Technology interventions to support adherence to oral oncolytic agents: A systematic review and meta-analysis.
Bibliographic record
Abstract
320 Background: The increase in patients receiving oral oncolytics requires oncology health care professionals to have processes and interventions that support patient adherence. Because adherence to oral oncolytics is a priority for achieving optimal outcomes, the ASCO QOPI Standards emphasize the need to assess for adherence at meaningful intervals as well as implement interventions to promote adherence. Technology-based interventions have the potential to assess and support adherence. The objective of this review is to evaluate the overall effect of any technology-based intervention, as well as compare the use of interactive technology rather than non-interactive technology to improve oral oncolytic adherence. This review will serve as the evidence base for a clinical practice guideline on oral oncolytic adherence. Methods: A medical librarian searched EMBASE, PubMed, and CINAHL for comparative studies published in English from January 2000 to May 2021. Two reviewers screened titles and abstracts, and eligible full text articles independently and in duplicate using Covidence. When possible, quantitative findings were pooled in a meta-analysis. Risk of bias assessment for randomized controlled trials was done using the Cochrane Collaboration risk-of-bias 2.0 tool and for observational studies, the ROBINS-I instrument was used. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Results: Out of 663 studies, we identified 14 as eligible for analysis. Of the six RCTs (n = 737) that compared any technological intervention to no intervention, there was some heterogeneity across interventions (e.g., text messaging, mobile app, computer program) and reported outcome measures for adherence, including adherence rates, relative dose intensity (RDI), and number of weeks adherent. Patients using any technology rather than no technology may have higher adherence rates (MD: 8.81; 95% CI: 3.82, 13.81); however, there may be little effect on RDI (MD: -0.01; 95% CI: -0.04, 0.02) and number of weeks adherent (MD: -0.70; 95% CI: -1.96, 0.56), respectively. One RCT (n = 444) reported on adolescents receiving interactive and non-interactive interventions. There may be little effect on adherence rates among patients receiving interactive technology interventions rather than non-interactive education intervention (MD: 1.5; 95% CI, −0.2, 3.2). Conclusions: Better systems of oral oncolytic care are needed to support patients and their caregivers. Technology-based interventions may improve medication adherence in cancer patients on oral oncolytics; however, we are uncertain about the impact on other measure of adherence or the superiority of interactive rather than non-interactive technology interventions. Due to inconsistency in the evidence, additional research in this area is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.029 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".