A Novel Remote Patient and Medication Monitoring Solution to Improve Adherence and Persistence With Inflammatory Bowel Disease Therapy (ASSIST Study): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel diseases (IBDs) are chronic inflammatory conditions of the gastrointestinal tract. Although adherence to IBD therapies is associated with improved clinical outcomes, overall adherence is poor. Consequently, there is a critical need to develop interventions that monitor adherence in real time and identify reasons for nonadherence to support clinical teams in initiating effective interventions. Recently, electronic- and web-based platforms have been developed to monitor adherence and guide interventions. A novel remote therapy monitoring (RTM) technology, the Tappt digital health system, has been developed to monitor real-time medication adherence patterns through smart label technologies, capture patient-reported outcomes and barriers to care, and process patient data through algorithms that trigger personalized digital and human touch points between clinical visits. Such a digital health solution enables care teams to proactively identify and mitigate nonadherence and worsening clinical outcomes. OBJECTIVE: We propose a 12-month multicenter randomized controlled trial to assess the effectiveness of the Tappt digital health system on adherence, clinical outcomes, and health care use among patients diagnosed with IBD starting a new oral or subcutaneous therapy. METHODS: The digital health system intervention will provide automatic measurement of medication adherence via smart labels for pill bottles or injectors as well as a monitoring platform for providers. The system will prompt patients to complete a two-item assessment of symptoms monthly using the PRO-2 scales for UC and Crohn disease, from which increased symptoms will be alerted to providers. Participants will be randomized 2:1 to the intervention group or the control group, which will receive standard of care. All participants are required to complete questionnaires at baseline as well as at 12, 26, and 52 weeks. Assuming an adherence rate of 0.65 and 0.9 among control and intervention participants, respectively, we will need to enroll 123 participants: 82 (66.7%) in the intervention group and 41 (33.3%) controls. We will compare adherence as measured by the medication possession ratio, defined as the number of days of supply of medication obtained during the observation period out of the total number of days in the observation period, in participants using the RTM versus those receiving standard of care. We will also compare clinical outcomes and health care use in participants using the RTM versus those receiving standard of care. RESULTS: We anticipate starting recruitment in December 2022. CONCLUSIONS: Effective medication adherence monitoring and intervention programs need to be cost-efficient, pose little or no burden to the patient, record reliable data in real time, and provide actionable insights to the health care team. We anticipate the Tappt digital health system to improve the medication possession ratio, clinical outcomes, and health care use compared with standard of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05316584; https://clinicaltrials.gov/ct2/show/NCT05316584. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40382.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".