Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance
Abstract Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balan...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01063-2 |
| _version_ | 1797258994202443776 |
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| author | Christopher Duckworth Bethany Cliffe Brian Pickering Ben Ainsworth Alison Blythin Adam Kirk Thomas M. A. Wilkinson Michael J. Boniface |
| author_facet | Christopher Duckworth Bethany Cliffe Brian Pickering Ben Ainsworth Alison Blythin Adam Kirk Thomas M. A. Wilkinson Michael J. Boniface |
| author_sort | Christopher Duckworth |
| collection | DOAJ |
| description | Abstract Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation. |
| first_indexed | 2024-04-24T23:02:22Z |
| format | Article |
| id | doaj.art-2cc4bfea8e5c4ed185b3d8c7e9d3c11c |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-04-24T23:02:22Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-2cc4bfea8e5c4ed185b3d8c7e9d3c11c2024-03-17T12:39:09ZengNature Portfolionpj Digital Medicine2398-63522024-03-01711910.1038/s41746-024-01063-2Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performanceChristopher Duckworth0Bethany Cliffe1Brian Pickering2Ben Ainsworth3Alison Blythin4Adam Kirk5Thomas M. A. Wilkinson6Michael J. Boniface7IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of SouthamptonSchool of Psychology, Faculty of Environmental and Life Sciences, University of SouthamptonIT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of SouthamptonSchool of Psychology, Faculty of Environmental and Life Sciences, University of Southamptonmy mHealth Limitedmy mHealth Limitedmy mHealth LimitedIT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of SouthamptonAbstract Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately ( < 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation.https://doi.org/10.1038/s41746-024-01063-2 |
| spellingShingle | Christopher Duckworth Bethany Cliffe Brian Pickering Ben Ainsworth Alison Blythin Adam Kirk Thomas M. A. Wilkinson Michael J. Boniface Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance npj Digital Medicine |
| title | Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance |
| title_full | Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance |
| title_fullStr | Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance |
| title_full_unstemmed | Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance |
| title_short | Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance |
| title_sort | characterising user engagement with mhealth for chronic disease self management and impact on machine learning performance |
| url | https://doi.org/10.1038/s41746-024-01063-2 |
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