Text messaging-based collection of patient data to inform prescription opioid management: Development and feasibility testing (Preprint)
Authors: Robinson-Papp Jessica , Cedillo Gabriela , Deshpande Richa , George Mary Catherine , Yang Qiuchen , Rogers Jason , Nmashie Alexandra , Mueller Bridget R. , Atreja Ashish , Kummer Benjamin , Benn Emma K.T. 
Published: August 2020

BACKGROUND

Opioid prescribing guidelines including the Centers for Disease Control and Prevention’s guideline (CDCG) recommend collection of a large amount of patient-reported data in order to perform an opioid risk-benefit assessment. However this is a time burden and barrier to guideline adherence.

OBJECTIVE

We used a pre-existing digital health platform to develop an automated text-message based mobile health technology called OM-App (opioid management app) to collect these data directly from patients. We sought to establish: 1) usability; 2) ability to generate clinically useful information; 3) methodology to export data for research.

METHODS

OM-App content, derived from the CDCG and supporting materials, queried: pain intensity/ interference; non-pharmacologic therapies; opioid adherence; opioid side effects and risk behaviors (e.g. gastrointestinal, cognitive, and respiratory side effects, symptoms of opioid use disorder). OM-App was tested within a 9-month feasibility trial of a provider-focused intervention to support CDCG-adherence in HIV primary care. At research visits patient-participants had urine drug testing and completed the Current Opioid Misuse Measure and Self-Reported Misuse, Abuse and Diversion of Prescription Opioids. Prescribed opioids were verified using the Prescription Drug Monitoring Program database. OM-App data were exported/integrated into the research database after two main modifications: 1) responses were re-coded from a ‘Response/Null’ format (which did not differentiate missing data from negative responses) into ‘1/0’ format; 2) data were reformatted so that each column contained responses to only one question.

RESULTS

Patient-participants (N=40) were women (n=22, 55%) and men living with HIV (mean age 61 years). Most were racial/ethnic minorities (n=38, 95%) with lower educational levels (n=21, 53% high school or below) and employment status (n=5, 13% employed). Thirty-nine (98%) patient-participants used OM-App at least once, and 21 (53%) used it consistently without any difficulty. Barriers to use included transient problems with message delivery or error messages and participant-centered issues including: forgetfulness or dislike of answering questions, compatibility issues with older devices, and disconnection of mobile service. Overall 70% of OM-App questions were answered with each individual question answered over 650 times. Few opioid side effects/risk behaviors were reported via OM-App cross-sectionally which precluded formal validation of the OM-App questions. However over the course of the study each side effect/risk behavior was reported at least once, and the most common (constipation) was endorsed by 24 (60%) participants. Twenty-one (53%) participants reported taking more opioids than prescribed at least once. The major provider-level barrier to using these patient-reported data was lack of integration into the electronic health record.

CONCLUSIONS

OM-App was used by patients to produce clinically relevant opioid- and pain-related data, which was successfully exported and integrated into a research database. These findings suggest that OM-App may be a useful tool for remote monitoring of patients prescribed opioids for chronic pain.

CLINICALTRIAL

Clinicaltrials.gov NCT03669939, https://clinicaltrials.gov/ct2/show/NCT03669939

INTERNATIONAL REGISTERED REPORT

RR2-doi:10.1016/j.conctc.2019.100468