Mobile Technology to Optimize Depression Treatment

移动技术优化抑郁症治疗

• 批准号: 10700120
• 负责人: Amy S B Bohnert
• 金额: $ 76.27万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700120
• 关键词: Acceleration; Address; Adopted; Behavior; Behavioral; Cardiovascular Physiology; Caring; Cellular Phone; Clinical; Clinical Trials; Data; Disease remission; Effectiveness; Emotions; Equipment and supply inventories; Frequencies; Goals; Health Services Accessibility; Healthcare Systems; Individual; Intervention; Knowledge; Lead; Machine Learning; Measures; Mental Depression; Mental Health; Mental Health Services; Methods; Modeling; Monitor; Outcome Assessment; Outpatients; Pathway interactions; Patients; Persons; Pharmaceutical Preparations; Physical activity; Population; Precision therapeutics; Predictive Factor; Psychotherapy; Public Health; Quality of life; Recording of previous events; Recovery; Research; Sample Size; Sampling; Signal Transduction; Sleep; Speed; Surveys; Symptoms; Technology; Time; Translating; Waiting Lists; Work; analytical method; analytical tool; clinical practice; demographics; density; depressive symptoms; design; digital intervention; disability; evidence base; experience; improved; individualized medicine; ineffective therapies; innovation; mobile computing; novel; patient response; patient variability; persistent symptom; predictive modeling; programs; recruit; sensor; social engagement; treatment duration; treatment effect; treatment response; treatment trial; wearable device; wearable sensor technology

Abstract Tailoring care to match patients to the treatment most effective for them has the potential to accelerate recovery and meaningfully reduce the growing burden of depression. A key barrier to tailoring care is the absence of objective, real-time methods to effectively predict and assess treatment response. Mobile technology holds promise to overcome this barrier. Specifically, smartphones and wearable sensors collect passive, continuous and objective measures of constructs central to depression, such as sleep, physical activity, cardiovascular function, and social engagement. Studies have demonstrated associations of single measures from these domains with depression. However, because most prior wearable studies have had limited sample sizes, they have not been able to synthesize actionable information across multiple domains of mobile technology data and effectively guide treatment. Our long-term goal is to substantially increase the effectiveness of depression treatments and the capacity of our mental health care system. Our objective in this application is to identify factors that can be used to effectively match patients to treatments and track their recovery. Through the PROviding Mental health Precision Treatment (PROMPT) study, we will complete the following specific aims: Aim 1) Identify factors that predict which treatment is most likely to reduce depression symptoms for a specific patient; and Aim 2) Identify passive mobile technology-based measures that serve as signals of treatment response. To achieve these aims, we will recruit 2,200 subjects from waitlist for outpatient depression treatment. We will then track patients for six months through wearable sensors, smartphones, and repeated surveys. For both aims, we will use machine learning approaches to develop comprehensive prediction models. Our approach is innovative because it applies technology and analytic tools to a large and diverse sample of subjects receiving treatment under real world conditions. Further, the project is designed to lead directly to an organization-level intervention that matches patients to treatments and continuously monitors their response to treatment. Finally, this project is significant because it has the potential to greatly accelerate recovery by identifying the treatment from which each person is likely to derive the most benefit, ultimately helping to address the high population burden of depression.

抽象的 调整护理使患者与对他们最有效的治疗相匹配，有可能加速 恢复并有意义减轻抑郁症的日益增长的负担。调整护理的关键障碍是 没有客观的实时方法可以有效预测和评估治疗反应。移动的 技术有望克服这一障碍。具体而言，智能手机和可穿戴传感器收集 被动，连续和客观的结构构造的抑郁症中心，例如睡眠，身体 活动，心血管功能和社交参与。研究表明了单一的关联 来自这些领域的措施。但是，因为大多数先前的可穿戴研究已经 样本量有限，他们无法综合跨多个域的可行信息 移动技术数据并有效指导治疗。我们的长期目标是实质上增加 抑郁症治疗的有效性和我们的心理保健系统的能力。我们的目标 应用是确定可用于有效与患者与治疗相匹配的因素并跟踪他们的 恢复。通过提供心理健康精确治疗（及时）研究，我们将完成 以下特定目的：目标1）确定预测哪种治疗方法最有可能减少抑郁症的因素 特定患者的症状；目标2）确定基于移动技术的措施 治疗反应的信号。为了实现这些目标，我们将从候补名单中招募2,200名受试者的受试者 抑郁症治疗。然后，我们将通过可穿戴传感器，智能手机和 重复调查。对于这两个目标，我们将使用机器学习方法来开发全面 预测模型。我们的方法具有创新性，因为它将技术和分析工具应用于大型且 在现实世界中接受治疗的受试者的不同样本。此外，该项目旨在 直接导致组织水平的干预措施，该干预措施与患者与治疗相匹配并不断 监视他们对治疗的反应。最后，这个项目很重要，因为它有可能大大 通过确定每个人可能获得最大收益的待遇来加速恢复， 最终有助于解决抑郁症的高人群负担。