# Data, Scripts, and Output for the use-case **Intermittent Fasting** This repository contains the data for the systematic review described in [Berk et al. (2021)][1] plus the scripts, data and output to reproduce the simulation study conducted in ASReview. ## Introduction Intermittent fasting has received increasing attention in popular media and science in recent years. Fasting leads to metabolic adaptations associated with minimizing anabolic processes (synthesis, growth, and reproduction), and enhancing maintenance, recycling, and repair to protect cells ([de Cabo & Mattson, 2019][2]). Theoretically, therefore, intermittent fasting would have health benefits. However, it is important to know to what extent intermittent fasting offers benefits over continuous fasting. With our review, we aim to answer the question: 'what is the effect of different intermittent fasting regimes on weight and cardiovascular risk compared to continuous fasting?' To increase the quality of a systematic review on Intermittent Fasting, we applied a 2-step screening. First, a classical search and screening process was performed identifying 638 records of which 18 were deemed as relevant, described in [Berk et al. (2021)][1]. Then, the search was updated with identifying 53 new publications. The labels of the first search were used as training data for the second search using ASReview. Then, the 53 records were screened and 7 additionally relevant records were identified. ## Search The search terms for the original search are available in the file `search_strategy.docx` and were used for the publication by Berk et al. (2021). Berk KAC, van der Voorn B, van Rossum EFC. Intermitterend vasten in wetenschap en praktijk [Intermittent fasting in science and practice; what are the effects on weight and cardiometabolic health?]. Ned Tijdschr Geneeskd. 2021 Apr 15;165:D5284. Dutch. PMID: 33914421. ## Raw data The following files are available: In `raw_data/data` the following datasets are available: - *search1_all_records.txt* including all records from search 1 (n_1=510); - *search1_included.txt* including relevant records from search 1 (n_2=15); - *search2_all_records.txt* including all records from search 2 (n_3=128); - *search2_included.txt* including relevant records from search 2 (n_4=3); A new search has been conducted on 01-03-2021, see `raw_data/data/search_3` for the results per database and see *search3_unlabelled* for the de-duplicated unlabbeled records from search 3 (n_5=53);. The 5 datasets have been combined using ``` python raw_data/scripts/merge.py ``` and the result is stored in `raw_data/output/intermittent_fasting_raw.csv`. A dataset with only the columns *title, abstract, label* has been created using ``` python raw_data/script/split_data_with_multiple_labels.py raw_data/output/intermittent_fasting_raw.csv output --split label ``` and the result is stored in `raw_data/output/intermittent_fasting_label.csv`. A version with only the labelled part of the data is stored in `raw_data/output/intermittent_fasting_only_labelled.csv`. The raw data was split into 2 subsets, one file with only the labelled data `data/intermittent_fasting.csv` and one file with partly labeled data (i.e., also containing the unlabelled data) stored in `asreview/intermittent_fasting_raw.csv` which was used for screening in ASReview. ## Installation requirements Run the following code in the CLI: ``` pip install -r requirements.txt ``` ## Simulation The data files in the data-folder were used for running a simulation study. To run the simulation, run ``` sh jobs.sh ``` The results are stored in `output/simulation`. The simulation was conducted on the original data with 93 runs with each relevant record being a prior inclusion and 10 randomly chosen irrelevant records. In each run the same 10 irrelevant records have been used. To extract this information run ``` python scripts/get_prior_knowledge.py ``` The results are stored in `output/tables`. The dataset characteristics are obtained with ``` python scripts/merge_descriptives.py ``` and stored in `output/tables`. The metrics resulting from the simulation study per run, can be obtained with ``` python scripts/merge_metrics.py ``` and are stored in `output/tables`. # ASReview The file `intermittent_fasting_raw.csv` was uploaded in ASReview, and the **638** labelled records from search 1 and 2 were used as prior knowledge and the **53** records were screened via the default model settings in version **XX [ADD VERSION NUMBER]** (see `if-2.asreview` for the project files) of which **7** records were denoted as relevant, see the file `asreview_result_if-2.xlsx`. [1]: https://www.ntvg.nl/artikelen/intermitterend-vasten-wetenschap-en-praktijk [2]: https://www.nejm.org/doi/10.1056/NEJMra1905136