Emezie Amarachi, Kamel Rima, Dunphy Morgan, Young Amanda, Nuske Heather J
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Psychiatry, Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sensors (Basel). 2024 Dec 16;24(24):8024. doi: 10.3390/s24248024.
Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents. This study examined whether heart rate data can predict strategy effectiveness. Teachers and coders from the research team recorded behavioral and heart rate data over three months for each participating student on the autism spectrum using the KeepCalm app, a platform that provides in-the-moment strategy suggestions based on heart rate and past behavioral data, across 226 instances of strategy interventions. A binary logistic regression was performed to assess whether heart rate reduction, time to return to heart rate baseline, and documented skills and challenging behaviors predicted strategy effectiveness. Results suggested that heart rate reduction may be a significant predictor, and supported the existing practice of using behavioral patterns as proxies for strategy effectiveness. Additional analyses indicate proactive strategies are more effective and are associated with greater reduction in heart rate, relative to reactive strategies. Further exploration of how internal physiological data can complement observable behaviors in assessing intervention strategy effectiveness is warranted given the novelty of our findings.
许多自闭症谱系障碍儿童由于沟通困难和难以调节压力，会出现一些具有挑战性的行为，比如攻击行为。确定有效的干预策略往往具有主观性且耗时。利用不可观察的内部生理数据来预测策略效果，可能有助于为教师和家长简化这一过程。本研究考察了心率数据是否能够预测策略效果。研究团队的教师和编码人员使用KeepCalm应用程序，在三个月的时间里记录了每位参与研究的自闭症谱系障碍学生的行为和心率数据。KeepCalm应用程序是一个平台，它根据心率和过去的行为数据提供即时策略建议，涵盖226次策略干预实例。进行了二元逻辑回归分析，以评估心率下降、恢复到心率基线的时间，以及记录的技能和具有挑战性的行为是否能预测策略效果。结果表明，心率下降可能是一个重要的预测指标，并支持了将行为模式作为策略效果替代指标的现有做法。进一步的分析表明，与被动策略相比，主动策略更有效，且与更大程度的心率下降相关。鉴于我们研究结果的新颖性，有必要进一步探索内部生理数据如何在评估干预策略效果时补充可观察到的行为。
