Implementation strategies for monitoring adherence in real time (iSMART): A pilot randomized trial.
Bibliographic record
Abstract
9115 Background: Helping patients to manage symptoms and adhere to oral anticancer agents (OACAs) is a major challenge in oncology. OACAs cause side effects that can lead to suboptimal adherence if not optimally managed, contributing to decreased effectiveness. Low-cost, text messaging approaches have shown promise, but have not been robustly studied in oncology. Guided by principles from implementation and behavioral science, we developed and tested the effect of an augmented intelligence chatbot on OACA adherence and symptom burden in patients with advanced lung cancer. Methods: We conducted a two-arm pilot randomized trial (NCT04347161) to evaluate the effect of the chatbot on OACA adherence and symptom burden compared to usual care. The chatbot engages patients via text messaging and applies natural language processing and machine learning to learn from interactions. Core functionalities include: 1) motivational daily adherence reminders, 2) longitudinal symptom monitoring with self-management support, and 3) bidirectional communication with clinical teams. Participants included English-speaking patients with advanced lung cancer treated with any of 9 OACAs targeting EGFR, ALK, or ROS-1. The primary outcome was 12-week adherence, measured using the microelectronic monitoring system (MEMS) and defined dichotomously if the patient achieved ≥95% adherent days or not. Secondary outcomes were assessed at baseline and 12 weeks using validated survey instruments, including symptom burden using the Edmonton Symptom Assessment System Total Distress Score (range 0-90), health-related quality of life (HRQOL) using EQ-5D-3L (range 0-100), and usability using Health-ITUES (range 1-5). We used multivariable logistic regression adjusting for stratification variables to test the chatbot’s effect on adherence (intent-to-treat analysis) and evaluated mean differences (by arm) in secondary outcomes using Fisher’s exact test. Results: From February 2021 to August 2022, 75 patients across 4 sites enrolled (median age 65 years, 64% female, 88% White, 21.3% high school education or lower); 50.7% (n=38) were randomized to intervention. Compared to usual care, we observed no significant differences in adherence in the intervention arm (78.8% vs 81.8%; aOR=1.7 95% CI: 0.3-9.4). However, in contrast to those in usual care, participants in the intervention arm had significantly greater decreases in symptom burden (mean difference: -2.7 vs 2.6; p<0.05) and increases in HRQOL (mean difference: 4.1 vs -4.8; p<0.05) from baseline to 12 weeks. Overall chatbot usability was high (mean score=3.9). Conclusions: In this pilot randomized trial, an augmented intelligence chatbot successfully reduced symptom burden and improved HRQOL but did not significantly alter OACA adherence. Chatbots are a potentially scalable strategy for improving symptom management that warrant study in larger randomized trials. Clinical trial information: NCT04347161 .
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".