Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs
Background: Recent years have seen a rapid growth in the number of online health communities targeted at patients with long-term conditions. Myasthenia Gravis (MG) is a rare neurological disease for which such communities have not been analysed before. The aim of this study was to better understand...
| Hoofdauteurs: | , , , , , , , , |
|---|---|
| Formaat: | Journal article |
| Taal: | English |
| Gepubliceerd in: |
Frontiers Media
2024
|
| _version_ | 1826315085541277696 |
|---|---|
| author | Legg, D Meisel, A Stein, M Gerischer, L Herdick, M Krüger, D Mergenthaler, P Masanneck, L Lehnerer, S |
| author_facet | Legg, D Meisel, A Stein, M Gerischer, L Herdick, M Krüger, D Mergenthaler, P Masanneck, L Lehnerer, S |
| author_sort | Legg, D |
| collection | OXFORD |
| description | Background: Recent years have seen a rapid growth in the number of online health communities targeted at patients with long-term conditions. Myasthenia Gravis (MG) is a rare neurological disease for which such communities have not been analysed before. The aim of this study was to better understand the needs of the MG population through the collation and categorisation of questions that users of MG social media were asking fellow users on these platforms. Methodology: Systematic observation of four MG Facebook groups was conducted over a 2-month period. Groups were selected for analysis based on the following systematic criteria: Language (English), Membership (≥ 5,000 members), group activity (≥ 2 posts per week), target audience (general MG population) and researcher engagement with group administrators. The study protocol was reviewed by the institutional review board of the Charité—Universitätsmedizin Berlin (EA2/106/22). During the observation period, data were extracted from individual posts featuring questions made across each group using a systematic and objective coding scheme. All data points were coded directly from the source and collated into an SPSS database (IBM SPSS V.27, SPSS). Absolute and relative frequencies were calculated for categorical variables and proportions were compared across groups to validate the credibility and relevance of different requests. Results: Of the 2,062 posts observed (N = 2,062), 1,392 featured questions (n = 1,392). Questions were asked by 787 unique users: 531 were identified as one-time users (67%) and 256 were identified as repeat users (33%). Six hundred and fifty six users were classified as presumed diagnosed (83%), 61 as seeking diagnosis (8%), 69 as family and/or friends (9%) and as other (<0%). Eight unique categories of questions were observed including MG treatment (31%), Symptoms (19%), Living with MG (12%), Diagnosis (10%), non-MG medication (11%), Tests (8%), Location (4%) and Other (4%). Conclusion: Members of the MG population make active use of online health communities to seek and discuss practical information concerning various aspects of the disease, its diagnosis and care. The openness and willingness of the sample population to share sensitive medical information shows a high need for information not entirely catered to by the medical profession. |
| first_indexed | 2024-12-09T03:19:43Z |
| format | Journal article |
| id | oxford-uuid:f7858dbf-2d03-4184-90b1-3fd8f44e84e2 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:19:43Z |
| publishDate | 2024 |
| publisher | Frontiers Media |
| record_format | dspace |
| spelling | oxford-uuid:f7858dbf-2d03-4184-90b1-3fd8f44e84e22024-10-31T20:03:46ZMyasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f7858dbf-2d03-4184-90b1-3fd8f44e84e2EnglishJisc Publications RouterFrontiers Media2024Legg, DMeisel, AStein, MGerischer, LHerdick, MKrüger, DMergenthaler, PMasanneck, LLehnerer, SBackground: Recent years have seen a rapid growth in the number of online health communities targeted at patients with long-term conditions. Myasthenia Gravis (MG) is a rare neurological disease for which such communities have not been analysed before. The aim of this study was to better understand the needs of the MG population through the collation and categorisation of questions that users of MG social media were asking fellow users on these platforms. Methodology: Systematic observation of four MG Facebook groups was conducted over a 2-month period. Groups were selected for analysis based on the following systematic criteria: Language (English), Membership (≥ 5,000 members), group activity (≥ 2 posts per week), target audience (general MG population) and researcher engagement with group administrators. The study protocol was reviewed by the institutional review board of the Charité—Universitätsmedizin Berlin (EA2/106/22). During the observation period, data were extracted from individual posts featuring questions made across each group using a systematic and objective coding scheme. All data points were coded directly from the source and collated into an SPSS database (IBM SPSS V.27, SPSS). Absolute and relative frequencies were calculated for categorical variables and proportions were compared across groups to validate the credibility and relevance of different requests. Results: Of the 2,062 posts observed (N = 2,062), 1,392 featured questions (n = 1,392). Questions were asked by 787 unique users: 531 were identified as one-time users (67%) and 256 were identified as repeat users (33%). Six hundred and fifty six users were classified as presumed diagnosed (83%), 61 as seeking diagnosis (8%), 69 as family and/or friends (9%) and as other (<0%). Eight unique categories of questions were observed including MG treatment (31%), Symptoms (19%), Living with MG (12%), Diagnosis (10%), non-MG medication (11%), Tests (8%), Location (4%) and Other (4%). Conclusion: Members of the MG population make active use of online health communities to seek and discuss practical information concerning various aspects of the disease, its diagnosis and care. The openness and willingness of the sample population to share sensitive medical information shows a high need for information not entirely catered to by the medical profession. |
| spellingShingle | Legg, D Meisel, A Stein, M Gerischer, L Herdick, M Krüger, D Mergenthaler, P Masanneck, L Lehnerer, S Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title | Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title_full | Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title_fullStr | Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title_full_unstemmed | Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title_short | Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs |
| title_sort | myasthenia gravis utilising cross platform quantitative content analysis to uncover and validate unmet needs |
| work_keys_str_mv | AT leggd myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT meisela myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT steinm myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT gerischerl myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT herdickm myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT krugerd myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT mergenthalerp myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT masanneckl myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds AT lehnerers myastheniagravisutilisingcrossplatformquantitativecontentanalysistouncoverandvalidateunmetneeds |