1,049
Views
16
CrossRef citations to date
0
Altmetric
Perspectives in Rehabilitation

Ecological momentary assessment for rehabilitation of chronic illness and disability

, , , &
Pages 974-987 | Received 25 Aug 2016, Accepted 06 Jan 2017, Published online: 07 Feb 2017
 

Abstract

Purpose: The main objectives of this manuscript were to provide a theoretical perspective on naturalistic delivery in rehabilitation based upon a literature review and establish a rationale for using ecological momentary assessment (EMA) for naturalistic assessment for chronic illness and disability (CID) in rehabilitation.

Method: Existing literature on EMA use across CID cohorts was gathered and analyzed to form a theoretical overview of implementation of this method in research. This review summarizes study results and provides a comprehensive literature table for greater analysis.

Results: EMA has been shown to optimize clinician time and reduce costs, reach greater numbers of people with disability-related needs, and reduce the need for retrospective recall through the collection of more objective data. Mixed method approaches were most commonly seen in the literature, and sampling schedules and the outcomes assessed varied widely.

Conclusions: EMA is emerging as a novel modality of assessment in rehabilitation. Scientists and clinicians should consider incorporating this assessment approach as a rehabilitation tool that may more accurately assess the complex and dynamic nature of disability over the long-term through an objective and ecologically-valid data source.

    Implications for rehabilitation

  • Ecological momentary assessment (EMA) has been underutilized in the rehabilitation field and should be considered by researchers and clinicians as a novel assessment method for capturing rich, ecologically-valid data.

  • EMA methods provide a greater capability to assess complex or difficult to measure outcomes of interest when compared with more traditional approaches conducted during finite clinic hours due to data collection occurring, with or without any input from the user, through wearable technology, and without a needed clinician presence.

  • EMA data can be integrated with other data sources (e.g., self-report or clinician observation) to assess a more comprehensive picture of outcomes of interest, including highlighting discordance and identifying the most efficient target areas for intervention.

Disclosure statement

The authors report no declarations of interest.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 374.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.