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The acceptability of real-time health monitoring among community participants with depression: A systematic review and meta-analysis of the literature

Research output: Contribution to journalReview article

Giovanni de Girolamo, Chiara Barattieri di San Pietro, Viola Bulgari, Jessica Dagani, Clarissa Ferrari, Matthew Hotopf, Giuseppe Iannone, Ambra Macis, Faith Matcham, Inez Myin-Germeys, Aki Rintala, Sara Simblett, Til Wykes, Cristina Zarbo

Original languageEnglish
JournalDepression and Anxiety
DOIs
Publication statusAccepted/In press - 1 Jan 2020

King's Authors

Abstract

Background: The application of experience sampling method/ecological momentary assessment (ESM/EMA) methods to individuals with major depressive disorder (MDD) seems promising, but evidence about their acceptability is still unclear. The aim of this systematic review and meta-analysis (registration number CRD42017060438) was to investigate the acceptability of ESM/EMA techniques for health monitoring in patients with MDD, by examining the dropout rate and related-reasons, and to explore the effects of individual, methodological, and technical features on dropping out. Method: According to PRISMA guidelines, after leading a systematic search on major electronic databases, a structured process for selecting and collecting data was followed. Results: A total of 19 studies were included in the analyses. From results, it emerged a dropout rate of 3.6%. Our findings showed that the use of paper and pencil tools in combination with electronic devices, the time-based sampling method, and not providing monetary incentives significantly increase the dropout rate of patients with MDD during ESM/EMA monitoring. Age, gender, depression severity, duration of monitoring, number of assessments each day, and number of questions did not affect dropout rate. Conclusions: The results of this systematic review may assist clinicians and researchers in planning, implementing, or evaluating the use of ESM/EMA to assess the health status of community-based individuals with MDD.

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