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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.
Zetterström, Andreas; Hämäläinen, Markku D; Karlberg, Elin; Winkvist, Maria; Söderquist, Marcus; Öhagen, Patrik; Andersson, Karl; Nyberg, Fred.
Afiliação
  • Zetterström A; Kontigo Care AB, Påvel Snickares Gränd 12, Uppsala, Sweden.
  • Hämäläinen MD; Kontigo Care AB, Påvel Snickares Gränd 12, Uppsala, Sweden.
  • Karlberg E; Innovation Akademiska, Uppsala University Hospital, Uppsala, Sweden.
  • Winkvist M; Kontigo Care AB, Påvel Snickares Gränd 12, Uppsala, Sweden.
  • Söderquist M; Kontigo Care AB, Påvel Snickares Gränd 12, Uppsala, Sweden.
  • Öhagen P; Uppsala Clinical Research Center, Dag Hammarskjölds väg 14 B, Uppsala Science Park, Uppsala, Sweden.
  • Andersson K; Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
  • Nyberg F; Ridgeview Instruments AB, Skillsta 4, Vänge, Sweden.
Alcohol Alcohol ; 54(1): 70-72, 2019 Jan 01.
Article em En | MEDLINE | ID: mdl-30541059
ABSTRACT

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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Detecção do Abuso de Substâncias / Cooperação do Paciente / Telemedicina / Alcoolismo Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Detecção do Abuso de Substâncias / Cooperação do Paciente / Telemedicina / Alcoolismo Idioma: En Ano de publicação: 2019 Tipo de documento: Article