Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort
Tino Prell,1 Gabriele Helga Franke,2 Melanie Jagla-Franke,2,3 Aline Schönenberg1 1Department of Geriatrics, Halle University Hospital, Halle, Germany; 2Department of Psychology of Rehabilitation, University of Applied Sciences Magdeburg-Stendal, Magdeburg-Stendal, Germany; 3Department of Psychology...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-05-01
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| Series: | Patient Preference and Adherence |
| Subjects: | |
| Online Access: | https://www.dovepress.com/identifying-patterns-of-self-reported-nonadherence-using-network-analy-peer-reviewed-fulltext-article-PPA |
| _version_ | 1811252702124965888 |
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| author | Prell T Franke GH Jagla-Franke M Schönenberg A |
| author_facet | Prell T Franke GH Jagla-Franke M Schönenberg A |
| author_sort | Prell T |
| collection | DOAJ |
| description | Tino Prell,1 Gabriele Helga Franke,2 Melanie Jagla-Franke,2,3 Aline Schönenberg1 1Department of Geriatrics, Halle University Hospital, Halle, Germany; 2Department of Psychology of Rehabilitation, University of Applied Sciences Magdeburg-Stendal, Magdeburg-Stendal, Germany; 3Department of Psychology in Health Promotion and Prevention, University of Applied Sciences Neubrandenburg, Neubrandenburg, GermanyCorrespondence: Aline Schönenberg, Department of Geriatrics, Halle University Hospital, Halle, Germany, Tel +49 345 5574071, Email aline.schoenenberg@uk-halle.dePurpose: Nonadherence is a complex behaviour that contributes to poor health outcomes; therefore, it is necessary to understand its underlying structure. Network analysis is a novel approach to explore the relationship between multiple variables.Patients and Methods: Patients from four different studies (N = 1.746) using the self-reported Stendal Adherence to Medication Score (SAMS) were pooled. Network analysis using EBICglasso followed by confirmatory factor analysis were performed to understand how different types of nonadherence covered in the SAMS items are related to each other.Results: Network analysis revealed different categories of nonadherence: lack of knowledge about medication, forgetting to take medication, and intentional modification of medication. The intentional modification can further be sub-categorized into two groups, with one group modifying medication based on changes in health (improvement of health or adverse effects), whereas the second group adjusts medication based on overall medication beliefs and concerns. Adverse effects and taking too many medications were further identified as most influential variables in the network.Conclusion: The differentiation between modification due to health changes and modification due to overall medication beliefs is crucial for intervention studies. Network analysis is a promising tool for further exploratory studies of adherence.Keywords: medication adherence, older adults, polypharmacy, Stendal adherence to medication score, network analysis |
| first_indexed | 2024-04-12T16:39:35Z |
| format | Article |
| id | doaj.art-9652b07bcd8b438592121121e2cd3968 |
| institution | Directory Open Access Journal |
| issn | 1177-889X |
| language | English |
| last_indexed | 2024-04-12T16:39:35Z |
| publishDate | 2022-05-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Patient Preference and Adherence |
| spelling | doaj.art-9652b07bcd8b438592121121e2cd39682022-12-22T03:24:52ZengDove Medical PressPatient Preference and Adherence1177-889X2022-05-01Volume 161153116274997Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German CohortPrell TFranke GHJagla-Franke MSchönenberg ATino Prell,1 Gabriele Helga Franke,2 Melanie Jagla-Franke,2,3 Aline Schönenberg1 1Department of Geriatrics, Halle University Hospital, Halle, Germany; 2Department of Psychology of Rehabilitation, University of Applied Sciences Magdeburg-Stendal, Magdeburg-Stendal, Germany; 3Department of Psychology in Health Promotion and Prevention, University of Applied Sciences Neubrandenburg, Neubrandenburg, GermanyCorrespondence: Aline Schönenberg, Department of Geriatrics, Halle University Hospital, Halle, Germany, Tel +49 345 5574071, Email aline.schoenenberg@uk-halle.dePurpose: Nonadherence is a complex behaviour that contributes to poor health outcomes; therefore, it is necessary to understand its underlying structure. Network analysis is a novel approach to explore the relationship between multiple variables.Patients and Methods: Patients from four different studies (N = 1.746) using the self-reported Stendal Adherence to Medication Score (SAMS) were pooled. Network analysis using EBICglasso followed by confirmatory factor analysis were performed to understand how different types of nonadherence covered in the SAMS items are related to each other.Results: Network analysis revealed different categories of nonadherence: lack of knowledge about medication, forgetting to take medication, and intentional modification of medication. The intentional modification can further be sub-categorized into two groups, with one group modifying medication based on changes in health (improvement of health or adverse effects), whereas the second group adjusts medication based on overall medication beliefs and concerns. Adverse effects and taking too many medications were further identified as most influential variables in the network.Conclusion: The differentiation between modification due to health changes and modification due to overall medication beliefs is crucial for intervention studies. Network analysis is a promising tool for further exploratory studies of adherence.Keywords: medication adherence, older adults, polypharmacy, Stendal adherence to medication score, network analysishttps://www.dovepress.com/identifying-patterns-of-self-reported-nonadherence-using-network-analy-peer-reviewed-fulltext-article-PPAmedication adherenceolder adultspolypharmacystendal adherence to medication scorenetwork analysis |
| spellingShingle | Prell T Franke GH Jagla-Franke M Schönenberg A Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort Patient Preference and Adherence medication adherence older adults polypharmacy stendal adherence to medication score network analysis |
| title | Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort |
| title_full | Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort |
| title_fullStr | Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort |
| title_full_unstemmed | Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort |
| title_short | Identifying Patterns of Self-Reported Nonadherence Using Network Analysis in a Mixed German Cohort |
| title_sort | identifying patterns of self reported nonadherence using network analysis in a mixed german cohort |
| topic | medication adherence older adults polypharmacy stendal adherence to medication score network analysis |
| url | https://www.dovepress.com/identifying-patterns-of-self-reported-nonadherence-using-network-analy-peer-reviewed-fulltext-article-PPA |
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