Summary Measure of Health-Related Quality of Life and Its Related Factors Based on the Chinese Version of the Core Healthy Days Measures: Cross-Sectional Study Article Swipe
Yulin Shi
,
Baohua Wang
,
Jian Zhao
,
Chunping Wang
,
Ning Li
,
Min Chen
,
Xia Wan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/52019
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/52019
Background The core Healthy Days measures were used to track the population-level health status in the China Chronic Disease and Risk Factor Surveillance; however, they were not easily combined to create a summary of the overall health-related quality of life (HRQOL), limiting this indicator’s use. Objective This study aims to develop a summary score based on the Chinese version of the core Healthy Days measures (HRQOL-5) and apply it to estimate HRQOL and its determinants in a Chinese population. Methods From November 2018 to May 2019, a multistage stratified cluster survey was conducted to examine population health status and behavioral risk factors among the resident population older than 15 years in Weifang City, Shandong Province, China. Both exploratory factor analyses and confirmatory factor analyses were performed to reveal the underlying latent construct of HRQOL-5 and then to quantify the overall HRQOL by calculating its summary score. Tobit regression models were finally carried out to identify the influencing factors of the summary score. Results A total of 26,269 participants (male: n=13,571, 51.7%; mean age 55.9, SD 14.9 years) were included in this study. A total of 71% (n=18,663) of respondents reported that they had excellent or very good general health. One summary factor was extracted to capture overall HRQOL using exploratory factor analysis. The confirmatory factor analysis further confirmed this one-factor model (Tucker-Lewis index, comparative fit index, and goodness-of-fit index >0.90; root mean square error of approximation 0.02). Multivariate Tobit regression analysis showed that age (β=–0.06), educational attainments (primary school: β=0.72; junior middle school: β=1.46; senior middle school or more: β=2.58), average income (≥¥30,000 [US $4200]: β=0.69), physical activity (β=0.75), alcohol use (β=0.46), self-reported disease (β=−6.36), and self-reported injury (β=–5.00) were the major influencing factors on the summary score of the HRQOL-5. Conclusions This study constructs a summary score from the HRQOL-5, providing a comprehensive representation of population-level HRQOL. Differences in summary scores of different subpopulations may help set priorities for health planning in China to improve population HRQOL.
Related Topics
Concepts
Confirmatory factor analysis
Medicine
Exploratory factor analysis
Population
Demography
Quality of life (healthcare)
Gerontology
Index (typography)
Tobit model
Chinese population
Environmental health
Statistics
Structural equation modeling
Psychometrics
Clinical psychology
Mathematics
Gene
Biochemistry
Computer science
Sociology
Chemistry
Genotype
Nursing
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.2196/52019
- OA Status
- gold
- Cited By
- 1
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401434401
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401434401Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/52019Digital Object Identifier
- Title
-
Summary Measure of Health-Related Quality of Life and Its Related Factors Based on the Chinese Version of the Core Healthy Days Measures: Cross-Sectional StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-31Full publication date if available
- Authors
-
Yulin Shi, Baohua Wang, Jian Zhao, Chunping Wang, Ning Li, Min Chen, Xia WanList of authors in order
- Landing page
-
https://doi.org/10.2196/52019Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2196/52019Direct OA link when available
- Concepts
-
Confirmatory factor analysis, Medicine, Exploratory factor analysis, Population, Demography, Quality of life (healthcare), Gerontology, Index (typography), Tobit model, Chinese population, Environmental health, Statistics, Structural equation modeling, Psychometrics, Clinical psychology, Mathematics, Gene, Biochemistry, Computer science, Sociology, Chemistry, Genotype, Nursing, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.score | 54, 288, 298 |
| abstract_inverted_index.study | 48, 294 |
| abstract_inverted_index.total | 165, 184 |
| abstract_inverted_index.track | 10 |
| abstract_inverted_index.using | 209 |
| abstract_inverted_index.years | 110 |
| abstract_inverted_index.(male: | 169 |
| abstract_inverted_index.0.02). | 237 |
| abstract_inverted_index.26,269 | 167 |
| abstract_inverted_index.51.7%; | 171 |
| abstract_inverted_index.China. | 116 |
| abstract_inverted_index.Factor | 22 |
| abstract_inverted_index.HRQOL. | 308, 328 |
| abstract_inverted_index.create | 31 |
| abstract_inverted_index.easily | 28 |
| abstract_inverted_index.factor | 119, 123, 202, 211, 215 |
| abstract_inverted_index.health | 13, 97, 321 |
| abstract_inverted_index.income | 262 |
| abstract_inverted_index.index, | 223, 226 |
| abstract_inverted_index.injury | 278 |
| abstract_inverted_index.junior | 251 |
| abstract_inverted_index.latent | 131 |
| abstract_inverted_index.middle | 252, 256 |
| abstract_inverted_index.models | 149 |
| abstract_inverted_index.reveal | 128 |
| abstract_inverted_index.school | 257 |
| abstract_inverted_index.score. | 146, 162 |
| abstract_inverted_index.scores | 312 |
| abstract_inverted_index.senior | 255 |
| abstract_inverted_index.showed | 242 |
| abstract_inverted_index.square | 233 |
| abstract_inverted_index.status | 14, 98 |
| abstract_inverted_index.study. | 182 |
| abstract_inverted_index.survey | 91 |
| abstract_inverted_index.years) | 177 |
| abstract_inverted_index.$4200]: | 265 |
| abstract_inverted_index.Chinese | 58, 78 |
| abstract_inverted_index.Chronic | 18 |
| abstract_inverted_index.Disease | 19 |
| abstract_inverted_index.HRQOL-5 | 134 |
| abstract_inverted_index.Healthy | 4, 63 |
| abstract_inverted_index.Methods | 80 |
| abstract_inverted_index.Results | 163 |
| abstract_inverted_index.Weifang | 112 |
| abstract_inverted_index.alcohol | 270 |
| abstract_inverted_index.average | 261 |
| abstract_inverted_index.capture | 206 |
| abstract_inverted_index.carried | 152 |
| abstract_inverted_index.cluster | 90 |
| abstract_inverted_index.develop | 51 |
| abstract_inverted_index.disease | 274 |
| abstract_inverted_index.examine | 95 |
| abstract_inverted_index.factors | 102, 158, 284 |
| abstract_inverted_index.finally | 151 |
| abstract_inverted_index.further | 217 |
| abstract_inverted_index.general | 198 |
| abstract_inverted_index.health. | 199 |
| abstract_inverted_index.improve | 326 |
| abstract_inverted_index.overall | 36, 140, 207 |
| abstract_inverted_index.quality | 38 |
| abstract_inverted_index.school: | 249, 253 |
| abstract_inverted_index.summary | 33, 53, 145, 161, 201, 287, 297, 311 |
| abstract_inverted_index.version | 59 |
| abstract_inverted_index.(HRQOL), | 41 |
| abstract_inverted_index.(primary | 248 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.HRQOL-5, | 301 |
| abstract_inverted_index.HRQOL-5. | 291 |
| abstract_inverted_index.November | 82 |
| abstract_inverted_index.Shandong | 114 |
| abstract_inverted_index.activity | 268 |
| abstract_inverted_index.analyses | 120, 124 |
| abstract_inverted_index.analysis | 216, 241 |
| abstract_inverted_index.combined | 29 |
| abstract_inverted_index.estimate | 71 |
| abstract_inverted_index.however, | 24 |
| abstract_inverted_index.identify | 155 |
| abstract_inverted_index.included | 179 |
| abstract_inverted_index.limiting | 42 |
| abstract_inverted_index.measures | 6, 65 |
| abstract_inverted_index.physical | 267 |
| abstract_inverted_index.planning | 322 |
| abstract_inverted_index.quantify | 138 |
| abstract_inverted_index.reported | 190 |
| abstract_inverted_index.resident | 105 |
| abstract_inverted_index.β=0.72; | 250 |
| abstract_inverted_index.β=1.46; | 254 |
| abstract_inverted_index.>0.90; | 230 |
| abstract_inverted_index.(HRQOL-5) | 66 |
| abstract_inverted_index.Objective | 46 |
| abstract_inverted_index.Province, | 115 |
| abstract_inverted_index.analysis. | 212 |
| abstract_inverted_index.conducted | 93 |
| abstract_inverted_index.confirmed | 218 |
| abstract_inverted_index.construct | 132 |
| abstract_inverted_index.different | 314 |
| abstract_inverted_index.excellent | 194 |
| abstract_inverted_index.extracted | 204 |
| abstract_inverted_index.n=13,571, | 170 |
| abstract_inverted_index.performed | 126 |
| abstract_inverted_index.providing | 302 |
| abstract_inverted_index.β=0.69), | 266 |
| abstract_inverted_index.β=2.58), | 260 |
| abstract_inverted_index.(n=18,663) | 187 |
| abstract_inverted_index.(β=0.46), | 272 |
| abstract_inverted_index.(β=0.75), | 269 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.behavioral | 100 |
| abstract_inverted_index.constructs | 295 |
| abstract_inverted_index.multistage | 88 |
| abstract_inverted_index.one-factor | 220 |
| abstract_inverted_index.population | 96, 106, 327 |
| abstract_inverted_index.priorities | 319 |
| abstract_inverted_index.regression | 148, 240 |
| abstract_inverted_index.stratified | 89 |
| abstract_inverted_index.underlying | 130 |
| abstract_inverted_index.Conclusions | 292 |
| abstract_inverted_index.Differences | 309 |
| abstract_inverted_index.attainments | 247 |
| abstract_inverted_index.calculating | 143 |
| abstract_inverted_index.comparative | 224 |
| abstract_inverted_index.educational | 246 |
| abstract_inverted_index.exploratory | 118, 210 |
| abstract_inverted_index.influencing | 157, 283 |
| abstract_inverted_index.population. | 79 |
| abstract_inverted_index.respondents | 189 |
| abstract_inverted_index.(β=–5.00) | 279 |
| abstract_inverted_index.(≥¥30,000 | 263 |
| abstract_inverted_index.Multivariate | 238 |
| abstract_inverted_index.confirmatory | 122, 214 |
| abstract_inverted_index.determinants | 75 |
| abstract_inverted_index.participants | 168 |
| abstract_inverted_index.(Tucker-Lewis | 222 |
| abstract_inverted_index.(β=–0.06), | 245 |
| abstract_inverted_index.(β=−6.36), | 275 |
| abstract_inverted_index.Surveillance; | 23 |
| abstract_inverted_index.approximation | 236 |
| abstract_inverted_index.comprehensive | 304 |
| abstract_inverted_index.indicator’s | 44 |
| abstract_inverted_index.self-reported | 273, 277 |
| abstract_inverted_index.health-related | 37 |
| abstract_inverted_index.representation | 305 |
| abstract_inverted_index.subpopulations | 315 |
| abstract_inverted_index.goodness-of-fit | 228 |
| abstract_inverted_index.population-level | 12, 307 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.72585581 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |