READ ME File For 'Dataset title'

Dataset DOI: 10.5258/SOTON/D2797

ReadMe Author: Dr Isla Robertson, University of Southampton ORCID ID 0000-0002-7982-199X

This dataset supports the thesis entitled Digital health technologies in improving efficiency in reproductive medicine
AWARDED BY: University of Southampton
DATE OF AWARD: [2024]

DESCRIPTION OF THE DATA
[This should include a detailed description of the data, how it was collected/created, any specialist software needed to view the data]

This dataset contains:
Merged app reviews.xlsx
App reviews for apps included in Chapter 2 study, reviews downloaded from Apple Appstore and Google Playstore.
  
Mediemodata.xlsx

MediEmoData_emotional.xlsx

MediEmo app data- this includes emotional tracking and medication tracking data entered into the MediEmo smartphone app by NHS and private patients having fertility treatments.
This is anonymised and contains no data that could be linked to participants.

scan_streamlining_paper_cohort_anon.csv

Ultrasound database of all scan measurements from 10 years of fertility treatment (IVF cycles) at Complete Fertility, Southampton with outcome data. This is pseudo-anonymised but there are features within the data that could be used to de-anonymise the patients and so given the sensitive nature of fertility treatment, this data should remain confidential and only shared with researchers wtih a legitimate interest as per the ethical approval.Please complete the ethics request form attached to the record to request access to this data.

CFC_prediction_model_V2.ipynb- Requires PYTHON, python is a free, open-source programming language

This is a model using data from the anonymised register of the HFEA after processing steps so dataset includes only the relevant cycles for my project (Chapter 6).

Date of data collection: 2020-2022

Information about geographic location of data collection: Southampton, UK

Licence:
CC-BY

Related projects/Funders:
Digital support tools for fertility patients – a systematic review, qualitative synthesis of user views and meta-analysis of the effect of these tools on psychological outcomes and pregnancy rates.  
Descriptive  evaluation study of a single digital support tool, the MediEmo smartphone app
Using machine learning and ultrasound data aiming to make IVF care more efficient by optimising and standardising trigger timing decisions 
Streamlining follicular monitoring during controlled ovarian stimulation? A data-driven approach to efficient IVF care  
Using machine learning to distil IVF data into simple tools for mid-cycle outcome prediction and communication 


Related publication:
Robertson, I., et al. (2020) Streamlining follicular monitoring during controlled ovarian stimulation? A data-driven approach to efficient IVF care in the new era of social distancing. Human Reproduction. 35, 65-65

Robertson, I., et al. (2020) The impact of Covid-19 on infertility services and future directions. Reproduction and Fertility. 1, C3-C7

Robertson, I., et al. (2021) Digital support tools for fertility patients – a narrative systematic review. Human Fertility. 1-10  

Robertson, I., Chmiel, F., & Cheong, Y. (2021) ‘After egg collection, can we predict the chance of embryos for day 5 transfer or freezing?’, Reproduction and Fertility, RAF-21-0018

Robertson, I., Harrison, C., Ng, K. Y. B., Macklon, N., Cheong, Y. & Boivin, J. (2022) Development, implementation, and initial feasibility testing of the MediEmo mobile application to provide support during medically assisted reproduction. Human Reproduction, deac046


Date that the file was created: January, 2024