Choi Yan Yee Cherizza, Fineberg Micah, Kassavou Aikaterini
Department of Public Health and Primary Care, Clinical Medical School, University of Cambridge, Cambridge CB2 0SR, UK.
UCL Queen Square Institute of Neurology, University College London, London NW3 2PF, UK.
Behav Sci (Basel). 2023 Mar 11;13(3):246. doi: 10.3390/bs13030246.
Stroke affects more than 30 million people every year, but only two-thirds of patients comply with prescribed medication, leading to high stroke recurrence rates. Digital technologies can facilitate interventions to support treatment adherence.
This study evaluates the effectiveness of remote interventions and their mechanisms of action in supporting medication adherence after stroke.
PubMed, MEDLINE via Ovid, Cochrane CENTRAL, the Web of Science, SCOPUS, and PsycINFO were searched, and meta-analysis was performed using the Review Manager Tool. Intervention content analysis was conducted based on the COM-B model.
Ten eligible studies were included in the review and meta-analysis. The evidence suggested that patients who received remote interventions had significantly better medication adherence (SMD 0.49, 95% CI [0.04, 0.93], and = 0.03) compared to those who received the usual care. The adherence ratio also indicated the interventions' effectiveness (odds ratio 1.30, 95% CI [0.55, 3.10], and = 0.55). The systolic and diastolic blood pressure (MD -3.73 and 95% CI [-5.35, -2.10])/(MD -2.16 and 95% CI [-3.09, -1.22]) and cholesterol levels (MD -0.36 and 95% CI [-0.52, -0.20]) were significantly improved in the intervention group compared to the control. Further behavioural analysis demonstrated that enhancing the capability within the COM-B model had the largest impact in supporting improvements in adherence behaviour and relevant clinical outcomes. Patients' satisfaction and the interventions' usability were both high, suggesting the interventions' acceptability.
Telemedicine and mHealth interventions are effective in improving medication adherence and clinical indicators in stroke patients. Future studies could usefully investigate the effectiveness and cost-effectiveness of theory-based and remotely delivered interventions as an adjunct to stroke rehabilitation programmers.
中风每年影响超过3000万人，但只有三分之二的患者遵医嘱服药，导致中风复发率很高。数字技术可以促进干预措施以支持治疗依从性。
本研究评估远程干预措施在支持中风后药物依从性方面的有效性及其作用机制。
检索了PubMed、通过Ovid检索的MEDLINE、Cochrane CENTRAL、科学网、SCOPUS和PsycINFO，并使用Review Manager工具进行荟萃分析。基于COM-B模型进行干预内容分析。
10项符合条件的研究纳入了综述和荟萃分析。证据表明，与接受常规护理的患者相比，接受远程干预的患者药物依从性显著更好（标准化均数差0.49，95%置信区间[0.04，0.93]，P = 0.03）。依从率也表明了干预措施的有效性（比值比1.30，95%置信区间[0.55，3.10]，P = 0.55）。与对照组相比，干预组的收缩压和舒张压（均数差-3.73和95%置信区间[-5.35，-2.10]）/（均数差-2.16和95%置信区间[-3.09，-1.22]）以及胆固醇水平（均数差-0.36和95%置信区间[-0.52，-0.20]）均有显著改善。进一步的行为分析表明，在COM-B模型内增强能力对支持依从行为和相关临床结果的改善影响最大。患者满意度和干预措施的可用性都很高，表明干预措施具有可接受性。
远程医疗和移动健康干预措施在改善中风患者的药物依从性和临床指标方面是有效的。未来的研究可以有益地调查基于理论的远程干预措施作为中风康复计划辅助手段的有效性和成本效益。
