Artificial Intelligence and Physical Activity Among People From Ethnic Minority Groups

NCT05779696 | Unknown

• 1 other identifier: 39522
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

Despite the high interest in physical activity, many individuals lack the necessary experience in being active and therefore have low levels of knowledge and confidence to become and stay active. For effective lifestyle changes to occur, information must be tailored to the individual's health, goals, motivations, and overall ability. Lifestyle interventions, for example those designed to increase physical activity, are only effective when adapted to the physical, social, and psychological needs of the patient and progressed at rate appropriate for their development by specialist health professional. In the context of ethnic minority health, information must also be culturally adapted, sensitive to religious needs, and accessible to those where English is not proficient. Behavioural digital health interventions have been moderately successful in increasing physical activity, although opportunities for further improvement remain to be discussed. New technologies involving the use of artificial intelligence (AI) are growing, and allow the dissemination of individualised and tailored advice and information. Whilst a few AI-driven physical activity-based applications exist, they are not widely used, particularly amongst people from ethnic minority groups where both physical activity and digital health literacy is poor. Research has identified that whilst many people would be receptive to using health chatbots, hesitancy regarding this technology is likely to compromise engagement. In particular, user perspectives, motivation and capabilities need to be taken into account when developing and assessing the effectiveness of health chatbots. Guidance suggests that developing health chatbots should focus on issues of digital literacy, linguistic and cultural issues, privacy concerns, and personalization. As such, any development needs to involve user-driven co-creation techniques and involving community partners to increase the probability that it will ultimately be effective. Aims Aim 1 Gain a new understanding of barriers and facilitators to digital physical activity interventions and AI-delivered healthcare in people from ethnic minority groups through an online survey Aim 2 To conduct a series of focus groups to explore participants understanding and identifying barriers and facilitators to digital physical activity interventions. In particular to: i)Better understand general barriers and facilitators (focus on access and provision of education, and physical, environmental, cultural and psycho-social barriers) to physical activity; ii)Explore current and future usage of digital-based resources to facilitate physical activity behaviour; iii)Investigate views of use of AI in digital-based healthcare applications (e.g., trust in such applications)

Conditions

Physical Inactivity

Keywords

Physical activity; Artificial intelligence; Ethnic health

Outcome Measures

Primary Outcomes (1)

• Feelings towards chatbots and artificial intelligence

  Bespoke questions via online survey. Questions answered on a Likert scale between 1 and 7; with 1 being 'disagree completely' and 7 being 'agree completely'

  12 months

Study Arms (1)

Group 1

We will recruit a diverse range of participants to take part and therefore have a limited inclusion/exclusion criteria. Participants must be over 18 years old and be able to a computer/mobile device to access the survey or video conference software (Zoom, as used by the Centre of Ethnic Health Research). Participants can come from a range of cultural communities and religious groups. Participants do not need to have any prior knowledge to participate in the virtual focus groups or the survey.

Other: No-intervention

Interventions

No-intervention OTHER

This is a non-intervention study.

Group 1

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

We aim to recruit a range of people from different ethnic minority groups - we are interested in a diverse sample of participants with a wide-ranging view on the use of digital-health based interventions and AI.

You may qualify if:

• Participants must be over 18 years old;
• Participants must be able to a computer/mobile device to access the survey or video conference software (Zoom, as used by the Centre of Ethnic Health Research)
• Participants can come from a range of cultural communities and religious groups;
• Participants do not need to have any prior knowledge to participate in the virtual focus groups or the survey.

Sponsors & Collaborators

• University of Leicester: lead

Study Sites (1)

Thomas Wilkinson

Leicester, Leicestershire, LE1 7RH, United Kingdom

RECRUITING

Related Links

• Webpage for study

MeSH Terms

Conditions

Sedentary Behavior; Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Fellow

Study Record Dates

• First Submitted: March 9, 2023
• First Posted: March 22, 2023
• Study Start: April 1, 2023
• Primary Completion: March 31, 2024
• Study Completion: March 31, 2024
• Last Updated: May 16, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)