Fostering medication adherence in children with epilepsy using mHealth technology

项目摘要/摘要 不坚持服用抗癫痫药物(AEDs)是一个常见的问题(即58%的患者有一定程度的 不遵守)，对新诊断为癫痫的幼儿，具有潜在的破坏性后果。 AED不坚持与癫痫发作风险增加3倍、生活质量差、不准确 临床决策，以及更高的医疗保健利用率和成本。遵守法律的主要障碍之一 遗忘，这可能特别容易受到mHealth(医疗保健中的移动技术)干预的影响。 尽管迫切需要制定和实施干预措施以提高遵从性，但很少有家庭- 为患有癫痫的幼儿及其家人提供的干预措施，但PI的飞行员和 现有的R01试用版。尽管前景看好，但这种干预需要六次面对面的会议，这可能是 对于由于时间、经济或交通原因而无法获得常规三级专科护理的家庭来说，这是不可能的 约束条件。因此，未得到充分服务的儿童癫痫患者的医疗和心理社会需求未得到满足。 由于有限的机会获得这种最先进的护理，使之永久化和复杂化。我们的总体目标是测试一个 MHealth遵从性干预，使用基于个人的分级护理模式可轻松访问 需要。这种加强的护理模式将节省患者、家庭和提供者的时间、成本和资源。这个 该多站点R01的目标是进行两阶段、顺序、多分配、随机试验(SMART) 评估移动健康干预策略在改善儿童照顾者对AED依从性方面的有效性 患有癫痫的年幼儿童。两个月的基准期之后将分两个阶段。阶段1(3个月 Long)，非依从性照顾者(&lt；95%)将被随机分配到mHealth教育模块并自动进行 数字提醒(控制)或mHealth教育模块、自动数字提醒和个性化 基于实时依从性监测(治疗)的依从性反馈，以解决以下主要障碍 忘记了。在第二阶段(长两个月)开始时，照顾者随机接受不接受治疗的患者 在阶段1结束前实现遵守&>95%(响应)将重新随机选择继续 个性化遵从性反馈或个性化遵从性反馈通过两个mHealth增强 与治疗师的问题解决模块(翻译自PIS现有的随机对照试验)。因此，有三个 此SMART中嵌入的干预策略：增加了#1控制、#2治疗和#3问题解决 治疗三个月后如无反应。主要结果是电子监控的遵从性和 次要结果包括癫痫发作的严重程度/频率、生活质量和医疗保健利用率。如果的目标是 该项目的实现，这项研究将对儿童癫痫产生很大影响，具有潜在的 改变治疗不依从的临床做法。这种智能设计将使我们能够识别出 最有可能对干预措施做出反应，并以更耗时和更资源密集型的方式加强护理 必要时进行干预(即通过网络与治疗师解决问题)。

PROJECT SUMMARY/ABSTRACT Non-adherence to antiepileptic drugs (AEDs) is a common problem (i.e., 58% of patients have some level of non-adherence) for young children with newly diagnosed epilepsy, with potentially devastating consequences. AED non-adherence is associated with a 3-fold increased risk of seizures, poor quality of life, inaccurate clinical decision-making, and higher health care utilization and costs. One of the primary barriers to adherence is forgetting, which may be particularly amenable to mHealth (mobile technology in healthcare) interventions. Despite the critical need to develop and implement interventions to improve adherence, there are few family- based interventions for young children with epilepsy and their families, with the exception of the PI's pilot and existing R01 trial. Although highly promising, this intervention requires six in-person sessions, which can be impossible for families who lack routine access to tertiary specialty care due to time, financial, or transportation constraints. Thus, unmet medical and psychosocial needs of the underserved pediatric epilepsy population are perpetuated and compounded by limited access to this state of the art care. Our overall goal is to test a mHealth adherence intervention that is easily accessible using a stepped up care model based on individual needs. This stepped up care model will conserve patient, family, and provider time, costs and resources. The aim of this multi-site R01 is to conduct a two-stage, sequential, multiple assignment, randomized trial (SMART) to evaluate the effectiveness of mHealth intervention strategies for improving AED adherence in caregivers of young children with epilepsy. A two-month baseline period will be followed by two stages. In Stage 1 (3-months long), non-adherent caregivers (< 95%) will be randomized to a mHealth education module and automated digital reminders (control) or the mHealth education module, automated digital reminders, and individualized adherence feedback based on real-time adherence monitoring (treatment) to address the primary barrier of forgetting. At the beginning of Stage 2 (two months long), caregivers randomized to treatment who do not achieve adherence > 95% (response) by the end of Stage 1 will be re-randomized to either continued individualized adherence feedback or individualized adherence feedback augmented with two mHealth problem-solving modules (translated from the PIs existing RCTs) with a therapist. Thus, there are three intervention strategies embedded in this SMART: #1 control, #2 treatment, and #3 problem-solving augmented treatment if nonresponsive at three months. The primary outcome is electronically-monitored adherence and secondary outcomes include seizure severity/frequency, quality of life, and healthcare utilization. If the aims of the project are achieved, this study would have a large impact on pediatric epilepsy, with the potential to change clinical practice for treating non-adherence. The SMART design would allow us to identify patients who are most likely to respond to interventions and step up care with more time- and resource-intensive interventions (i.e., problem-solving with a therapist via the web), when necessary.