Automated Assessment of Maternal Sensitivity to Infant Distress: Leveraging Wearable Sensors for Substance Use Disorder Prevention and Research

自动评估母亲对婴儿痛苦的敏感性：利用可穿戴传感器进行药物滥用障碍预防和研究

• 批准号: 10777818
• 负责人: Kaya de Barbaro
• 金额: $ 49.93万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10777818
• 关键词: Adolescence; Adolescent; Algorithms; Assessment tool; Back; Behavior; Behavior assessment; Characteristics; Child; Child Abuse and Neglect; Child Development; Childhood; Classification; Communities; Competence; Consumption; Crying; Data; Data Pooling; Detection; Development; Distress; Emotional; Ensure; Evidence based intervention; Family; Feedback; Future; Goals; Home; Home environment; Individual; Infant; Infant Behavior; Informal Social Control; Intervention; Maps; Modeling; Mothers; Motion; Outcome; Parents; Performance; Play; Population; Prevention; Problem Solving; Problem behavior; Process; Progress Reports; Protocols documentation; Public Health; Recording of previous events; Research; Risk; Risk Factors; Risk Reduction Behavior; Role; Sampling; Schools; Secure; Security; Source; Standardization; Substance Use Disorder; Substance abuse problem; Support System; Target Populations; Testing; Time; Training; Validation; Variant; Woman; Work; caregiving; craving; density; detection sensitivity; disorder prevention; disorder risk; diverse data; emotional functioning; experience; externalizing behavior; flexibility; high risk; improved; innovation; intergenerational; lifetime risk; mobile sensor; next generation; novel; peer; preventive intervention; recruit; remote assessment; remote sensor; response; self reliance; sensor; social; stressor; substance use; success; tool; transmission process; wearable sensor technology

“This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.” Decades of research have established the importance of early mother-infant interactions for lifelong adaptive social emotional functioning, with implications for the development of later problem behaviors including substance use disorder. A history of maternal sensitivity to infant distress – that is, responses that are consistently contingent, nurturing, and appropriate – is thought to allow infants to internalize socioemotional competencies for maintaining states of emotional security. This is reflected in the development of secure mother-infant attachment and self-regulation behaviors. In turn, mother-infant attachment security is predictive of a constellation of behaviors in childhood and adolescence – including parent-child and parent-adolescent relationship quality, childhood internalizing and externalizing behaviors, competence with peers, and school success – each of which independently predict substance abuse and substance use disorder. The objective of this proposal is to advance opportunities for research and preventative interventions for the intergenerational transmission of substance use disorders by developing and validating mobile sensor algorithms that can be used to remotely assess maternal sensitivity in both standardized protocols and everyday ecologically valid home interactions. Using audio recorders worn by infants and mother-infant motion and proximity data, we will develop models that can distinguish sensitive from insensitive maternal responses to infant distress. Critically, we develop our models with “training data” from a diverse sample of families, including families at both high- and low- risk for substance use disorders and who speak both English and Spanish. This will ensure that our tools will benefit the families who need them the most. We envision that future efforts could leverage our algorithms to identify families at greatest need for existing evidence-based interventions to improve maternal sensitivity and child outcomes. Once trained, our algorithms could be integrated into “just in time” interventions to provide real-time feedback and progress reports for mothers participating in interventions. Additionally, these algorithms will be invaluable to research examining the development of challenges in early caregiving and how such challenges can become amplified over time. For example, these tools could be used to observe the role of difficult infant characteristics, like aversive or excessive crying, maternal stressors or substance use cravings, and maternal support systems, including paternal involvement. As such, the innovative tools produced in the present proposal will both contribute to real-world public health efforts and expand research on the dynamics of early child development.

“这项研究是NIH帮助结束长期成瘾（HEAL）计划的一部分，旨在加快国家阿片类药物公共卫生危机的科学解决方案。NIH HEAL倡议支持整个NIH的研究，以改善阿片类药物滥用和成瘾的治疗。 几十年的研究已经确立了早期母婴互动对终身适应性社会情绪功能的重要性，并对后期问题行为（包括物质使用障碍）的发展产生影响。母亲对婴儿痛苦的敏感史--即一贯的偶然、养育和适当的反应--被认为允许婴儿内化社会情感能力，以维持情感安全的状态。这反映在安全的母婴依恋和自我调节行为的发展。反过来，母婴依恋安全性是一个预测， 儿童期和青少年期的一系列行为--包括亲子关系和亲子青少年关系质量、儿童期的内化和外化行为、与同伴的能力以及学校的成功--每一项都能独立预测物质滥用和物质使用障碍。该提案的目的是通过开发和验证移动的传感器算法来促进物质使用障碍代际传播的研究和预防性干预措施的机会，这些算法可用于远程评估标准化协议和日常生态有效的家庭互动中的母亲敏感性。使用婴儿佩戴的录音机和母婴运动， 通过邻近数据，我们将开发模型，可以区分敏感和不敏感的母亲对婴儿痛苦的反应。至关重要的是，我们使用来自不同家庭样本的“训练数据”来开发我们的模型，这些家庭包括物质使用障碍的高风险和低风险家庭，以及会说英语和西班牙语的家庭。这将确保我们的工具将使最需要它们的家庭受益。我们设想，未来的努力可以利用我们的算法来确定最需要现有循证干预措施的家庭，以改善孕产妇的敏感性和儿童的结局。一旦经过训练，我们的算法可以集成到“及时”干预中，为参与干预的母亲提供实时反馈和进度报告。此外，这些算法将是非常宝贵的研究检查 在早期创业中的挑战的发展，以及这些挑战如何随着时间的推移而扩大。例如，这些工具可以用来观察困难的婴儿特征的作用，如厌恶或过度哭泣，母亲的压力或物质使用的渴望，和母亲的支持系统，包括父亲的参与。因此，本提案中提出的创新工具将有助于现实世界的公共卫生工作，并扩大对幼儿发展动态的研究。