Purpose: To assess the ability and accuracy of elderly men to recall their weights and determine what characteristics might predict recall ability and accuracy.
Methods: Eight hundred sixty-nine elderly men (mean age, 84 years), participants of the Manitoba Follow-up Study (MFUS), responded to a questionnaire asking them to recall their weights at ages 20, 30, 50, and 65 years. Recalled weights were compared with measured weights collected since MFUS began in 1948. Logistic regression was used to predict ability and accuracy of weight recall.
Results: Only 75% of respondents attempted to recall their weights at all 4 ages. Among men recalling 4 weights, fewer than half were accurate within +/- 10%, just 7% were within +/- 5% of their measured weights. Accuracy of recall was significantly and independently associated with body mass index during middle age (5 kg/m(2)) (odds ratio 0.83, 95% confidence interval: 0.76, 0.90) and weight change. Unmarried men were less likely than married men to attempt recalling all 4 weights. Men overweight at middle age were more likely to underestimate their recalled weights.
Conclusions: Studies relating weight in early adulthood or middle age with outcomes in later life should not rely on elderly male participants recalling those weights.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.annepidem.2007.06.009 | DOI Listing |
Anal Chem
January 2025
Institute of Artificial Intelligence, Xiamen University, Xiamen, Fujian, 361005, China.
Metabolite identification from 1D H NMR spectra is a major challenge in NMR-based metabolomics. This study introduces NMRformer, a Transformer-based deep learning framework for accurate peak assignment and metabolite identification in 1D H NMR spectroscopy. Unlike traditional approaches, NMRformer interprets spectra as sequences of spectral peaks and integrates a self-attention mechanism and peak height ratios directly into the Transformer encoder layer.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Immunity, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Background: HFMD is a common infectious disease that is prevalent worldwide. In many provinces in China, there have been outbreaks and epidemics of whooping cough, posing a threat to public health.
Purpose: It is crucial to grasp the epidemiological characteristics of HFMD in Quzhou and establish a prediction model for HFMD to lay the foundation for early warning of HFMD.
Background: The mid-upper arm circumference (MUAC) is an anthropometric screening tool used to assess the nutritional status of individuals, offering a practical and feasible option in low-resource settings. However, the potential of MUAC as a screening tool for identifying thinness among adolescents remains underexplored.
Objective: This study aimed to evaluate the accuracy of MUAC in identifying all forms of thinness among adolescent girls enrolled in selected schools in Addis Ababa, Ethiopia.
Front Cardiovasc Med
December 2024
Department of Cardiology of Lu'an People's Hospital, Lu'an Hospital of Anhui Medical University, Lu'an, China.
Background: To investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.
Methods: We selected 575 elderly patients with CHD admitted to the Affiliated Lu'an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients).
Turk J Ophthalmol
December 2024
Mustafa Kemal University, Tayfur Sökmen Faculty of Medicine, Department of Ophthalmology, Hatay, Türkiye.
Objectives: This study compared the readability of patient education materials from the Turkish Ophthalmological Association (TOA) retinopathy of prematurity (ROP) guidelines with those generated by large language models (LLMs). The ability of GPT-4.0, GPT-4o mini, and Gemini to produce patient education materials was evaluated in terms of accuracy and comprehensiveness.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!