運用心率變化評估焦慮：以幼兒說謊為例

Shuhui Chiu; Tsu-Te Chuang Hu

doi:10.6209/JORIES.202503_70(1).0004

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社会科学 > 教育学

DOI： 10.6209/JORIES

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运用心率变化评估焦虑：以幼儿说谎为例

Recognizing Young Children's Anxiety While Lying: Utilizing Heart Rate Data From a Smart Bracelet

邱淑惠(Shuhui Chiu) ；庄胡祖德(Tsu-Te Chuang Hu)

教育科学研究期刊； 70 卷 1 期 (2025 / 03 / 01) ， P115 - 141

繁体中文;英文 DOI： 10.6209/JORIES.202503_70(1).0004

心率；心率手环；焦虑；说谎；学龄前幼儿； heart rate ； smart bracelet ； anxiety ； lying ； preschoolers

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摘要 │ 参考文献 (32)

摘要〈TOP〉

并列摘要〈TOP〉

Motivation and Purpose: Preschool children and those with special needs often struggle to articulate their anxiety clearly. Identifying anxiety through a simple, non-invasive, and valid physiological indicator would be invaluable in educational, counseling, and academic research fields. This study aims to propose practical applications such as using a smart bracelet with alert settings for children with emotional regulation difficulties. The monitors could warn teachers or the children themselves when heart rates exceed a specific threshold, indicating potential emotional distress. This study compares the heart rate variations of preschool children across different phases of an experiment. The primary objectives are: (1) to investigate the heart rate changes in lying versus honest children during the four experimental phases (baseline, play, alone, response), and (2) to utilize data from heart rate monitors to establish threshold values, distinguishing between calm (baseline) and anxious (alone, response) states. Literature Review: The Polyvagal Theory describes how the autonomic nervous system responds to various environmental contexts, influencing subsequent physiological responses such as heart rate (Porges, 2009). When environmental threats increase, the ventral vagal complex is suppressed, leading to the dominance of the sympathetic nervous system, resulting in an accelerated heart rate. Children as young as three and a half years old begin to lie (Evans & Lee, 2011). In experimental settings, spontaneous lying behaviors in children are often studied through a temptation scenario, such as leaving the child alone after instructing them not to peek at a noise-making toy (Lewis et al., 1989) or to refrain from looking at a covered card to win a game (Zhao et al., 2017). Lying can induce anxiety and heart rate changes, with heart rate variability serving as an indicator of anxiety levels. Methodology: This study invited 91 children (45 girls and 46 boys) aged 5.43 to 6.49 years (M = 5.93) to participate in a game. Based on the card game scenario from Zhao et al. (2017), the experimental setup was divided into four phases: baseline (sitting quietly before the game), play (introduction of the game), alone (researcher leaves the room), and response (researcher asks if the child peeked at the cards). Children were categorized into lying and honest groups based on their spontaneous behavior. Research Results: 1. Overall Heart Rate Variability: The heart rate variability was significantly higher during the alone and response phases compared to the play and baseline phases. The findings support the feasibility of using heart rate to detect anxiety in young children. 2. Distinguishing Lying and Honest Groups: The heart rate variations in different phases could not reliably distinguish between lying and honest children. This could be due to greater within-subject variability in heart rates than between-group differences or some honest children experiencing anxiety in the later phases. 3. Individual Heart Rate Comparison: Comparing a child's heart rate against their baseline average and standard deviation is essential to determine anxiety states. This supports the notion that heart rate and its variability are influenced by individual physiological traits (Meijer & Verschuere, 2018). The variability in baseline heart rates predicted subsequent heart rate changes, with greater baseline variability indicating a higher likelihood of anxiety-induced heart rate increases. 4. Effect Sizes of Phase and Baseline Heart Rate: Both the phase factor and the baseline heart rate standard deviation (as a covariate) had small effect sizes on subsequent heart rates. The effect size of the phase (representing context) was slightly higher than that of the covariate (representing physiological traits). This suggests that situational factors have a more substantial impact on heart rate changes than personal traits in this study. 5. Maximum Heart Rate Variability: The highest heart rate variability during the alone phase distinguished between the honest and lying groups. Honest children showed greater variability, possibly due to internal struggles and anxiety while deciding whether to peek, or because some waited calmly without intent to cheat. The lying group was more uniformly anxious about deciding to cheat. 6. Minimum Heart Rate: Minimum heart rates showed significant differences across all phases, indicating a consistent upward trend. According to The Polyvagal Theory (Porges, 1995), as anxiety increases, the minimum heart rate stabilizes and rises unless controlled by the dorsal vagal complex, which lowers the heart rate. Therefore, examining maximum and minimum heart rates, in addition to the average, provides richer information. 7. Adjusting Heart Rate Alert Thresholds: Results indicated that using the formula "baseline maximum heart rate + 0.2 * (baseline maximum heart rate - baseline average)" produced similar effects to Lee et al.'s (2010) "baseline average + 2SD" but both were slightly lenient. In practical applications, thresholds can be flexibly adjusted according to individual children's traits or specific usage purposes, tailoring appropriate alert values for each child. Discussion and Recommendations: This study employed an experimental setting to tempt children to lie. The findings show that children's heart rates significantly increased when they had the opportunity to cheat and subsequently lie to cover up their actions, compared to their calm states. This demonstrates the practical utility of using heart rate changes to detect emotional shifts in children. Additionally, the study found that real-time data from heart rate bracelets, including the average and maximum heart rates during calm states, can be used to set alert thresholds with similar effectiveness to the standards used by Lee et al. (2010). Exploring the use of heart rate to detect anxiety can expand its application range. For example, during play therapy with traumatized children, monitoring their emotional fluctuations with heart rate bracelets can be beneficial. Similarly, pairing heart rate bracelets with behavioral training techniques for children with emotional disorders can help manage anxiety. Setting alert thresholds on the bracelets can teach children to pause activities and use pre-taught emotional regulation strategies when alerted. It can also prompt caregivers to identify environmental factors that may cause anxiety, enabling preventive measures. Most children's heart rates did not significantly spike, indicating that the experimental setting induced only mild anxiety. However, in practical applications, severe anxiety might cause the dorsal vagal complex to take over the autonomic nervous system, leading to a sudden drop in heart rate. Therefore, setting minimum heart rate thresholds is also crucial. Lee et al. (2010) used the baseline average heart rate minus two standard deviations as an alert threshold. While this study's data do not provide specific recommendations for this, practitioners can experiment with this approach. Additionally, the experience of lying might predict heart rate changes across different phases, a variable not considered in this study due to a lack of relevant data. Future research should include this variable. Lastly, factors other than anxiety, such as excitement, can also increase heart rates, which may explain the significant rise in heart rates during the play phase in this study.

参考文献 ( 32 ) 〈TOP〉

庄胡祖德, T.-T.,邱淑惠, S.(2023)。说谎与认知弹性：幼儿头脑灵活有助说谎？教育学报。教育学报，51(2)，187-205。
连结：
Akar, S. A.,Kara, S.,Agambayev, S.,Bilgiç, V.(2015).Nonlinear analysis of EEGs of patients with major depression during different emotional states.Computers in Biology and Medicine,67,49-60.
Bailenson, J. N.,Pontikakis, E. D.,Mauss, I. B.,Gross, J. J.,Jabon, M. E.,Hutcherson, C. A.,Nass, C.,John, O.(2008).Real-time classification of evoked emotions using facial feature tracking and physiological responses.International Journal of Human-Computer Studies,66(5),303-317.
Bowen, M.(1978).Family therapy in clinical practice.Rowman & Littlefield.
Brown, C. L.,Van Doren, N.,Ford, B. Q.,Mauss, I. B.,Sze, J. W.,Levenson, R. W.(2020).Coherence between subjective experience and physiology in emotion: Individual differences and implications for well-being.Emotion,20(5),818-829.

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