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Record W2917896950

A influência do estilo de vida no estado nutricional de idosos: uma revisão sistemática da literatura

2019· article· pt· W2917896950 on OpenAlexaboutno aff
Geórgia Ferreira da Silva Bandeira, Rafael da Silveira Moreira, Gerlane Henrique de Lima, Vanessa de Lima Silva

Bibliographic record

VenueAmericanae (AECID Library) · 2019
Typearticle
Languagept
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSciELOGerontologyAlcohol consumptionPhysical activityMedicinePsychologyMEDLINEAlcoholPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Introdução: O estilo de vida é caracterizado como um padrão de comportamento que pode ter efeito na saúde e está relacionado a aspectos que refletem atitudes, valores e oportunidades. Objetivo: Realizar uma revisão sistemática de artigos publicados sobre a influência do estilo de vida no estado nutricional de idosos. Materiais e Métodos: Foram pesquisados artigos publicados em três bases de pesquisa, Lilacs, Pubmed e Scielo. Após a busca dos descritores foi realizada seleção dos resumos por pares em duas fases por dois leitores em cada uma. Análise do risco de viés foi realizada através de protocolo validado (Newcastle-Ottawa). Esse foi adaptado para estudos transversais. Resultados: Durante a revisão sistemática foram identificados 8 artigos. Houve um maior número de trabalhos publicados em 2013. Do total de artigos analisados, foram identificados quatro fatores do estilo de vida significativamente associados ao estado nutricional do idoso. Os estudos incluídos na revisão utilizaram como ferramentas para avaliação do estado nutricional, Mini Nutritional Assessment, Mini Nutritional Assessment-Short Form e o critério European Working Group on Sarcopenia in Older People. Discussão: Alguns dos fatores de estilo de vida que foram identificados significativamente como protetores para o estado nutricional, foram: consumo usual de álcool, atividade física habitual e lazer. Outros fatores com interferência negativa são o tabagismo e sedentarismo. Conclusão: O presente estudo identificou a existência de influência dos fatores de estilo de vida (atividade física, álcool, tabagismo, lazer) no estado nutricional de indivíduos idosos. ABSTRACT The lifestyle influence the nutritional status of the elderly: a systematic literature reviewIntroduction: Lifestyle is described as a pattern of behavior that can have effects on health and is related to aspects that reflect attitudes, values, and opportunities. Goal: To conduct a systematic review of published articles on the influence of lifestyle on the nutritional status of elderly people. Materials and Methods: Published articles found in three research databases, namely, Lilacs, PubMed, and SciELO, were analyzed. After searching for descriptors, abstracts were peer-selected in two phases, by two readers each. Bias analysis was conducted using a validated protocol (Newcastle-Ottawa), which has been adapted for cross-sectional studies. Results: Eight articles were identified in the systematic review. Most of the papers were published in 2013. From the analyzed articles, four lifestyle factors were found to be significantly associated with the nutritional status of elderly people. The studies included in the review employed the Mini Nutritional Assessment, the Mini Nutritional Assessment-Short Form, and the criterion of the European Working Group on Sarcopenia in Older People to assess nutritional status. Discussion: Some of the lifestyle factors found to be significant protectors of nutritional status were regular consumption of alcohol, habitual physical activity, and leisure. Factors with a negative influence were smoking and sedentarism. Conclusion: The study identified the influence of lifestyle factors (physical activity, alcohol, smoking, and leisure) on the nutritional status of elderly people.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0120.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.281
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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