Health-related Quality of Life and Long COVID

Long COVID Clinical Trial

— OpenPROMPT  

Official title:

Quality-of-life in Patients With Long COVID: Harnessing the Scale of Big Data to Quantify the Health and Economic Costs

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05552612
• Other study ID #: 2022-KEP-799
• Status: Completed
• Start date: September 26, 2022
• Est. completion date: December 31, 2023

Study information

• Verified date: March 2024
• Source: London School of Hygiene and Tropical Medicine
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Background: Long COVID is estimated to affect 1.7 million people in the UK. One way of assessing the impact of long COVID is to measure quality-of-life through a standard questionnaire, which can then be used to understand the costs of long COVID to the NHS and wider economy. The impact of long COVID on these measures is not currently known. It is important to understand who is worst affected by long COVID and the cost to the National Health Service (NHS), so that strategies like booster vaccines can be prioritised to the right people.

Aim: OpenPROMPT aims to understand the impact of long COVID on quality-of-life in adults in English primary care.

Methods: We will ask people to participate in the study by downloading a smartphone app, and completing a series of questionnaires held within the app. Questionnaires will ask about quality of life, productivity and symptoms of long COVID. Participants will be asked to fill in the questionnaires once a month, for four months. Electronic reminders will be sent to participants to ask them to complete the questionnaires, which will take roughly 15 minutes to complete each month. Participants will also be asked to give consent for linkage of their questionnaire responses to their existing health records.

The results will tell us if long COVID has different quality-of-life and economic impacts among people of different ages, ethnicities, geographic regions, or because of any underlying health conditions. These results can be converted into standardised measurements used by the NHS to measure the impact of illnesses and the cost of long COVID to health services.

Impact: Together with other researchers studying long COVID, we will provide results to support long-term care, and make recommendations for prevention of long COVID in the future. At all stages we will ensure that the input of patients and the public is central to running the study and interpreting the outputs.

Funding: OpenPROMPT is a collaboration between the London School of Hygiene and Tropical Medicine, the OpenSAFELY patient data platform at the University of Oxford, and TPP (which supplies clinical software to General Practices (GPs) in the UK). The project is funded by the National Institute for Health Research.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 7574
• Est. completion date: December 31, 2023
• Est. primary completion date: October 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Inclusion criteria are that the patient is aged 18 or over and can read and understand English, and can download and use the Airmid app.

Exclusion Criteria:

• The following patients will be excluded:

• Patients under 18 years old;

• Patients who do not have a smartphone or cannot download or log in to the Airmid app;

• Patients who cannot understand English.

Related Conditions & MeSH terms

• Long COVID

Locations

Country	Name	City	State
United Kingdom	London School of Hygiene and Tropical Medicine	London

Sponsors (2)

Lead Sponsor	Collaborator
London School of Hygiene and Tropical Medicine	University of Oxford

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Health-related quality of life	Measured using the EQ-5D-5L score (EuroQoL score across 5 dimensions and 5 levels).; EQ-5D-5L is measured on a scale between 0 and 1, where 0 is a state as bad as being dead, and 1 is full health.	day 0