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. 2022 Jul 14;17(7):e0271465.
doi: 10.1371/journal.pone.0271465. eCollection 2022.

Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring

Andreas Zetterström  1 , Gunnar Dahlberg  1 , Sara Lundqvist  1 , Markku D Hämäläinen  1 , Maria Winkvist  1 , Fred Nyberg  2 , Karl Andersson  3   4
Affiliations

Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring

Andreas Zetterström et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

Markku D. Hämäläinen, Gunnar Dahlberg, Andreas Zetterström, Maria Winkvist, are all employees of Kontigo Care AB. Fred Nyberg is member of the scientific advisory committee of Kontigo Care AB. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The data was collected and provided for the study by Kontigo Care. Markku D. Hämäläinen, Gunnar Dahlberg, Andreas Zetterström, Maria Winkvist receives their salaries from of Kontigo Care AB. Fred Nyberg, Karl Andersson and Sara Lundberg did not receive any specific funding for this work.

Figures

Fig 1
Fig 1
Distribution of averaged wellbeing data, WeBe, and motivation and self-confidence data, MotSC, (top) and the corresponding digital biomarkers, WeBe-i and MotSC-i (bottom).
Fig 2
Fig 2. Depicting the clinical course of patients using wellbeing and motivation/self-confidence data.
The average/digital biomarker view of wellbeing (WeBe/WeBe-i), motivation/self-confidence (MotSc/MotSc-i), and Addiction Monitoring Index (AMI) data for 3 patients (A-C) as time series during 4 months (x-axis = Treatment day). Symbols: Green circle = no alcohol detected; Red square = alcohol detected; Black diamond = all breathalyzer tests omitted.
See this image and copyright information in PMC

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