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Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring
Kontigo Care AB, Uppsala, Sweden..
Kontigo Care AB, Uppsala, Sweden..
Kontigo Care AB, Uppsala, Sweden..
Kontigo Care AB, Uppsala, Sweden..
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2022 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 17, no 7, article id e0271465Article in journal (Refereed) Published
Abstract [en]

Purpose: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners.

Methods: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared.

Results: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale.

Conclusion: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.

Place, publisher, year, edition, pages
Public Library of Science (PLoS), 2022. Vol. 17, no 7, article id e0271465
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Public Health, Global Health, Social Medicine and Epidemiology
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URN: urn:nbn:se:uu:diva-501092DOI: 10.1371/journal.pone.0271465ISI: 000965303000084PubMedID: 35834544OAI: oai:DiVA.org:uu-501092DiVA, id: diva2:1754935
Available from: 2023-05-05 Created: 2023-05-05 Last updated: 2023-05-05Bibliographically approved

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Nyberg, FredAndersson, Karl

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