Latent classes and symptom network analysis of kinesiophobia in older adult patients with chronic obstructive pulmonary disease.

IF 3.6 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Frontiers in Medicine Pub Date : 2026-10-05 eCollection Date: 2026-01-01 DOI:10.3389/fmed.2026.1919256
Mengyao Liang, Congying Kang, Li Feng, Jiajia Hua
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Abstract

Objective: To identify the latent classes of kinesiophobia in older adult patients with chronic obstructive pulmonary disease (COPD), analyze the symptom network structure of different classes, determine the core symptoms, and provide evidence for precision pulmonary rehabilitation interventions.

Methods: A convenience sample of 546 older adult COPD patients was recruited from the Department of Respiratory and Critical Care Medicine, Nantong Sixth People's Hospital, between March 2025 and April 2026. Participants completed a general information questionnaire, the Breathlessness Beliefs Questionnaire (BBQ), and a COPD symptom survey. Latent profile analysis was used to identify potential classes of kinesiophobia. Symptom networks for each subgroup were constructed using R software, and three centrality indices (strength, closeness, and betweenness) were calculated to determine core symptoms.

Results: The prevalence of kinesiophobia in older adult patients with COPD was 88.46%. Latent profile analysis identified two classes: a high-fear-somatic focus group (67.4%) and a lower-fear-activity avoidance group (32.6%). Symptom network analysis revealed that the core symptom was dyspnea (strength = 1.78) in the high-fear group, whereas fatigue (strength = 1.64) was the core symptom in the lower-fear group. The difference in network structure between the two groups was not statistically significant (P > 0.05).

Conclusion: Kinesiophobia in older adult patients with COPD exhibits population heterogeneity, with different core symptoms across subgroups. The findings of this study suggest that dyspnea deserves particular attention in the high-fear group, while fatigue deserves particular attention in the lower-fear group. Future longitudinal or interventional studies are needed to further verify the causal relationships between core symptoms and pulmonary rehabilitation participation, thereby informing the development of individualized pulmonary rehabilitation strategies.

老年慢性阻塞性肺疾病患者运动恐惧症的潜在分类和症状网络分析。
目的:识别老年慢性阻塞性肺疾病(COPD)患者运动恐惧症的潜在类型,分析不同类型的症状网络结构,确定核心症状,为精准肺康复干预提供依据。方法:于2025年3月至2026年4月从南通市第六人民医院呼吸与重症医学科招募546例老年COPD患者作为方便样本。参与者完成了一般信息问卷、呼吸困难信念问卷(BBQ)和COPD症状调查。潜在剖面分析用于识别运动恐惧症的潜在类别。使用R软件构建每个亚组的症状网络,并计算三个中心性指数(强度、亲密度和中间度)以确定核心症状。结果:老年COPD患者运动恐惧症患病率为88.46%。潜在特征分析确定了两类:高恐惧-躯体焦点组(67.4%)和低恐惧-活动回避组(32.6%)。症状网络分析显示,高恐惧组的核心症状为呼吸困难(强度= 1.78),低恐惧组的核心症状为疲劳(强度= 1.64)。两组间网络结构差异无统计学意义(P < 0.05)。结论:老年COPD患者的运动恐惧症表现出人群异质性,不同亚组的核心症状不同。本研究结果提示,呼吸困难在高恐惧组中值得特别注意,而疲劳在低恐惧组中值得特别注意。未来的纵向或介入性研究需要进一步验证核心症状与肺康复参与之间的因果关系,从而为个性化肺康复策略的制定提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Medicine
Frontiers in Medicine Medicine-General Medicine
CiteScore
5.10
自引率
5.10%
发文量
3710
审稿时长
12 weeks
期刊介绍: Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world
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