Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA.
Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA.
NEJM Evid. 2024 Feb;3(2):EVIDoa2300164. doi: 10.1056/EVIDoa2300164. Epub 2024 Jan 22.
Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials. METHODS: We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial. RESULTS: Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention. CONCLUSIONS: Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)
数字健康干预措施在随机临床试验之前可能需要进行优化。尽管许多数字健康干预措施已在试点研究中部署，但很少有研究利用收集到的数据来改进干预措施和后续的临床试验。方法：我们利用远程患者监测试点研究中符合数字健康干预条件的患者的自然差异，来设计和比较后续随机临床试验的干预措施。结果：我们的方法利用患者的异质性来确定一种干预措施，其估计效果是未经优化干预措施的两倍。结论：基于试点数据优化干预措施和临床试验可能会提高疗效并增加成功的可能性。（由美国国立卫生研究院等资助；ClinicalTrials.gov 编号，NCT04336969。）
