TY - JOUR
T1 - Adapting Behavioral Interventions for a Changing Public Health Context
T2 - A Worked Example of Implementing a Digital Intervention During a Global Pandemic Using Rapid Optimisation Methods
AU - Morton, Kate
AU - Ainsworth, Ben
AU - Miller, Sascha
AU - Rice, Cathy
AU - Bostock, Jennifer
AU - Denison-Day, James
AU - Towler, Lauren
AU - Groot, Julia
AU - Moore, Mike
AU - Willcox, Merlin
AU - Chadborn, Tim
AU - Amlôt, Richard
AU - Gold, Natalie
AU - Little, Paul
AU - Yardley, Lucy
N1 - Funding Information:
This study was funded by the UKRI/MRC Rapid Response Call: UKRI CV220-009. The Germ Defense intervention was hosted by the Lifeguide Team, supported by the NIHR Biomedical Research Centre, University of Southampton. LY is a National Institute for Health Research (NIHR) Senior Investigator and theme lead for University of Southampton Biomedical Research Centre. LY and RA are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Behavioral Science and Evaluation of Interventions at the University of Bristol in partnership with Public Health England (PHE). MW is a NIHR Academic Clinical Lecturer, under grant CL-2016-26-005. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health, or Public Health England. The funders had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript.
Funding Information:
Thank you to our voluntary research assistants, Amina Khan and Lara Rosa, who led the engagement with social media and charities for our qualitative study recruitment, and contributed to the re-design of the Germ Defence front page. Funding. This study was funded by the UKRI/MRC Rapid Response Call: UKRI CV220-009. The Germ Defense intervention was hosted by the Lifeguide Team, supported by the NIHR Biomedical Research Centre, University of Southampton. LY is a National Institute for Health Research (NIHR) Senior Investigator and theme lead for University of Southampton Biomedical Research Centre. LY and RA are affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Behavioral Science and Evaluation of Interventions at the University of Bristol in partnership with Public Health England (PHE). MW is a NIHR Academic Clinical Lecturer, under grant CL-2016-26-005. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health, or Public Health England. The funders had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript.
Publisher Copyright:
© Copyright © 2021 Morton, Ainsworth, Miller, Rice, Bostock, Denison-Day, Towler, Groot, Moore, Willcox, Chadborn, Amlot, Gold, Little and Yardley.
PY - 2021/4/26
Y1 - 2021/4/26
N2 - Background: A rigorous approach is needed to inform rapid adaptation and optimisation of behavioral interventions in evolving public health contexts, such as the Covid-19 pandemic. This helps ensure that interventions are relevant, persuasive, and feasible while remaining evidence-based. This paper provides a set of iterative methods to rapidly adapt and optimize an intervention during implementation. These methods are demonstrated through the example of optimizing an effective online handwashing intervention called Germ Defense. Methods: Three revised versions of the intervention were rapidly optimized and launched within short timeframes of 1–2 months. Optimisations were informed by: regular stakeholder engagement; emerging scientific evidence, and changing government guidance; rapid qualitative research (telephone think-aloud interviews and open-text surveys), and analyses of usage data. All feedback was rapidly collated, using the Table of Changes method from the Person-Based Approach to prioritize potential optimisations in terms of their likely impact on behavior change. Written feedback from stakeholders on each new iteration of the intervention also informed specific optimisations of the content. Results: Working closely with clinical stakeholders ensured that the intervention was clinically accurate, for example, confirming that information about transmission and exposure was consistent with evidence. Patient and Public Involvement (PPI) contributors identified important clarifications to intervention content, such as whether Covid-19 can be transmitted via air as well as surfaces, and ensured that information about difficult behaviors (such as self-isolation) was supportive and feasible. Iterative updates were made in line with emerging evidence, including changes to the information about face-coverings and opening windows. Qualitative research provided insights into barriers to engaging with the intervention and target behaviors, with open-text surveys providing a useful supplement to detailed think-aloud interviews. Usage data helped identify common points of disengagement, which guided decisions about optimisations. The Table of Changes was modified to facilitate rapid collation and prioritization of multiple sources of feedback to inform optimisations. Engagement with PPI informed the optimisation process. Conclusions: Rapid optimisation methods of this kind may in future be used to help improve the speed and efficiency of adaptation, optimization, and implementation of interventions, in line with calls for more rapid, pragmatic health research methods.
AB - Background: A rigorous approach is needed to inform rapid adaptation and optimisation of behavioral interventions in evolving public health contexts, such as the Covid-19 pandemic. This helps ensure that interventions are relevant, persuasive, and feasible while remaining evidence-based. This paper provides a set of iterative methods to rapidly adapt and optimize an intervention during implementation. These methods are demonstrated through the example of optimizing an effective online handwashing intervention called Germ Defense. Methods: Three revised versions of the intervention were rapidly optimized and launched within short timeframes of 1–2 months. Optimisations were informed by: regular stakeholder engagement; emerging scientific evidence, and changing government guidance; rapid qualitative research (telephone think-aloud interviews and open-text surveys), and analyses of usage data. All feedback was rapidly collated, using the Table of Changes method from the Person-Based Approach to prioritize potential optimisations in terms of their likely impact on behavior change. Written feedback from stakeholders on each new iteration of the intervention also informed specific optimisations of the content. Results: Working closely with clinical stakeholders ensured that the intervention was clinically accurate, for example, confirming that information about transmission and exposure was consistent with evidence. Patient and Public Involvement (PPI) contributors identified important clarifications to intervention content, such as whether Covid-19 can be transmitted via air as well as surfaces, and ensured that information about difficult behaviors (such as self-isolation) was supportive and feasible. Iterative updates were made in line with emerging evidence, including changes to the information about face-coverings and opening windows. Qualitative research provided insights into barriers to engaging with the intervention and target behaviors, with open-text surveys providing a useful supplement to detailed think-aloud interviews. Usage data helped identify common points of disengagement, which guided decisions about optimisations. The Table of Changes was modified to facilitate rapid collation and prioritization of multiple sources of feedback to inform optimisations. Engagement with PPI informed the optimisation process. Conclusions: Rapid optimisation methods of this kind may in future be used to help improve the speed and efficiency of adaptation, optimization, and implementation of interventions, in line with calls for more rapid, pragmatic health research methods.
KW - COVID-19
KW - adaptation
KW - behavior change
KW - intervention - behavioral
KW - optimisation
KW - rapid research methods
UR - http://www.scopus.com/inward/record.url?scp=85105842482&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2021.668197
DO - 10.3389/fpubh.2021.668197
M3 - Article
SN - 2296-2565
VL - 9
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 668197
ER -