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的一部分，有助于结束长期（治愈）倡议，以加快美国阿片类药物公共卫生危机的科学解决方案。NIHHeal Heal Initiative Bolsters跨NIH的研究，以改善对阿片类药物滥用和成瘾的治疗。” 数十年的研究已经确定了早期母亲相互作用对终身适应性社会情感功能的重要性，这对后来的问题行为（包括药物使用障碍）的发展产生了影响。孕产妇对婴儿苦难的敏感性的历史 - 即始终是偶然，养育和适当的反应 - 被认为使婴儿能够将社会情感能力内在化，以维持情感安全状态。这反映在安全母亲依恋和自我调节行为的发展中。反过来，母亲的依恋安全性可预测 童年和青少年行为的星座 - 包括亲子和父母的关系质量，童年的内在化和外在的行为，与同龄人的能力以及学校的成功 - 每种行为都独立地预测了药物滥用和药物使用障碍。该提案的目的是通过开发和验证可用于在标准化协议和每天生态有效的家庭互动中远程评估的移动传感器算法来促进对物质使用障碍的研究和预防性干预的机会。使用婴儿和母亲动作所戴的音频记录以及 接近数据，我们将开发可以将敏感性与对婴儿遇险的不敏感材料反应区分开来的模型。至关重要的是，我们使用来自潜水员家庭的“培训数据”开发模型，包括高风险和低风险的药物使用障碍的家庭，并说英语和西班牙语。这将确保我们的工具将使最需要他们的家庭受益。我们设想，未来的努力可以利用我们的算法来确定对现有基于证据的干预措施的最高需求，以提高孕产妇的敏感性和儿童结果。经过培训后，我们的算法可以集成到“及时”干预措施中，以为参加干预措施的母亲提供实时反馈和进度报告。此外，这些算法对于研究研究的研究将是无价的 早期护理中的挑战发展以及这种挑战如何随着时间而扩大。例如，这些工具可用于观察困难的婴儿特征的作用，例如厌恶或过度的哭泣，母体压力源或吸引物质的渴望以及孕产妇的支持系统，包括父亲的参与。因此，本提案中生产的创新工具都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。