• Ashlee McKeon, Michael McCue, Elizabeth Skidmore, Michelle Schein, and Jamie Kulzer.
• a Department of Rehabilitation Science & Technology , University of Pittsburgh , Pittsburgh , PA , USA.
• Disabil Rehabil. 2018 Apr 1; 40 (8): 974-987.

PurposeThe 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.MethodExisting 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.ResultsEMA 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.ConclusionsEMA 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.

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