Literature DB >> 27498066

Let's get Physiqual - An intuitive and generic method to combine sensor technology with ecological momentary assessments.

F J Blaauw1, H M Schenk2, B F Jeronimus3, L van der Krieke4, P de Jonge5, M Aiello6, A C Emerencia7.   

Abstract

The emergence of wearables and smartwatches is making sensors a ubiquitous technology to measure daily rhythms in physiological measures, such as movement and heart rate. An integration of sensor data from wearables and self-report questionnaire data about cognition, behaviors, and emotions can provide new insights into the interaction of mental and physiological processes in daily life. Hitherto no method existed that enables an easy-to-use integration of sensor and self-report data. To fill this gap, we present 'Physiqual', a platform for researchers that gathers and integrates data from commercially available sensors and service providers into one unified format for use in Ecological Momentary Assessments (EMA) or Experience Sampling Methods (ESM), and Quantified Self (QS). Physiqual currently supports sensor data provided by two well-known service providers and therewith a wide range of smartwatches and wearables. To demonstrate the features of Physiqual, we conducted a case study in which we assessed two subjects by means of data from an EMA study combined with sensor data as aggregated and exported by Physiqual. To the best of our knowledge, the Physiqual platform is the first platform that allows researchers to conveniently aggregate and integrate physiological sensor data with EMA studies.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  API; Android; EMA; ESM; Fitbit; Google Fit; Interoperability; Platform; Smartwatch; Wearables

Mesh:

Year:  2016        PMID: 27498066     DOI: 10.1016/j.jbi.2016.08.001

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  7 in total

1.  A smartwatch-based framework for real-time and online assessment and mobility monitoring.

Authors:  Matin Kheirkhahan; Sanjay Nair; Anis Davoudi; Parisa Rashidi; Amal A Wanigatunga; Duane B Corbett; Tonatiuh Mendoza; Todd M Manini; Sanjay Ranka
Journal:  J Biomed Inform       Date:  2018-11-07       Impact factor: 6.317

2.  Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.

Authors:  Mingrui Liang; Matthew D Koslovsky; Emily T Hébert; Darla E Kendzor; Michael S Businelle; Marina Vannucci
Journal:  Psychol Methods       Date:  2021-12-20

3.  A SURVEY OF SMARTWATCHES IN REMOTE HEALTH MONITORING.

Authors:  Christine E King; Majid Sarrafzadeh
Journal:  J Healthc Inform Res       Date:  2017-12-18

4.  WaaS architecture-driven depressive mood status quantitative analysis based on forehead EEG and self-rating tool.

Authors:  Zhijiang Wan; Hao Zhang; Jianhui Chen; Haiyan Zhou; Jie Yang; Ning Zhong
Journal:  Brain Inform       Date:  2018-12-05

Review 5.  Potential benefits of integrating ecological momentary assessment data into mHealth care systems.

Authors:  Jinhyuk Kim; David Marcusson-Clavertz; Kazuhiro Yoshiuchi; Joshua M Smyth
Journal:  Biopsychosoc Med       Date:  2019-08-09

6.  Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources.

Authors:  Sebastian Neubert; André Geißler; Thomas Roddelkopf; Regina Stoll; Karl-Heinz Sandmann; Julius Neumann; Kerstin Thurow
Journal:  Int J Telemed Appl       Date:  2019-10-08

Review 7.  Single-Subject Research in Psychiatry: Facts and Fictions.

Authors:  Marij Zuidersma; Harriëtte Riese; Evelien Snippe; Sanne H Booij; Marieke Wichers; Elisabeth H Bos
Journal:  Front Psychiatry       Date:  2020-11-13       Impact factor: 4.157

  7 in total

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