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Biomedical

UK women smokers' experiences of an age-progression smoking cessation intervention: Thematic analysis of accounts

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Lucy Walker,

Lucy Walker

Department of Psychology, Manchester Metropolitan University,

lucy.walker@mmu.ac.uk


Sarah Grogan,

Sarah Grogan

Department of Psychology, Manchester Metropolitan University,

s.grogan@mmu.ac.uk


Keira Sholtens,

Keira Sholtens

entre for Health Psychology, The Science Centre, Staffordshire University,

lucy.walker@mmu.ac.uk


Andrew Denovan,

Andrew Denovan

Adelphi Values Ltd,

a.denovan@mmu.ac.uk


Brian Mcmillan,

Brian Mcmillan

Centre for Primary Care and Health Services Research, University of Manchester,

bran.mcmillan@manchester.ac.uk


Christopher Armitage,

Christopher Armitage

Division of Psychology and Mental Health, Manchester Centre for Health Psychology, University of Manchester, Manchester University NHS Foundation Trust, Manchester Aca- demic Health Science Centre, NIHR Greater Manchester Patient Safety Translational Rese

christopher.armitage@manchester.ac.uk


Mark Conner,

Mark Conner

School of Psychology, University of Leeds,

m.t.conner@leeds.ac.uk


Tracy Epton,

Tracy Epton

Division of Psychology and Mental Health, Manchester Centre for Health Psychology, University of Manchester,

travy.epton@manchester.ac.uk


Maria Cordero

Maria Cordero

Department of Psychology, Manchester Metropolitan University,

m.cordero@mmu.ac.uk


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© attribution CC-BY

  • 0

rating
1932 Views

Added on

2022-03-08

Doi: https://doi.org/10.1016/j.pecinn.2022.100021

Related Subjects
Anatomy
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Epidemiology
Genetics
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Pathology
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Primary care
Women and reproductive health

Abstract

Objectives Appearance-related interventions to promote healthy behaviour have been found effective to communicate health risks. The current study aimed to explore women smokers' experiences of age-progression software showing the effects of smoking on the face. Methods A qualitative design was implemented, utilizing both individual interviews and focus groups within a critical realist framework. Fifteen, 19–52 year-old women smokers were administered an age-progression intervention. All participants responded to the intervention, engaged in semi-structured interviews, and were invited back to attend one of three focus groups. Data were analysed using inductive thematic analysis. Results Four main themes were identified: Health versus Appearance, Shock Reaction, Perceived Susceptibility, and Intention to Quit. Participants found the intervention useful, voicing need for a comprehensive approach that includes both appearance and health. Despite increases in appearance-based apps which could diminish impact, women's accounts of shock induced by the aged smoking-morphed images were similar to previous work conducted more than ten years previously. Conclusions The study provides novel insights in how women smokers currently perceive, and react to, an age-progression intervention for smoking cessation. Innovation Findings emphasise the implementation of this intervention type accompanied by health information in a range of patient settings.

Key Question

What is the main focus of this study?

The study explores the impact of accountability, training, and human factors on the use of artificial intelligence (AI) in healthcare, focusing on the perceptions of healthcare practitioners in the United States.

How does accountability influence the use of AI in healthcare?

The study found that a lack of clear accountability can inhibit the use of AI in healthcare settings, as practitioners may be uncertain about who is responsible for AI-driven decisions.

What role does training play in the adoption of AI by healthcare practitioners?

Willingness to receive AI training was identified as a significant factor influencing practitioners' intention to use AI, suggesting that adequate training can facilitate AI adoption in healthcare.

What human factors affect the perception of AI in healthcare?

Perceived workload, trustworthiness of AI, and perceived risk were significant factors affecting practitioners' perceptions of AI's impact on decision-making in healthcare.

What are the implications of this study for healthcare practice?

The findings suggest that addressing issues of accountability, providing adequate training, and considering human factors are crucial for the effective integration and adoption of AI in healthcare systems.

Why is this research significant?

This research provides insights into the barriers and facilitators of AI adoption in healthcare, highlighting the importance of a systems approach that includes human factors considerations alongside technological advancements.

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Article usage: Mar-2022 to May-2025
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2025 May 110 110
2025 April 87 87
2025 March 76 76
2025 February 57 57
2025 January 102 102
2024 December 46 46
2024 November 56 56
2024 October 62 62
2024 September 64 64
2024 August 110 110
2024 July 47 47
2024 June 40 40
2024 May 32 32
2024 April 46 46
2024 March 54 54
2024 February 36 36
2024 January 34 34
2023 December 43 43
2023 November 58 58
2023 October 28 28
2023 September 30 30
2023 August 22 22
2023 July 38 38
2023 June 28 28
2023 May 40 40
2023 April 41 41
2023 March 53 53
2023 January 6 6
2022 December 43 43
2022 November 65 65
2022 October 33 33
2022 September 38 38
2022 August 51 51
2022 July 53 53
2022 June 102 102
2022 May 50 50
2022 April 21 21
2022 March 30 30
Total 1932 1932
Show by month Manuscript Video Summary
2025 May 110 110
2025 April 87 87
2025 March 76 76
2025 February 57 57
2025 January 102 102
2024 December 46 46
2024 November 56 56
2024 October 62 62
2024 September 64 64
2024 August 110 110
2024 July 47 47
2024 June 40 40
2024 May 32 32
2024 April 46 46
2024 March 54 54
2024 February 36 36
2024 January 34 34
2023 December 43 43
2023 November 58 58
2023 October 28 28
2023 September 30 30
2023 August 22 22
2023 July 38 38
2023 June 28 28
2023 May 40 40
2023 April 41 41
2023 March 53 53
2023 January 6 6
2022 December 43 43
2022 November 65 65
2022 October 33 33
2022 September 38 38
2022 August 51 51
2022 July 53 53
2022 June 102 102
2022 May 50 50
2022 April 21 21
2022 March 30 30
Total 1932 1932
Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health
copyright icon

© attribution CC-BY

  • 0

rating
1932 Views

Added on

2022-03-08

Doi: https://doi.org/10.1016/j.pecinn.2022.100021

Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health

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