Katherine Gordon-Smith1, Kate Saunders2, John R Geddes2, Paul J Harrison2, Chris Hinds2, Nick Craddock3, Ian Jones3, Lisa Jones4. 1. Psychological Medicine, University of Worcester, Henwick Grove, Worcester, WR2 6AJ, UK. 2. Department of Psychiatry, University of Oxford, UK and Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK. 3. National Centre for Mental Health, Cardiff University, UK. 4. Psychological Medicine, University of Worcester, Henwick Grove, Worcester, WR2 6AJ, UK. Electronic address: lisa.jones@worc.ac.uk.
Abstract
BACKGROUND: Electronic longitudinal mood monitoring has been shown to be acceptable to patients with affective disorders within clinical settings, but its use in large-scale research has not yet been established. METHODS: Using both postal and email invitations, we invited 4080 past research participants with affective disorders who were recruited into the Bipolar Disorder Research Network (BDRN) over a 10 year period to participate in online weekly mood monitoring. In addition, since January 2015 we have invited all newly recruited BDRN research participants to participate in mood monitoring at the point they were recruited into BDRN. RESULTS: Online mood monitoring uptake among past participants was 20%, and among new participants to date was 46% with participants recruited over the last year most likely to register (61%). More than 90% mood monitoring participants engaged for at least one month, with mean engagement period greater than one year (58 weeks) and maximum engagement for longer than three years (165 weeks). There were no significant differences in the proportion of past and new BDRN participants providing data for at least 4 weeks (91%, 92% respectively), 3 months (78%, 82%), 6 months (65%, 54%) or one year (51%, 44%). LIMITATIONS: Our experiences with recruiting participants for electronic prospective mood monitoring may not necessarily generalise fully to research situations that are very different from those we describe. CONCLUSIONS: Large-scale electronic longitudinal mood monitoring in affective disorders for research purposes is feasible with uptake highest among newly recruited participants.
BACKGROUND: Electronic longitudinal mood monitoring has been shown to be acceptable to patients with affective disorders within clinical settings, but its use in large-scale research has not yet been established. METHODS: Using both postal and email invitations, we invited 4080 past research participants with affective disorders who were recruited into the Bipolar Disorder Research Network (BDRN) over a 10 year period to participate in online weekly mood monitoring. In addition, since January 2015 we have invited all newly recruited BDRN research participants to participate in mood monitoring at the point they were recruited into BDRN. RESULTS: Online mood monitoring uptake among past participants was 20%, and among new participants to date was 46% with participants recruited over the last year most likely to register (61%). More than 90% mood monitoring participants engaged for at least one month, with mean engagement period greater than one year (58 weeks) and maximum engagement for longer than three years (165 weeks). There were no significant differences in the proportion of past and new BDRN participants providing data for at least 4 weeks (91%, 92% respectively), 3 months (78%, 82%), 6 months (65%, 54%) or one year (51%, 44%). LIMITATIONS: Our experiences with recruiting participants for electronic prospective mood monitoring may not necessarily generalise fully to research situations that are very different from those we describe. CONCLUSIONS: Large-scale electronic longitudinal mood monitoring in affective disorders for research purposes is feasible with uptake highest among newly recruited participants.
Authors: Sarah M Goodday; Mary-Jane Attenburrow; Lauren Atkinson; Guy Goodwin; Kate Saunders; Matthew South; Clare Mackay; Mike Denis; Chris Hinds; Jim Davies; James Welch; William Stevens; Karen Mansfield; Juulia Suvilehto; John Geddes Journal: J Med Internet Res Date: 2020-01-15 Impact factor: 5.428
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