Attention to participant preferences when selecting outcome measures may result in better study recruitment, retention and representativeness, assuming psychometric rigor can simultaneously be maintained. In particular, rural communities are under-represented in research ( 11), with declination also being related to inconvenience and travel cost ( 12). This is magnified for individuals who are homebound or have limited mobility. In a review of reasons for declining research study participation in NIH intramural research ( 10), 33% of decliners cited inconvenience as the primary reason (e.g., inability to take time off of work inability to travel to the Clinical Center distance to NIH lack of flexibility in the participant’s schedule and inability to participate during the work week and/or during work hours). Research that occurs in large urban medical centers is often not accessible to non-urban patients who may lack resources or willingness to participate. In addition to economic and logistical concerns, there are growing concerns about the representativeness of samples included in cognitive research, with particular concern about disparities in study enrollment based on dwelling location, socioeconomic status, race and ethnicity, comorbidities, gender, and age ( 9). Self-administered online (SAO) cognitive assessment addresses these economic and efficiency barriers by allowing patients to complete testing with no human supervision within their own homes using their personal electronic devices. While allowing for remote testing, teleneuropsychology requires skilled test administration and supervision via videoconferencing. Teleneuropsychology, or the use of video conferencing to administer traditional neuropsychology tests, has been shown to produce equivalent results to in person testing for many verbally administered measures ( 5 6), although the evidence for non-verbal processing speed and executive functioning tests is limited (i.e., few studies and small sample sizes) ( 7 8). Many existing computerized cognitive tests, however, remain limited by the need for costly technology and skilled test administration in a clinical setting (e.g., NIH Toolbox Cognition Battery). Therefore, there is a need for developing and validating remote cognitive assessment approaches that could allow for greater efficiency in conducting clinical trials.Ĭomputerized cognitive assessment is often perceived as less difficult and distressing to patients ( 3), increases efficiency, and reduces associated costs ( 4). Variables that are not possible to measure remotely, such as cognition via in-person traditional neuropsychological testing, are not feasible in these novel trial designs. Increasingly, researchers are exploring remote data collection (e.g., continuous glucose monitoring, GPS, physical activity tracking) and delivery of interventions (e.g., mHealth interventions for people with diabetes ( 1 2). These costs are magnified in longitudinal study designs, such as those involved in clinical trials. Measures traditionally in use are resource intensive and costly, as they require extensive examiner training and supervision, extended time occupying lab or clinic space, and complex scoring procedures that may introduce human error or bias. However, several limitations of traditional neuropsychological approaches make application for clinical research a challenge. In recent years there has been a growing accumulation of evidence that many systemic diseases, medications, and medical procedures adversely impact cognitive function.
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