Thomas Gültzow1, Eline Suzanne Smit2, Raesita Hudales1, Carmen D Dirksen3, Ciska Hoving1. 1. Department of Health Promotion, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands. 2. Department of Communication Science, Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, the Netherlands. 3. Department of Clinical Epidemiology and Medical Technology Assessment, CAPHRI Care and Public Health Research Institute, Maastricht University Medical Center, Maastricht, the Netherlands.
Abstract
OBJECTIVES: Evidence-based smoking cessation support tools (EBSTs) can double the quitting chances, but uptake among smokers is low. A digital decision aid (DA) could help smokers choose an EBST in concordance with their values and preferences, but it is unclear which type of smokers are interested in a digital DA. We hypothesized that smokers' general decision-making style (GDMS) could be used to identify early adopters. This study therefore aimed to identify smoker profiles based on smokers' GDMS and investigate these profiles' association with intention to use a digital DA. DESIGN: A cross-sectional dataset (N = 200 smokers intending to quit) was used to perform a hierarchical cluster analysis based on smokers' GDMS scores. METHODS: Clusters were compared on demographic and socio-cognitive variables. Mediation analyses were conducted to see if the relationship between cluster membership and intention was mediated through socio-cognitive variables (e.g., attitude). RESULTS: Two clusters were identified; " Avoidant Regretters " (n = 134) were more avoidant, more regretful and tended to depend more on others in their decision making, while " Intuitive Non-regretters " (n = 66) were more spontaneous and intuitive in their decision making. Cluster membership was significantly related to intention to use a DA, with " Avoidant Regretters " being more interested. Yet, this association ceased to be significant when corrected for socio-cognitive variables (e.g., attitude). This indicates that cluster membership affected intention via socio-cognitive variables. CONCLUSIONS: The GDMS can be used to identify smokers who are interested in a digital DA early on. As such, the GDMS can be used to tailor recruitment and DA content.
OBJECTIVES: Evidence-based smoking cessation support tools (EBSTs) can double the quitting chances, but uptake among smokers is low. A digital decision aid (DA) could help smokers choose an EBST in concordance with their values and preferences, but it is unclear which type of smokers are interested in a digital DA. We hypothesized that smokers' general decision-making style (GDMS) could be used to identify early adopters. This study therefore aimed to identify smoker profiles based on smokers' GDMS and investigate these profiles' association with intention to use a digital DA. DESIGN: A cross-sectional dataset (N = 200 smokers intending to quit) was used to perform a hierarchical cluster analysis based on smokers' GDMS scores. METHODS: Clusters were compared on demographic and socio-cognitive variables. Mediation analyses were conducted to see if the relationship between cluster membership and intention was mediated through socio-cognitive variables (e.g., attitude). RESULTS: Two clusters were identified; " Avoidant Regretters " (n = 134) were more avoidant, more regretful and tended to depend more on others in their decision making, while " Intuitive Non-regretters " (n = 66) were more spontaneous and intuitive in their decision making. Cluster membership was significantly related to intention to use a DA, with " Avoidant Regretters " being more interested. Yet, this association ceased to be significant when corrected for socio-cognitive variables (e.g., attitude). This indicates that cluster membership affected intention via socio-cognitive variables. CONCLUSIONS: The GDMS can be used to identify smokers who are interested in a digital DA early on. As such, the GDMS can be used to tailor recruitment and DA content.
Authors: Geoffrey C Williams; Holly A McGregor; Daryl Sharp; Chantal Levesque; Ruth W Kouides; Richard M Ryan; Edward L Deci Journal: Health Psychol Date: 2006-01 Impact factor: 4.267
Authors: Karen R Sepucha; Cornelia M Borkhoff; Joanne Lally; Carrie A Levin; Daniel D Matlock; Chirk Jenn Ng; Mary E Ropka; Dawn Stacey; Natalie Joseph-Williams; Celia E Wills; Richard Thomson Journal: BMC Med Inform Decis Mak Date: 2013-11-29 Impact factor: 2.796