数据表1：COVID-19大流行对代谢健康远程医疗减重干预效果影响的倾向评分匹配分析

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Impact of the COVID-19 Pandemic on the Effectiveness of a Metabolic Health Telemedicine Intervention for Weight Loss: A Propensity Score Matching Analysis.pdf
• DOI：10.3389/fpubh.2022.897099.s001
• 发布日期：2022-06-16
• 文件数：1 个
• 文件总体积：659.56 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Impact_of_the_COVID-19_Pandemic_on_the_Effectiveness_of_a_Metabolic_Health_Telemedicine_Intervention_for_Weight_Loss_A_Propensity_Score_Matching_Analysis_pdf/20077493

命令行下载

curl -L -o "Data_Sheet_1_Impact of the COVID-19 Pandemic on the Effectiveness of a Metabolic Health Telemedicine Intervention for Weight Loss: A Propensity Score .pdf" "https://ndownloader.figshare.com/files/35912816"

文件清单

• Data_Sheet_1_Impact of the COVID-19 Pandemic on the Effectiveness of a Metabolic Health Telemedicine Intervention for Weight Loss: A Propensity Score .pdf（659.56 KB）

文件总体积 659.56 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

BackgroundCoronavirus disease 2019 (COVID-19) pandemic public health measures such as stay-at-home and mandatory work-from-home orders have been associated with obesogenic lifestyle changes, increased risk of weight gain, and their metabolic sequelae. We sought to assess the impact of this pandemic on weight loss from a telemedicine-delivered very-low-carbohydrate intervention targeting nutritional ketosis (NKI).
MethodsA total of 746 patients with a BMI ≥25kg/m2, enrolled between January and March 2020 and treated for at least 1 year with the NKI, were classified as pandemic cohort (PC). A separate cohort of 699 patients who received 1 year of the NKI in the preceding years, enrolled between January and March 2018, were identified as pre-pandemic cohort (Pre-PC). Demographic and clinical data were obtained from medical records to compare the cohorts and assess the outcomes. Using propensity score matching (PSM), balanced and matched groups of 407 patients in the Pre-PC and 407 patients in the PC were generated. Longitudinal change in absolute weight and percentage weight change from baseline to 1 year were assessed.
ResultsWeight significantly decreased in both PC and Pre-PC at 3, 6, 9, and 12 months. The weight loss trajectory was similar in both PC and Pre-PC with no significant weight differences between the two cohorts at 3, 6, 9, and 12 months. On an average, the PC lost 7.5% body weight while the Pre-PC lost 7.9% over 1 year, and the percent weight loss did not differ between the two cohorts (p = 0.50).
ConclusionA very-low-carbohydrate telemedicine intervention delivered comparable and medically significant weight loss independent of pandemic stress and lifestyle limitations.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.897099.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Impact_of_the_COVID-19_Pandemic_on_the_Effectiveness_of_a_Metabolic_Health_Telemedicine_Intervention_for_Weight_Loss_A_Propensity_Score_Matching_Analysis_pdf/20077493）。