健康焦虑的移动网络化解释偏向矫正: 一项随机对照试验

doi:10.3724/SP.J.1041.2024.01351

摘要/Abstract

摘要：

本研究旨在考察移动网络化解释偏向矫正(internet-based Cognitive Bias Modification-Interpretation, iCBM-I)对健康焦虑的干预效果、影响因素及灾难化解释在干预起效中的机制作用。采用随机对照试验, 将符合健康焦虑标准的被试(N = 228)随机分配到iCBM-I干预组(100%积极反馈, N = 76)、注意控制组(50%积极50%消极反馈, N = 76)以及等待组(N = 76)。干预组和注意控制组进行为期12天的在线任务训练, 等待组不做训练。在干预前、干预后及干预后一个月对被试的健康焦虑、灾难化解释、一般焦虑和抑郁进行测量。结果发现: 与等待组相比, iCBM-I干预对健康焦虑个体的灾难化解释、一般焦虑和抑郁症状存在显著的即时效果和一个月后的追踪效果; 基于潜在增长曲线模型的纵向中介检验显示, 相对于注意控制组, iCBM-I干预通过降低个体的灾难化解释进而改善健康焦虑。本研究为健康焦虑的网络化干预提供了新视角, 未来研究可以考虑联合“自上而下”和“自下而上”的干预方法, 以提高健康焦虑的干预效果。

关键词: 健康焦虑, 解释偏向矫正, 随机对照试验, 潜在增长曲线模型, 纵向中介

Abstract:

Health anxiety, characterized by excessive fear and worry about one's health, is closely linked to interpretation bias as outlined in the comprehensive model of health anxiety. Research suggests that individuals with health anxiety often exhibit catastrophizing interpretations, and mitigating these interpretations can alleviate health anxiety symptoms. This study aims to examine the effects of internet-based cognitive bias modification of interpretation (iCBM-I) training on health anxiety, catastrophizing interpretation, anxiety, and depression symptoms. It also explores the longitudinal mediating mechanism of catastrophizing interpretation, providing insights for internet-based interventions for health anxiety.

In this randomized controlled trial, 228 participants with health anxiety (Short Health Anxiety Inventory [SHAI] scores ≥ 15) were randomly assigned to an iCBM-I group (100% positive feedback, N = 76), an attention control group (50% positive and 50% negative feedback, N = 76), or a wait-list control group (N = 76). Participants in the iCBM-I and attention control groups completed internet-based tasks over 12 days (one session every three days, totaling four sessions). Health anxiety, catastrophizing interpretation, anxiety, and depression were measured at pre-treatment, post-treatment, and a one-month follow-up to assess the immediate and long-term effects of the iCBM-I program.

The intent-to-treat principle was employed, and the generalized linear mixed model analyzed the intervention effects. Two-factor analysis of variance and hierarchical regression examined the moderating effects of demographic and baseline symptom variables. Additionally, the parallel development latent growth curve model analyzed the longitudinal mediating effect of catastrophizing interpretation.

Results indicated that the iCBM-I group with 100% positive feedback showed significant immediate effects on reducing catastrophizing interpretation, anxiety, and depression symptoms compared to the wait-list group. One-month follow-up results revealed significant effects on health anxiety, catastrophizing interpretation, anxiety, and depression. Marital status, baseline health anxiety symptoms, and catastrophizing interpretation moderated the iCBM-I intervention effects, with catastrophizing interpretation serving as a longitudinal mediator.

This study developed an iCBM-I self-help intervention mini-program for health anxiety, demonstrating its feasibility, efficacy, and psychological mechanisms. The findings provide empirical support for cognitive theories of emotion and offer a novel perspective for networked interventions in health anxiety. Future research should consider integrating “top-down” and “bottom-up” combined intervention therapies to further investigate their effects and mechanisms in health-anxious individuals.

Key words: health anxiety, cognitive bias modification of interpretation, randomized controlled trials, latent growth curve model, longitudinal mediation

中图分类号:

R395

杜夏雨, 赖丽足, 史从戎, 郭子涵, 韩菁, 张涛, 任志洪. (2024). 健康焦虑的移动网络化解释偏向矫正: 一项随机对照试验. 心理学报, 56(10), 1351-1366.

DU Xiayu, LAI Lizu, SHI Congrong, GUO Zihan, HAN Jing, ZHANG Tao, REN Zhihong. (2024). Internet-based cognitive bias modification of interpretation in health anxiety: A randomized controlled trial. Acta Psychologica Sinica, 56(10), 1351-1366.

图/表 12

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结果变量		干预组(n = 76)		控制组(n = 76)		等待组(n = 76)		组间差异	组间效应量(Cohen's d, 95% CI)
结果变量		M	SD	M	SD	M	SD	F (2, 671)	干预组vs.等待组	控制组vs.等待组	干预组vs.控制组
SHAI	基线	27.64	8.53	28.20	9.95	29.45	8.75	0.852	−0.21(−0.53, 0.11)	−0.14(−0.46, 0.17)	−0.07(−0.38, 0.25)
	后测	22.41	9.03	26.64	9.41	24.27	8.51	4.767^**	−0.21(−0.53, 0.11)	0.27(−0.05, 0.59)	−0.50(−0.82, −0.18)
	追踪	19.98	9.14	22.17	8.92	25.11	8.69	6.574^**	−0.59(−0.91, −0.26)	−0.34(−0.66, −0.02)	−0.26(−0.58, 0.06)
CABAH	基线	36.54	6.07	36.79	6.40	35.00	6.31	2.343	0.28(−0.04, 0.60)	0.33(0.01, 0.65)	−0.05(−0.36, 0.27)
	后测	29.22	6.74	32.22	6.47	33.77	5.60	13.835^***	−0.83(−1.16, −0.50)	−0.28(−0.60, 0.04)	−0.56(−0.89, −0.24)
	追踪	29.87	7.30	32.48	6.20	34.05	5.97	11.471^***	−0.76(−1.09, −0.44)	−0.29(−0.61, 0.03)	−0.49(−0.81, −0.17)
PHQ-9	基线	19.12	4.95	19.80	5.71	20.33	4.97	1.063	−0.23(−0.55, 0.08)	−0.10(−0.42, 0.22)	−0.14(−0.45, 0.18)
	后测	15.79	4.12	17.29	5.46	20.59	4.74	17.244^***	−0.94(−1.27, −0.60)	−0.64(−0.97, −0.32)	−0.30(−0.62, 0.02)
	追踪	16.72	4.35	18.26	5.75	20.64	4.10	11.212^***	−0.77(−1.10, −0.44)	−0.46(−0.79, −0.14)	−0.31(−0.63, 0.01)
GAD-7	基线	15.31	4.50	15.36	4.72	15.98	4.66	0.510	−0.15(−0.47, 0.17)	−0.14(−0.45, 0.18)	−0.01(−0.33, 0.31)
	后测	12.46	3.95	14.09	4.60	15.26	3.92	7.536^**	−0.62(−0.95, −0.30)	−0.26(−0.58, 0.06)	−0.38(−0.70, −0.06)
	追踪	12.71	4.36	13.55	4.42	15.23	3.75	6.103^**	−0.56(−0.88, −0.24)	−0.37(−0.69, −0.05)	−0.19(−051, 0.12)

结果变量		干预组(n = 76)		控制组(n = 76)		等待组(n = 76)		组间差异	组间效应量(Cohen's d, 95% CI)
结果变量		M	SD	M	SD	M	SD	F (2, 671)	干预组vs.等待组	控制组vs.等待组	干预组vs.控制组
SHAI	基线	27.64	8.53	28.20	9.95	29.45	8.75	0.852	−0.21(−0.53, 0.11)	−0.14(−0.46, 0.17)	−0.07(−0.38, 0.25)
	后测	22.41	9.03	26.64	9.41	24.27	8.51	4.767^**	−0.21(−0.53, 0.11)	0.27(−0.05, 0.59)	−0.50(−0.82, −0.18)
	追踪	19.98	9.14	22.17	8.92	25.11	8.69	6.574^**	−0.59(−0.91, −0.26)	−0.34(−0.66, −0.02)	−0.26(−0.58, 0.06)
CABAH	基线	36.54	6.07	36.79	6.40	35.00	6.31	2.343	0.28(−0.04, 0.60)	0.33(0.01, 0.65)	−0.05(−0.36, 0.27)
	后测	29.22	6.74	32.22	6.47	33.77	5.60	13.835^***	−0.83(−1.16, −0.50)	−0.28(−0.60, 0.04)	−0.56(−0.89, −0.24)
	追踪	29.87	7.30	32.48	6.20	34.05	5.97	11.471^***	−0.76(−1.09, −0.44)	−0.29(−0.61, 0.03)	−0.49(−0.81, −0.17)
PHQ-9	基线	19.12	4.95	19.80	5.71	20.33	4.97	1.063	−0.23(−0.55, 0.08)	−0.10(−0.42, 0.22)	−0.14(−0.45, 0.18)
	后测	15.79	4.12	17.29	5.46	20.59	4.74	17.244^***	−0.94(−1.27, −0.60)	−0.64(−0.97, −0.32)	−0.30(−0.62, 0.02)
	追踪	16.72	4.35	18.26	5.75	20.64	4.10	11.212^***	−0.77(−1.10, −0.44)	−0.46(−0.79, −0.14)	−0.31(−0.63, 0.01)
GAD-7	基线	15.31	4.50	15.36	4.72	15.98	4.66	0.510	−0.15(−0.47, 0.17)	−0.14(−0.45, 0.18)	−0.01(−0.33, 0.31)
	后测	12.46	3.95	14.09	4.60	15.26	3.92	7.536^**	−0.62(−0.95, −0.30)	−0.26(−0.58, 0.06)	−0.38(−0.70, −0.06)
	追踪	12.71	4.36	13.55	4.42	15.23	3.75	6.103^**	−0.56(−0.88, −0.24)	−0.37(−0.69, −0.05)	−0.19(−051, 0.12)

模型	χ²(df)	χ²/df	SRMR	CFI	AIC	BIC
参照	—	< 3	< 0.08	> 0.95	—	—
SHAI
无增长模型	98.29(13)^***	7.56	0.25	0.82	4886.13	4907.29
线性增长模型	49.61(10)^***	4.96	0.14	0.92	4843.45	4873.69
二次增长模型	11.26(6)	1.88	0.06	0.99	4813.09	4855.43
潜在基础增长模型	33.31(7)^***	4.76	0.04	0.95	4833.15	4872.46
CABAH
无增长模型	96.27(13)^***	7.41	0.27	0.84	4308.65	4329.82
线性增长模型	28.63(10)^**	2.86	0.11	0.96	4247.01	4277.25
二次增长模型	10.24(6)	1.71	0.05	0.99	4236.61	4278.95
潜在基础增长模型	14.70(7)^*	2.10	0.03	0.99	4239.08	4278.39

模型	χ²(df)	χ²/df	SRMR	CFI	AIC	BIC
参照	—	< 3	< 0.08	> 0.95	—	—
SHAI
无增长模型	98.29(13)^***	7.56	0.25	0.82	4886.13	4907.29
线性增长模型	49.61(10)^***	4.96	0.14	0.92	4843.45	4873.69
二次增长模型	11.26(6)	1.88	0.06	0.99	4813.09	4855.43
潜在基础增长模型	33.31(7)^***	4.76	0.04	0.95	4833.15	4872.46
CABAH
无增长模型	96.27(13)^***	7.41	0.27	0.84	4308.65	4329.82
线性增长模型	28.63(10)^**	2.86	0.11	0.96	4247.01	4277.25
二次增长模型	10.24(6)	1.71	0.05	0.99	4236.61	4278.95
潜在基础增长模型	14.70(7)^*	2.10	0.03	0.99	4239.08	4278.39

段落/标题		条目号	核查单条目描述	页码
标题和摘要
		1a	文题中能确认是随机化的试验	p1351
		1b	用结构式摘要概括试验设计、方法、结果和结论	p1351
引言
	背景和目的	2a	研究课题的科学背景和原理的解释	p1351−1353
		2b	研究课题的特定目的或假设	p1353
方法
	试验设计	3a	描述包括分配比例的试验设计(例如平行设计、析因设计)	p1354
		3b	试验启动后方法上的重要改变及理由(例如合格标准)	NA
	受试者	4a	参加者的合格标准	p1354
		4b	资料收集的环境和地点	p1354
	干预方法	5	各组干预措施的详细情况以及实际实施的方法和时间, 以便重复试验	p1354−1356
	结局指标	6a	明确定义事先确定的主要和次要结局指标, 包括测评的方法和时间	p1354
		6b	试验启动后试验结局的任何改变及理由	NA
	样本量	7a	样本量是如何确定的	p1354
		7b	进行任何的中期分析都应给予解释, 并给出终止试验的原则	NA
	随机序列生成	8a	用于产生随机分配顺序的方法	p1355
		8b	随机化类型, 任何限定细节(例如区组化和各区组样本大小)	p1355
	分配隐藏	9	用于实施随机分配顺序的方法(例如按顺序编号的容器), 说明分配干预之前所采取隐藏顺序的步骤	p1355
	实施	10	谁产生的分配顺序, 谁入选的受试者, 谁将受试者分到各干预组	p1355
	盲法	11a	如果使用了盲法, 分配干预后谁处于盲态(例如受试者、保健提供者和结局评估者)	p1355
		11b	描述干预的相似性情况	p1355
	统计学方法	12a	用于比较各组主要和次要结局的统计学方法	p1356
		12b	额外分析方法, 例如亚组分析和校正分析	p1356−1357
结果
	受试者纳入流程	13a	每组被随机分配、接受预期处理及分析主要结局的人数	p1357
		13b	每组随机化后丢失和剔除的情况及理由	NA
	招募情况	14a	用日期来明确招募和随访的时间	p1355
		14b	为什么试验结束了或者暂停了下来	NA
	基线数据	15	用表格显示各组的基线人口统计学资料和临床特征	p1358
	纳入分析的受试者数量	16	分析每次纳入到各组的受试者例数(分母), 而不管是否用了原来的分组	p1357−1358
	结局和效应估计	17a	各组每一个主要和次要结局结果、估计的效应大小及其精度(例如95%置信区间)	p1357−1358
		17b	对两分类的结局, 推荐绝对的和相对的效应大小两者兼用	p1357−1358
	辅助分析	18	报告所进行的任何其他分析, 包括亚组分析、校正分析, 并区分开哪些是预定的?哪些是探索性的?	p1358−1360
	不良反应	19	每组所有的重要危害或非预期的效应	NA
讨论
	局限性	20	试验的局限性, 说明潜在偏倚、不够准确的来源, 可能还有分析的多样性	p1362
	可推广性	21	试验结果的可推广性(外部有效性、适用性)	p1368
	解释	22	给出与结果一致的解释, 在受益和伤害间进行平衡, 考虑其他的相关证据	p1360−1362
其他信息
	注册	23	试验注册的注册号和名称	p1354
	研究方案	24	需要时在哪里可以获取完整的试验方案	p1354−1356
	基金资助	25	资助的来源和其他的支持(例如药品供应), 资助者的作用	p1351