Back

London Ramadan Fasting Study (LORANS): Rationale, design, and methods

Al-Jafar, R.; Elliott, P.; Tsilidis, K. K.; Dehghan, A.

2021-07-16 public and global health
10.1101/2021.07.14.21260518 medRxiv
Show abstract

BackgroundHundreds of millions of Muslims fast during the month of Ramadan. The London Ramadan Fasting Study (LORANS) aims to assess the lifestyle changes during this month and investigate the effect of Ramadan fasting on health. MethodsLORANS is an observational study of participants that follow religious fasting in Ramadan. We advertised, recruited, and visited participants in five mosques in London, United Kingdom. In total, 146 individuals were recruited before Ramadan in May 2019 of which 85 participated in the follow up visit after Ramadan. The study protocol was approved by the ethics committee affiliated to Imperial College London. A written informed consent was signed by all the participants. Every participant completed a questionnaire, a physical examination, and gave blood samples at each visit. Moreover, they completed a 3-day food diary before Ramadan and once again during Ramadan to record dietary changes during the month of fasting. ResultsThe mean age of participants was 45.6{+/-} 15.9 years. 47.1% of the participants were females, 25.5% were obese, 4.7% were smokers, 14% were diabetic, 24% were hypertensive, and 5.2% had cardiovascular diseases. Data collection covered demographics, lifestyle, food intake, blood pressure, anthropometric measurements, body composition, and metabolic biomarker profiling. ConclusionBy engaging with mosques, proper introduction of the study aims and convenient recruitment in the mosque, we were able to recruit a balanced population regarding age and sex and collected valuable data on Ramadan fasting using high-quality techniques.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.