ArticleBMC geriatrics2021
Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong: a random intercept model.
Article in BMC geriatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Determining optimal Barthel Index cutoff scores for predicting Longshi Scale grades across age groups in stroke patients.Frontiers in aging · 2026Article
- Assessing Functional Independence and Associated Factors in Older Populations of Kazakhstan: Implications for Long-Term Care.Healthcare (Basel, Switzerland) · 2025Article
- Article
- Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.Frontiers in public health · 2023Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundBarthel Index (BI) is one of the most widely utilized tools for assessing functional independence in activities of daily living. Most existing BI studies used populations with specific diseases (e.g., Alzheimer's and stroke) to test prognostic factors of BI scores; however, the generalization of these findings was limited when the target populations varied.
objectivesThe aim of the present study was to utilize electronic health records (EHRs) and data mining techniques to develop a generic procedure for identifying prognostic factors that influence BI score changes among community-dwelling elderly.
methodsLongitudinal data were collected from 113 older adults (81 females; mean age = 84 years, SD = 6.9 years) in Hong Kong elderly care centers. Visualization technologies were used to align annual BI scores with individual EHRs chronologically. Linear mixed-effects (LME) regression was conducted to model longitudinal BI scores based on socio-demographics, disease conditions, and features extracted from EHRs.
resultsThe visualization presented a decline in BI scores changed by time and health history events. The LME model yielded a conditional R
conclusionsThe proposed visualization approach and the LME model estimation can help to trace older adults' BI score changes and identify the influencing factors. The constructed long-term surveillance system provides reference data in clinical practice and help healthcare providers manage the time, cost, data and human resources in community-dwelling settings.
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