.1). Patients reported 2.5 ± 1.6 BTP episodes/day with a mean intensity of 7.5 ± 1.4 and duration of 43 ± 40 min; 977 patients (65.1%) reported non-predictable BTP, and 1076 patients (71.7%) reported a rapid onset of BTP (≤10 min). Higher patient satisfaction was reported by patients treated with fast onset opioids.

Conclusions

These preliminary data underline that the standard algorithm used is a valid tool for a proper diagnosis of BTP in cancer patients. Moreover, rapid relief of pain is crucial for patients’ satisfaction. The final IOPS-MS data are necessary to understand relationships between BTP characteristics and other clinical variables in oncologic patients.

Funding

Molteni Farmaceutici, Italy.