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 帮助结束成瘾长期 (HEAL) 计划的一部分，该计划旨在加快对国家阿片类药物公共卫生危机的科学解决方案。NIH HEAL 计划支持 NIH 的研究，以改善阿片类药物滥用和成瘾的治疗。” 数十年的研究已经证实，早期母婴互动对于终生适应性社会情感功能的重要性，对后来问题行为的发展（包括物质使用障碍）有影响。母亲对婴儿痛苦的敏感历史，即始终如一的反应。偶然性、养育性，以及——被认为可以让婴儿内化适当的社会情感能力，以维持情感安全状态，这反映在安全的母婴依恋和自我调节行为的发展上，进而体现出母婴依恋安全感。是预测一个 儿童期和青少年期的一系列行为——包括亲子和亲子关系质量、童年内化和外化行为、与同龄人相处的能力以及学业成功——每一项都独立预测药物滥用和药物使用障碍。旨在通过开发和验证移动传感器算法来促进药物使用障碍代际传播的研究和预防性干预的机会，这些算法可用于使用婴儿佩戴的录音机远程评估标准化协议和日常生态家庭有效互动中的母亲敏感性。和母婴运动和 重要的是，我们利用来自不同家庭样本的“训练数据”来开发我们的模型，其中包括物质使用高风险和低风险的家庭。这将确保我们的工具将使最需要它们的家庭受益。我们预计未来的工作可以利用我们的算法来识别最需要现有循证干预措施的家庭，以提高母亲的敏感性。一旦经过训练，我们的算法就可以集成到其中。 “及时”干预措施可以为参与干预措施的母亲提供实时反馈和进度报告。此外，这些算法对于检查干预措施的研究非常有价值。 例如，这些工具可用于观察婴儿困难特征的作用，例如厌恶或过度哭泣、母亲压力源或物质使用渴望以及母亲支持系统。 ，包括父亲的参与，因此，本提案中产生的创新工具将有助于现实世界的公共卫生工作，并扩大对早期儿童发展动态的研究。