At present, there are many studies that collect information on aspects of neurologic and behavioral function (cognition, sensation, movement, emotion), but with little uniformity among the measures used to capture these constructs. Further, available measures are generally expensive, normed on homogenous nondiverse populations, not easily administered, do not cover the lifespan (or have easily linked pediatric and adult counterparts for the purposes of longitudinal comparison), and not based on the current thinking in the neuroscience community. There is also a paucity of measurement tools to gauge normal children in the motor and sensation domain areas, and many of these measures rely heavily on proxy reporting. Investigators have expressed the need for brief assessment tools that could address these issues and be used as a form of "common currency" across diverse study designs and populations. This ability to assess functionality along a common metric and "crosswalk" across measures is essential to the process of being able to pool data, which is often necessary when a large and diverse sample is needed. When individual studies employ unique assessment batteries, comparisons between studies and combining data from multiple studies can be problematic. The contract for the NIH Toolbox for the Assessment of Neurological and Behavioral Function (www.nihtoolbox.org) was initiated by the NIH Blueprint for Neuroscience Research (www.neuroscienceblueprint.nih.gov) to develop a set of state-of-the-art measurement tools to enhance collection of data in large cohort studies and to advance the biomedical research enterprise.

Background: Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps. Objective: This study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention. Methods: We conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention. Results: Of the 389 identified studies, 62 (15.9%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained. Conclusions: Retaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention.

Language facilitates communication and efficient encoding of thought and experience. Because of its essential role in early childhood development, in educational achievement and in subsequent life adaptation, language was included as one of the subdomains in the NIH Toolbox for the Assessment of Neurological and Behavioral Function Cognition Battery (NIHTB-CB). There are many different components of language functioning, including syntactic processing (i.e., morphology and grammar) and lexical semantics. For purposes of the NIHTB-CB, two tests of language—a picture vocabulary test and a reading recognition test—were selected by consensus based on literature reviews, iterative expert input, and a desire to assess in English and Spanish. NIHTB-CB’s picture vocabulary and reading recognition tests are administered using computer adaptive testing and scored using item response theory. Data are presented from the validation of the English versions in a sample of adults ages 20–85 years (Spanish results will be presented in a future publication). Both tests demonstrated high test–retest reliability and good construct validity compared to corresponding gold-standard measures. Scores on the NIH Toolbox measures were consistent with age-related expectations, namely, growth in language during early development, with relative stabilization into late adulthood. (JINS, 2014,20, 1–10)