Can-Pain-a digital intervention to optimise cancer pain control in the community: development and feasibility testing

Abstract

Abstract Purpose To develop a novel digital intervention to optimise cancer pain control in the community. This paper describes intervention development, content/rationale and initial feasibility testing. Methods Determinants of suboptimal cancer pain management were characterised through two systematic reviews; patient, caregiver and healthcare professional (HCP) interviews (n = 39); and two HCP focus groups (n = 12). Intervention mapping was used to translate results into theory-based content, creating the app “Can-Pain”. Patients with/without a linked caregiver, their general practitioners and community palliative care nurses were recruited to feasibility test Can-Pain over 4 weeks. Results Patients on strong opioids described challenges balancing pain levels with opioid intake, side effects and activities and communicating about pain management problems with HCPs. Can-Pain addresses these challenges through educational resources, contemporaneous short-acting opioid tracking and weekly patient-reported outcome monitoring. Novel aspects of Can-Pain include the use of contemporaneous breakthrough analgesic reports as a surrogate measure of pain control and measuring the level at which pain becomes bothersome to the individual. Patients were unwell due to advanced cancer, making recruitment to feasibility testing difficult. Two patients and one caregiver used Can-Pain for 4 weeks, sharing weekly reports with four HCPs. Can-Pain highlighted unrecognised problems, promoted shared understanding about symptoms between patients and HCPs and supported shared decision-making. Conclusions Preliminary testing suggests that Can-Pain is feasible and could promote patient-centred pain management. We will conduct further small-scale evaluations to inform a future randomised, stepped-wedge trial. Trial registration Qualitative research: ClinicalTrials.gov, reference NCT02341846 Feasibility study: NIHR CPMS database ID 34172