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Can Activity Monitors Predict Outcomes in Patients with Heart Failure? A Systematic Review.

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Can Activity Monitors Predict Outcomes in Patients with Heart Failure? A Systematic Review.

Eur Heart J Qual Care Clin Outcomes. 2018 Sep 12;:

Authors: Tan MKH, Wong JKL, Bakrania K, Abdullahi Y, Harling L, Casula R, Rowlands AV, Athanasiou T, Jarral OA

Abstract
Background: Actigraphy is increasingly incorporated into clinical practice to monitor intervention effectiveness and patient health in congestive heart failure (CHF). We explored the prognostic impact of actigraphy-quantified physical activity (AQPA) on CHF outcomes.
Methods: PubMed and Medline databases were systematically searched for cross-sectional studies, cohort studies or randomised controlled trials from January 2007 to December 2017. We included studies that used validated actigraphs to predict outcomes in adult HF patients. Study selection and data extraction were performed by two independent reviewers.
Results: A total of 17 studies (15 cohort, 1 cross-sectional, 1 randomised controlled trial) were included, reporting on 2,759 CHF patients (22-89 years, 27.7% female). Overall, AQPA showed a strong inverse relationship with mortality and predictive utility when combined with established risk scores, and prognostic roles in morbidity, predicting cognitive function, New York Heart Association functional class and intercurrent events (e.g. hospitalisation), but weak relationships with health-related quality of life scores. Studies lacked consensus regarding device choice, time points and thresholds of PA measurement, which rendered quantitative comparisons between studies difficult.
Funding: No specific funding was provided for this review.
Conclusions: AQPA has a strong prognostic role in CHF. Multiple sampling time points would allow calculation of AQPA changes for incorporation into risk models. Consensus is needed regarding device choice and AQPA thresholds, while data management strategies are required to fully utilise generated data. Big data and machine learning strategies will potentially yield better predictive value of AQPA in CHF patients.
Registration: Nil.

PMID: 30215706 [PubMed – as supplied by publisher]

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