Center for Global Mental Health, National Institute of Psychiatry Ramón de la Fuente Muñiz, Mexico City, Mexico.
Department of Health care Policy, Harvard Medical School, Boston, MA, USA.
Trials. 2022 Jun 2;23(1):450. doi: 10.1186/s13063-022-06255-3.
Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective.
We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms.
By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation.
IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024.
ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.
重度抑郁症（MDD）和广泛性焦虑症（GAD）在大学生中非常普遍，并且会预测到他们在大学期间的表现受损以及之后生活中的角色功能受损。然而，大多数学生并未接受治疗，尤其是在中低收入国家（LMICs）。我们旨在评估在拉丁美洲的两个 LMIC（哥伦比亚和墨西哥）的大学生中使用可扩展且价格低廉的互联网提供的跨诊断认知行为疗法（iCBT）来扩大治疗的效果，并探讨创建预测 iCBT 对谁最有效的精准治疗规则（PTR）的可行性。
我们将首先在参与大学的学生心理健康诊所寻求治疗的学生或对提供服务的电子邮件做出回应的学生中进行一项多地点随机实用临床试验（N=1500）。等候诊所服务的学生将随机分为无指导 iCBT（33%）、指导 iCBT（33%）和常规治疗（TAU）（33%）。iCBT 将立即提供，而 TAU 将在任何可用的诊所预约时提供。将在 90 天评估短期综合效果，在随机分组后 12 个月评估长期效果。我们将使用集成机器学习来预测无指导 iCBT 与指导 iCBT 与 TAU 的治疗效果的异质性，并制定一个精准治疗规则（PTR）来优化个别学生的结果。然后，我们将使用另外两个样本（每组 500 例）进行第二次和第三次试验，但两个臂之间的分配不均等：根据第一次试验中开发的 PTR（第二次试验的短期最佳结果的 PTR 和第三次试验的 12 个月结果），25%将被分配到产生最佳结果的治疗中，而剩余的 75%将在所有三个治疗组之间平均分配。
通过收集全面的基线特征来评估治疗效果的异质性，我们将提供有价值的创新信息，以优化治疗效果并指导大学心理健康治疗计划。这种努力可能会对该地区产生巨大的公共卫生影响，因为它增加了治疗的覆盖面，减少了未满足的需求和诊所等候时间，并为循证干预计划和实施提供了模型。
IRB 批准方案版本 1.0；2020 年 6 月 3 日。招募于 2021 年 3 月 1 日开始。预计招募将于 2024 年 5 月 30 日完成。
ClinicalTrials.gov NCT04780542。首次提交日期：2021 年 2 月 28 日。
