Personal Characteristics Associated with Ecological Momentary Assessment Compliance in Adult Cochlear Implant Candidates and Users.

Abstract

Background: Ecological momentary assessment (EMA) often places high physical and mental burden on research participants compared with retrospective self-reports. The high burden could result in noncompliance with the EMA sampling scheme protocol. It has been a concern that certain types of participants could be more likely to have low compliance, such as those who have severe hearing loss and poor speech recognition performance, are employed, are not familiar with technologies used to implement EMA (e.g., smartphones), and have poorer cognitive abilities. Noncompliance dependent on personal characteristics could negatively impact the generalizability of EMA research.

Purpose: This article aims to determine personal characteristics associated with EMA compliance in a group of adult cochlear implant (CI) candidates and users.

Research design: An observational study.

Study sample: Fifty-eight adults who were either scheduled to received CIs or were experienced CI users completed the study.

Data collection and analysis: Participants conducted smartphone-based EMA designed to assess an individual's daily auditory ecology for 1 week. EMA compliance was quantified using two metrics: the number of completed surveys and the response rate to the notification delivered by the EMA app. Personal characteristics (i.e., predictors) included age, gender, CI status (candidate or user), employment status (employed or not employed), smartphone ownership, speech recognition performance, social network size, level of depressive symptoms, and neurocognitive abilities. A word recognition test, questionnaires, and a test battery of neurocognitive assessments were used to measure the predictors. We used negative binomial regression and logistic mixed models to determine the factors associated with the number of completed surveys and the response rate, respectively. We hypothesized that, for example, employed participants with poorer speech recognition performance would have lower compliance.

Results: Contrary to the hypothesis, word recognition score was negatively associated with the number of completed surveys (p = 0.022). Holding all other variables constant, a 10-point (i.e., 10%) word recognition score decrease was associated with an 11% increase in the number of completed surveys. For the response rate, employment status was the only significant predictor (p < 0.0001). Consistent with our hypothesis, the odds of responding to EMA notifications for those who are not employed are 82% higher than the odds for those who are employed. No other studied personal characteristic was associated with compliance.

Conclusion: For CI candidates and users, EMA compliance could be affected by personal characteristics such as speech recognition performance and employment status. Because (1) participants with poorer speech recognition performance do not necessarily have lower compliance and (2) most personal characteristics investigated in the present study (e.g., age, gender, smartphone ownership, and neurocognitive abilities) do not predict compliance, a wide range of participants could successfully conduct smartphone-based EMA.