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.

Purpose: This study investigated the impact of healthcare professional-led interventions on adherence to oral targeted therapy and identified the behavior change techniques (BCTs) underpinning the interventions. Methods: A systematic search of MEDLINE, Embase, APA PsycInfo, CINAHL Plus, PubMed, and Web of Science up to July 2024 identified randomized controlled trials and cohort studies involving adult patients (≥ 18 years) with cancer on oral targeted therapy receiving healthcare professional-led interventions to improve adherence. Adherence-related outcomes, including proportions of patients continuing treatments or with a medication possession ratio (MPR) ≥ 90%, were compared between intervention and control (usual care) groups. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) and heterogeneity (I ² statistic) were reported. Differences in median time to treatment discontinuation were calculated and synthesized where applicable. Interventions were categorized using the BCT taxonomy. Results: This review included 11 studies (1,654 patients). The pooled results for proportions of patients continuing treatment (OR 17.91; 95%CI 3.18, 100.73; I ² < 0.1%) or with an MPR ≥ 90% (OR 3.67; 95%CI 1.98, 6.80; I ² < 0.1%) showed a significantly favorable outcome in the intervention group compared to the control group. In two studies, the median time to treatment discontinuation was longer in the intervention group than in the control group. The most commonly used BCTs were “credible source” (n = 11), “problem-solving” (n = 9), “instruction on how to perform a behavior” (n = 9), and “pharmacological support” (n = 8). Conclusion: Despite limited evidence, healthcare professional–led interventions significantly improve treatment adherence. Future studies should tailor strategies for individual needs and apply BCTs in designing effective interventions. PROSPERO registered: no. CRD42024571808.

Introduction: Tyrosine kinase inhibitors (TKIs) have been used as the first-line treatment for many patients with renal cell carcinoma (RCC), the seventh most common cancer in the United Kingdom. However, suboptimal adherence to TKIs can result in poor clinical prognosis. This study quantified RCC patients’ adherence to TKIs and explored factors associated with suboptimal adherence. Method: This retrospective cohort study was conducted at a specialist oncology tertiary hospital in Northwest England, using pharmacy dispensing records between November 2021 and March 2022. TKI prescriptions dispensed to patients with RCC were extracted to calculate the persistency gaps (≥7 or ≥14 days) and medication possession ratio (MPR). Multilevel regression analysis was conducted to associate MPR and persistency gaps with specific patient-related and TKI-related factors. This study did not require ethics approval. Results: Of the 2225 prescriptions dispensed to 109 patients, 469 (23.4%) and 274 (13.7%) persistency gaps of ≥7 and ≥14 days were identified. About 75% and 92% of patients had a persistency gap of ≥7 days within the first 90 days and 180 days. The length of time since the first TKI prescription ( p < 0.001) and the use of sunitinib( p = 0.003) were significantly associated with the number of prescription gaps of ≥7 days. Moreover, the median MPR was 95.6% (interquartile range: 90.7%, 100.1%). Similarly, the length of time since the first TKI prescription was dispensed ( p < 0.001) and the use of sunitinib ( p = 0.034) were significantly associated with MPR. Discussion and conclusion: This single-centre study found that patients with RCC generally adhere to TKIs (MPR > 90%), but many patients experienced a persistency gap. The crucial window to mitigate TKI utilisation is within 180 days after the initial dispensing of TKIs. Further large-scale studies are required to comprehensively investigate other factors associated with adherence to TKIs and develop interventions to improve adherence and medication use problems.

Background: Adherence to tyrosine kinase inhibitors (TKIs), the first-line treatment for renal cell carcinoma (RCC), is critical to ensure intended treatment outcomes. However, 75% of patients with RCC have persistency gaps (>7 days) within the first 90 days after initiating TKIs. This study explored factors affecting TKI adherence in RCC patients to inform future interventions. Methods: A retrospective cohort study was conducted at a specialist oncology hospital in Northwest England from October 2020 to October 2022 on patients with RCC treated with TKIs. TKI prescriptions and persistence gaps (>7 days) were identified from electronic dispensing records. Factors associated with persistence gaps were retrieved by reviewing patients’ clinical records. We used descriptive statistics to summarise the results and Kaplan–Meier analysis to assess the probability and the time to the first gap, stratified by adverse drug effect (ADE)-related and non-ADE-related gaps. Results: Among 165 included patients, 611 persistence gaps were identified. ADEs accounted for 59% (n = 464) of 787 recorded factors, with diarrhoea being the most frequent ADE (9.5%). Patients holding leftover TKIs were the primary (15.1%) non-ADE factor for persistency gaps. At least one gap was observed with 82% of patients (n = 135); 19% had ≥5 ADE-related gaps, and 25% had ≥5 non-ADE-related gaps. ADE-related gaps typically occurred within the first three months (50%), while non-ADE-related gaps were not time-dependent. Conclusions: ADEs, including diarrhoea and pain-related reactions, were the most frequently reported issues affecting TKI persistency in patients with RCC. These ADEs are likely to impact patients’ quality of life and adherence. Future qualitative research is warranted to explore patients’ care needs and additional factors such as health literacy and self-efficacy.