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1000
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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>Endometriosis is a frequent disease in women of reproductive age in which the endometrium occurs outside the uterine cavity. Multimodal treatment approaches are necessary due to loss of quality of life and the chronic nature of the disease. Digital health applications (DiGa) are becoming increasingly important. This research project investigates how a healthcare app can influence the subjective experience of illness in patients with endometriosis.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>Empiric data were collected through semi-structured interviews. Data analysis was carried out using qualitative focussed interview analysis. Reliability was ensured by joint interdisciplinary and interprofessional evaluation of the interviews by experts and those affected.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>Ten patients with endometriosis and the prescribed healthcare app Endo-App<jats:sup>©</jats:sup> were examined. Categories were defined from the superordinate categories “Factors influencing the experience of illness” and “Evaluation of the app”. The app provided reliable information, promoted self-efficacy through exercises and strengthened the perception of the individuality of the illness. It helped to minimise nocebo effects from internet research and enabled a positive change of perspective. Patients criticised the time required for data input and had data protection concerns. The educational elements were often seen as redundant. Some patients only used the app briefly, or not at all.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusion</jats:title>
<jats:p>Once a DiGa has been prescribed, it may be useful to explain its use on an outpatient basis and validate regular use. Blind re-prescribing of DiGas should be avoided. Younger patients with a recent diagnosis or patients following rehabilitation may benefit more from prescribing.</jats:p>
</jats:sec>
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1000
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Hinweis
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DeepGreen-ID: 15009ac832a3466288a0d122edbcd994 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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