The Efficacy of Digital Interventions on Adherence to Oral Systemic Anticancer Therapy Among Patients With Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Digital interventions have been increasingly applied in multidisciplinary care plans to improve medication adherence to oral systemic anticancer therapy (SACT), the crucial lifesaving treatments for many cancers. However, there is still a lack of consensus on the efficacy of those digital interventions. objectives: This systematic review and meta-analysis aimed to investigate the efficacy of digital interventions in improving adherence to oral SACTs in patients with cancer. Methods: This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines. The protocol has been registered at PROSPERO (no. CRD42024550203). Fully published, randomized controlled trials (RCTs) in English on adults with cancer assessing digital interventions for improving adherence to oral SACTs were retrieved from MEDLINE, Embase, APA PsycINFO, and CINAHL Plus up to May 31, 2024. Adherence measures compared between digital intervention users and nonusers were extracted. The proportions of poor adherence were synthesized using a random-effects model. The pooled results were reported as the odds ratio and 95% CI. The heterogeneity was assessed with the I2 test (%). The mean difference and 95% CI were calculated from the mean adherence score and SD. A risk of bias assessment was conducted using version 2 of the Cochrane Risk of Bias Assessment Tool (RoB 2) for RCTs, which ensured that a quality assessment of all included studies was conducted as recommended by the Cochrane Collaboration. Results: This study included 13 RCTs on digital interventions for improving adherence to oral SACTs in patients with cancer. The 13 RCTs, published between 2016 and 2024, were conducted in the United States, South Korea, France, Egypt, Finland, Australia, Colombia, Singapore, and Turkey. The technologies used were mobile apps (n=4), reminder systems (n=4), telephone follow-ups (n=3), and interactive multimedia platforms (n=2). Adherence was measured by surveys (n=8), relative dose intensity (n=2), pill count (n=1), self-reported missed doses (n=1), a smart pill bottle (n=1), and urine aromatase inhibitor metabolite assays (n=1). Concerns regarding risk of bias primarily involved randomization, missing outcome data, and outcome measurement, including nonblinded randomization, subjective patient-reported data, and difficulties in distinguishing between missed appointments and actual medication nonadherence. Pooled results from 11 trials showed that digital technology users had significantly lower risk of poor adherence (odds ratio 0.60, 95% CI 0.47-0.77). Two studies reported positive mean differences in adherence scores comparing digital intervention users and nonusers. However, due to considerable heterogeneity (I²=73.1%), it is difficult to make a definitive conclusion from the pooled results about the effect of digital interventions upon adherence to oral anticancer therapy. Conclusions: Digital intervention users exhibited significantly lower risk of poor oral SACTs adherence than nonusers. Acknowledging individual variation and tailoring digital technologies to prioritize patient needs is essential.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".