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dc.contributor.authorWaaler, Per Niklas Benzler
dc.contributor.authorBongo, Lars Ailo Aslaksen
dc.contributor.authorRolandsen, Christina
dc.contributor.authorLorem, Geir Fagerjord
dc.date.accessioned2024-08-30T08:20:06Z
dc.date.available2024-08-30T08:20:06Z
dc.date.issued2024-02-08
dc.description.abstractIf scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n= 12 981, 53.4% women and 46.6% men) and 7th (n= 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61–0.57). Vigorous physical activity (exercising to exhaustion≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56–0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.en_US
dc.identifier.citationWaaler, Bongo, Rolandsen, Lorem. An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients. Scientific Reports. 2024;14(1):3199en_US
dc.identifier.cristinIDFRIDAID 2249594
dc.identifier.doi10.1038/s41598-024-53275-x
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/10037/34481
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalScientific Reports
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleAn individually adjusted approach for communicating epidemiological results on health and lifestyle to patientsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)