Predictors for adherent behavior in the COVID-19 pandemic: A cross-sectional telephone survey.
COVID-19
adherence
health belief model
pandemic fatigue
perceived health risk
risk perception
self-efficacy
social norms
Journal
Frontiers in public health
ISSN: 2296-2565
Titre abrégé: Front Public Health
Pays: Switzerland
ID NLM: 101616579
Informations de publication
Date de publication:
2022
2022
Historique:
received:
11
03
2022
accepted:
10
10
2022
entrez:
7
11
2022
pubmed:
8
11
2022
medline:
9
11
2022
Statut:
epublish
Résumé
During the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to. A cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (β) could be compared. Overall median adherence was 0.75 (IQR: 0.5-1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (β = 0.43, 95%CI: 0.33-0.54), social standards of acceptable behavior (β = 0.33, 95%CI: 0.27-0.40), subjective/individual assessment of an increased personal health risk (β = 0.12, 95%CI: 0.05-0.18), self-efficacy (β = 0.06, 95%CI: 0.02-0.10), female gender (β = 0.05, 95%CI: 0.01-0.08), and low corona fatigue (behavioral fatigue: β = -0.11, 95%CI: -0.18 to -0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence. This study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.
Sections du résumé
Background
During the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to.
Methods
A cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (β) could be compared.
Results
Overall median adherence was 0.75 (IQR: 0.5-1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (β = 0.43, 95%CI: 0.33-0.54), social standards of acceptable behavior (β = 0.33, 95%CI: 0.27-0.40), subjective/individual assessment of an increased personal health risk (β = 0.12, 95%CI: 0.05-0.18), self-efficacy (β = 0.06, 95%CI: 0.02-0.10), female gender (β = 0.05, 95%CI: 0.01-0.08), and low corona fatigue (behavioral fatigue: β = -0.11, 95%CI: -0.18 to -0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence.
Conclusions
This study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.
Identifiants
pubmed: 36339221
doi: 10.3389/fpubh.2022.894128
pmc: PMC9632415
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
894128Informations de copyright
Copyright © 2022 Siebenhofer, Könczöl, Jeitler, Schmid, Elliott and Avian.
Déclaration de conflit d'intérêts
Author DS was employed by Austrian Agency for Health and Food Safety Ltd. AGES. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
J Public Health Res. 2021 Mar 05;10(1):1943
pubmed: 35585964
Soc Sci Med. 2021 Jan;268:113370
pubmed: 32980677
Am J Public Health. 2003 Nov;93(11):1887-8
pubmed: 14600058
Comput Methods Programs Biomed. 2022 Sep 12;226:107109
pubmed: 36174422
Front Public Health. 2021 Apr 30;9:646764
pubmed: 33996723
Infect Drug Resist. 2020 Oct 27;13:3817-3832
pubmed: 33149627
BMC Public Health. 2022 May 5;22(1):898
pubmed: 35513803
Am J Prev Med. 2020 Aug;59(2):157-167
pubmed: 32576418
Ann Behav Med. 2022 Aug 2;56(8):781-790
pubmed: 35759288
Appl Psychol Health Well Being. 2020 Dec;12(4):1205-1223
pubmed: 33010119
Br J Health Psychol. 2022 Feb;27(1):116-135
pubmed: 34000098
J Healthc Eng. 2022 Apr 1;2022:4096950
pubmed: 35368915
Proc Natl Acad Sci U S A. 2020 Nov 3;117(44):27285-27291
pubmed: 33060298
R Soc Open Sci. 2021 Sep 29;8(9):201445
pubmed: 34603740
Milbank Mem Fund Q. 1966 Jul;44(3):Suppl:94-127
pubmed: 5967464
Tour Manag Perspect. 2016 Oct;20:195-203
pubmed: 32289007
J Med Internet Res. 2002 Apr-Nov;4(2):E13
pubmed: 12554560
Front Psychol. 2020 Oct 09;11:570017
pubmed: 33154727
J Public Health (Oxf). 2022 Mar 7;44(1):e117-e125
pubmed: 34159382
Sci Rep. 2021 Nov 5;11(1):21751
pubmed: 34741109
BMJ. 2021 Jan 18;372:n137
pubmed: 33461963
J Med Internet Res. 2021 Feb 26;23(2):e23720
pubmed: 33571103
Front Psychiatry. 2021 Jan 27;12:596281
pubmed: 33584382
PLoS One. 2020 Oct 7;15(10):e0239795
pubmed: 33027281
R Soc Open Sci. 2020 Oct 14;7(10):201199
pubmed: 33204475
Front Public Health. 2021 May 31;9:678926
pubmed: 34136459
Soc Personal Psychol Compass. 2021 May;15(5):e12596
pubmed: 34230834
Health Commun. 2010 Dec;25(8):661-9
pubmed: 21153982
Health Educ Q. 1988 Summer;15(2):175-83
pubmed: 3378902
Glob Transit. 2020;2:76-82
pubmed: 32835202
Front Public Health. 2021 Jan 14;8:589372
pubmed: 33520911
PLoS One. 2019 Apr 10;14(4):e0214450
pubmed: 30969975
Death Stud. 2022;46(4):979-986
pubmed: 32673183
J Appl Gerontol. 2020 Nov;39(11):1175-1183
pubmed: 32697126
Sci Rep. 2021 Nov 18;11(1):22480
pubmed: 34795312
PLoS One. 2020 Oct 15;15(10):e0240644
pubmed: 33057450
J Epidemiol Community Health. 2003 Nov;57(11):857-63
pubmed: 14600110
PLoS One. 2009 Dec 03;4(12):e8032
pubmed: 19997505
Trends Psychiatry Psychother. 2020 Oct-Dec;42(4):389-392
pubmed: 32997044
Health Commun. 2015;30(6):566-76
pubmed: 25010519
PLoS One. 2021 Dec 9;16(12):e0260171
pubmed: 34882685
Sensors (Basel). 2022 Aug 08;22(15):
pubmed: 35957477
Sci Rep. 2021 Sep 20;11(1):18626
pubmed: 34545107
BMJ Open. 2021 Sep 14;11(9):e051447
pubmed: 34521674