Clinical trial NCT06049056: Which Automated Methodology?
Official title: Which Automated Methodology?
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Recruitment
Keywords: Primary care
Digital data collection
Lead sponsor: Keele University
Sponsor class: OTHER
First posted: 2023-09-21
Enrollment: 8880
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All adults of age 18 years and over, registered at a participating general practice, will be eligible to participate.

Brief summary: The aim of this study is to identify the modes of clinical research invitation that optimise digital responses from participants in primary care settings. This will be achieved by investigating and characterising response rates (recruitment) obtained from a hybrid of digital recruitment methods, in general practice settings.

Responses to this research will provide a single data item on general health to further characterise the sample and whether, since Covid-19, adults are more or less willing to participate in research.

Detailed description: When conducting research, which data collection method to use, can be a difficult decision for many researchers. Choosing an inappropriate method can result in untrustworthy findings and therefore wrong conclusions. The use of digital technologies to improve the quality and reduce the cost of health care services, is a vision set out by government in 2021. How digital technologies can be used, in carrying out health research, is an area that there is not a lot known about and needs further research. This study will therefore investigate how to invite people to take part in research and which digital methods people use, to take part.

It is commonly known that the recruitment of research participants from primary care settings is difficult. The Covid-19 pandemic however highlighted the importance of primary care, in the delivery of healthcare research, which is the setting where most patients visit. Being contacted about a research study of importance, is the first step in a patients journey towards taking part in a research study. Patients will though, often drop out before they actually consent to take part in a research study. A recent NIHR survey suggested a positive increase in the public's attitude to health research. This research study will also investigate whether, since Covid-19, people are actually more or less willing to take part in research and their reasons for this.

The digital methods used for collecting data in this study, will include: automated check-in screens; Short Messaging Service (SMS or text message) with online data collection; and postal invitations from the general practice, with online data collection accessed by a quick response (QR) code or website. All people of age 18 years and over, registered at a participating general practice, will be allowed to take part. Data collected will include demographics, a data item on general health and one on willingness to take part in research.

Eligibility criteria: Inclusion Criteria:

* Adults 18 years of age or over.
* Adults registered at participating general practices.

Exclusion Criteria:

* People under the age of 18 years.
* Adults who have asked for their medical records not to be used for research and have a relevant code indicating this included on their records.

Primary outcomes:
measure: Recruitment (RM1)
timeFrame: 14 days
measure: Recruitment (RM2)
timeFrame: 14 Days
measure: Recruitment (RM3a)
timeFrame: 14 days
measure: Recruitment (RM3b)
timeFrame: 14 days
Secondary outcomes:
measure: Willingness to participate in research since the COVID-19 pandemic
timeFrame: 14 days
measure: Willingness to participate in research since the COVID-19 pandemic
timeFrame: 14 days
Other outcomes:
Locations:
city: Wolverhampton
country: United Kingdom
facility: NIHR CRN: West Midlands
Study officials:
affiliation: Keele CTU
name: Sarah Lawton
role: PRINCIPAL_INVESTIGATOR
References: