Sokoya Temiloluwa, Zhou Yuchun, Diaz Sebastian, Law Timothy D, Himawan Lina, Lekey Francisca, Shi Lu, Griffin Sarah F, Gimbel Ronald W, Jing Xia
Department of Behavioral and Environmental Health, College of Health Sciences, Jackson State University, Jackson, MS, United States.
Gladys W and David H Patton College of Education, Ohio University, Athens, OH, United States.
JMIR Public Health Surveill. 2025 Sep 8;11:e65616. doi: 10.2196/65616.
Health indicators can facilitate the assessment of individual and population health status and are valuable in health care and public health settings. However, it is challenging to collect all measures at once. There is no single, objective, comprehensive, accurate, easy-to-use measure for individual health status yet, and we aim to develop one. We identified 29 health indicators via literature review and ranked their importance through public ratings to start the effort.
This study aims to explore the effects of 4 demographic variables (age, sex, professional group, and educational level) on the importance ratings of the 29 indicators.
This was a cross-sectional study. Online surveys were administered on 2 US college campuses and through ResearchMatch, an online platform specialized in finding clinical research participants. Only individuals 18 years or older were considered for inclusion as respondents. A 4-way multivariate analysis of variance (MANOVA) with post hoc testing was conducted to determine the effects of 4 demographic factors on the 29 health indicators. Descriptive statistics and bivariate correlation analysis were reported, along with the results of MANOVA with post hoc testing.
The study included 1153 participants from the United States of America with 1144 complete responses. Our study found significant correlations among the 29 health indicators. Age, education, sex, and professional groups showed correlations with multiple health indicators. Correlation analysis indicated that age was correlated with 16 of the 29 health indicators, followed by education (9 indicators), sex (8 indicators), and professional group (7 indicators). MANOVA modeling revealed that age (partial eta squared=0.058; P<.001), sex (partial eta squared=0.05; P<.001), professional group (partial eta squared=0.049; P<.001), and educational levels (partial eta squared=0.047; P<.001), as well as the interactions between sex and professional group (partial eta squared=0.044; P<.001) and sex and age (partial eta squared=0.041; P<.001), showed significant effects on the combination of the 29 health indicators. In general, female participants provided significantly higher ratings on 10 health indicators than male participants (P<.05), whereas older participants provided significantly higher ratings on 20 health indicators than younger participants (P<.05).
Age emerged as a critical factor influencing the ratings of the 29 health indicators and showed the largest effect size along with sex, professional group, and educational level. These findings can be used as a quantitative foundation for the development of an objective measure for individual health status, for grouping health indicators, for conducting demographic factor adjustments during data analysis, for designing customized behavioral intervention studies based on demographic factors, or for health policy-making or program development.
健康指标有助于评估个人和人群的健康状况，在医疗保健和公共卫生领域具有重要价值。然而，一次性收集所有指标具有挑战性。目前尚无单一、客观、全面、准确且易于使用的个人健康状况衡量指标，我们旨在开发这样一种指标。我们通过文献综述确定了29项健康指标，并通过公众评分对其重要性进行排序，以启动这项工作。
本研究旨在探讨4个人口统计学变量（年龄、性别、职业群体和教育水平）对29项指标重要性评分的影响。
这是一项横断面研究。在美国的2个大学校园以及通过专门寻找临床研究参与者的在线平台ResearchMatch进行了在线调查。仅纳入18岁及以上的个人作为受访者。进行了带有事后检验的4因素多变量方差分析（MANOVA），以确定4个人口统计学因素对29项健康指标的影响。报告了描述性统计和双变量相关性分析，以及带有事后检验的MANOVA结果。
该研究包括来自美利坚合众国的1153名参与者，其中1144份回复完整。我们的研究发现29项健康指标之间存在显著相关性。年龄、教育程度、性别和职业群体与多项健康指标存在相关性。相关性分析表明，年龄与29项健康指标中的16项相关，其次是教育程度（9项指标）、性别（8项指标）和职业群体（7项指标）。MANOVA建模显示，年龄（偏η² = 0.058；P <.001）、性别（偏η² = 0.05；P <.001）、职业群体（偏η² = 0.049；P <.001）和教育水平（偏η² = 0.047；P <.001），以及性别与职业群体之间的交互作用（偏η² = 0.044；P <.001）和性别与年龄之间的交互作用（偏η² = 0.041；P <.001），对29项健康指标的组合均有显著影响。总体而言，女性参与者对10项健康指标的评分显著高于男性参与者（P <.05），而年龄较大的参与者对20项健康指标的评分显著高于年龄较小的参与者（P <.05）。
年龄是影响29项健康指标评分的关键因素，与性别、职业群体和教育水平一样，其效应量最大。这些发现可为开发个人健康状况的客观衡量指标、对健康指标进行分组、在数据分析过程中进行人口统计学因素调整、基于人口统计学因素设计定制的行为干预研究或制定健康政策或项目提供定量基础。
