Factors predicting different times for brushing teeth during the day: multilevel analyses
Abstract Background The most effective and simple intervention for preventing oral disease is toothbrushing. However, there is substantial variation in the timing of brushing teeth during the day. We aimed to identify a comprehensive set of predictors of toothbrushing after lunch and after dinner an...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Oral Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12903-023-03555-1 |
| _version_ | 1797451284958150656 |
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| author | Hwa-Young Lee Nam-Hee Kim Jin-Young Jeong Sun-Jung Shin Hee-Jung Park Ichiro Kawachi |
| author_facet | Hwa-Young Lee Nam-Hee Kim Jin-Young Jeong Sun-Jung Shin Hee-Jung Park Ichiro Kawachi |
| author_sort | Hwa-Young Lee |
| collection | DOAJ |
| description | Abstract Background The most effective and simple intervention for preventing oral disease is toothbrushing. However, there is substantial variation in the timing of brushing teeth during the day. We aimed to identify a comprehensive set of predictors of toothbrushing after lunch and after dinner and estimated contextual (i.e., geographic) variation in brushing behavior at different times of the day. Methods We constructed a conceptual framework for toothbrushing by reviewing health behavior models. The main data source was the 2017 Community Health Survey. We performed a four-level random intercept logistic regression to predict toothbrushing behavior. (individual, household, Gi/Gun/Gu, and Si/Do). Results Individuals under 30 years of age had higher likelihood of brushing after lunch, while brushing after dinner was higher among those aged 40–79 years. People engaged in service/sales, agriculture/fishing/labor/mechanics, as well as student/housewife/unemployed were 0.60, 0.41, and 0.49 times less likely to brush their teeth after lunch, respectively, compared to those working in the office, but the gap narrowed to 0.97, 0.96, 0.94 for brushing after dinner. We also found significant area-level variations in the timing of brushing. Conclusions Different patterns in association with various factors at individual-, household- and Si/Gun/Gu-levels with toothbrushing after lunch versus toothbrushing after dinner suggests a need for tailored interventions to improve toothbrushing behavior depending on the time of day. |
| first_indexed | 2024-03-09T14:52:29Z |
| format | Article |
| id | doaj.art-9cffa2d18fd64403be8138c7d5e2bcd5 |
| institution | Directory Open Access Journal |
| issn | 1472-6831 |
| language | English |
| last_indexed | 2024-03-09T14:52:29Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj.art-9cffa2d18fd64403be8138c7d5e2bcd52023-11-26T14:24:15ZengBMCBMC Oral Health1472-68312023-11-012311910.1186/s12903-023-03555-1Factors predicting different times for brushing teeth during the day: multilevel analysesHwa-Young Lee0Nam-Hee Kim1Jin-Young Jeong2Sun-Jung Shin3Hee-Jung Park4Ichiro Kawachi5Graduate School of Public Health and Healthcare Management, The Catholic University of KoreaDepartment of Dental Hygiene, Mirae Campus, Yonsei UniversityHallym Research Institute of Clinical Epidemiology, Hallym UniversityDepartment of Dental Hygiene, College of Dentistry, Gangneung Wonju National UniversityDepartment of Dental Hygiene, College of Health Science, Kangwon National UniversityHarvard T.H. Chan School of Public HealthAbstract Background The most effective and simple intervention for preventing oral disease is toothbrushing. However, there is substantial variation in the timing of brushing teeth during the day. We aimed to identify a comprehensive set of predictors of toothbrushing after lunch and after dinner and estimated contextual (i.e., geographic) variation in brushing behavior at different times of the day. Methods We constructed a conceptual framework for toothbrushing by reviewing health behavior models. The main data source was the 2017 Community Health Survey. We performed a four-level random intercept logistic regression to predict toothbrushing behavior. (individual, household, Gi/Gun/Gu, and Si/Do). Results Individuals under 30 years of age had higher likelihood of brushing after lunch, while brushing after dinner was higher among those aged 40–79 years. People engaged in service/sales, agriculture/fishing/labor/mechanics, as well as student/housewife/unemployed were 0.60, 0.41, and 0.49 times less likely to brush their teeth after lunch, respectively, compared to those working in the office, but the gap narrowed to 0.97, 0.96, 0.94 for brushing after dinner. We also found significant area-level variations in the timing of brushing. Conclusions Different patterns in association with various factors at individual-, household- and Si/Gun/Gu-levels with toothbrushing after lunch versus toothbrushing after dinner suggests a need for tailored interventions to improve toothbrushing behavior depending on the time of day.https://doi.org/10.1186/s12903-023-03555-1ToothbrushingHealth behaviorOral healthMultilevel modeling |
| spellingShingle | Hwa-Young Lee Nam-Hee Kim Jin-Young Jeong Sun-Jung Shin Hee-Jung Park Ichiro Kawachi Factors predicting different times for brushing teeth during the day: multilevel analyses BMC Oral Health Toothbrushing Health behavior Oral health Multilevel modeling |
| title | Factors predicting different times for brushing teeth during the day: multilevel analyses |
| title_full | Factors predicting different times for brushing teeth during the day: multilevel analyses |
| title_fullStr | Factors predicting different times for brushing teeth during the day: multilevel analyses |
| title_full_unstemmed | Factors predicting different times for brushing teeth during the day: multilevel analyses |
| title_short | Factors predicting different times for brushing teeth during the day: multilevel analyses |
| title_sort | factors predicting different times for brushing teeth during the day multilevel analyses |
| topic | Toothbrushing Health behavior Oral health Multilevel modeling |
| url | https://doi.org/10.1186/s12903-023-03555-1 |
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