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Maximum Time Between Tests: A Digital Biomarker to Detect Therapy Compliance and Assess Schedule Quality in Measurement-Based eHealth Systems for Alcohol Use Disorder.
Alcohol and Alcoholism 2019 January 2
Aim: To evaluate, in a breathalyzer-based eHealth system, whether the time-based digital biomarker 'maximum time between tests' (MTBT) brings valuable information on alcohol consumption patterns as confirmed by correlation with blood phosphatidyl ethanol (PEth), serum carbohydrate deficient transferrin (CDT) and timeline follow-back data.
Method: Data on 54 patients in follow-up for treatment of alcohol use disorder were analysed.
Results: The model of weekly averages of 24-log transformed MTBT adequately described timeline follow-back data (P < 0.0001, R = 0.27-0.38, n = 650). Significant correlations were noted between MTBT and PEth (P < 0.0001, R = 0.41, n = 148) and between MTBT and CDT (P < 0.0079, R = 0.22, n = 120).
Conclusions: The time-based digital biomarker 'maximum time between tests' described here has the potential to become a generally useful metric for all scheduled measurement-based eHealth systems to monitor test behaviour and compliance, factors important for 'dosing' of eHealth systems and for early prediction and interventions of lapse/relapse.
Method: Data on 54 patients in follow-up for treatment of alcohol use disorder were analysed.
Results: The model of weekly averages of 24-log transformed MTBT adequately described timeline follow-back data (P < 0.0001, R = 0.27-0.38, n = 650). Significant correlations were noted between MTBT and PEth (P < 0.0001, R = 0.41, n = 148) and between MTBT and CDT (P < 0.0079, R = 0.22, n = 120).
Conclusions: The time-based digital biomarker 'maximum time between tests' described here has the potential to become a generally useful metric for all scheduled measurement-based eHealth systems to monitor test behaviour and compliance, factors important for 'dosing' of eHealth systems and for early prediction and interventions of lapse/relapse.
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