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Web-based experimentation with uncompensated and unsupervised samples allows for a larger and more diverse sample population, more generalizable results, and faster theory to experiment cycle. Given that participants are unsupervised, it is still unknown whether the data collected in such settings would be of sufficiently high quality to support robust conclusions. Therefore, we investigated the feasibility of conducting such experiments online using virtual environment technologies. We conducted a conceptual replication of two prior experiments that have been conducted in virtual environments. Our results replicate findings previously obtained in conventional laboratory settings. These results hold across different device types of participants (ranging from desktop, through mobile devices to immersive virtual reality headsets), suggesting that experiments can be conducted online with uncompensated samples in virtual environments.

Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians’ treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians’ treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.

The “Reading the Mind in the Eyes” Test (Eyes Test) is a widely used assessment of “theory of mind.” The NIMH Research Domain Criteria recommends it as one of two tests for “understanding mental states.” Previous studies have demonstrated an on-average female advantage on the Eyes Test. However, it is unknown whether this female advantage exists across the lifespan and across a large number of countries. Thus, we tested sex and age differences using the English version of the Eyes Test in adolescents and adults across 57 countries. We also tested for associations with sociodemographic and cognitive/personality factors. We leveraged one discovery dataset (N = 305,726) and three validation datasets (Ns = 642; 5,284; and 1,087). The results show that: i) there is a replicable on-average female advantage in performance on the Eyes Test; ii) performance increases through adolescence and shallowly declines across adulthood; iii) the on-average female advantage is evident across the lifespan; iv) there is a significant on-average female advantage in 36 out of 57 countries; v) there is a significant on-average female advantage on translated (non-English) versions of the Eyes Test in 12 out of 16 countries, as confirmed by a systematic review; vi) D-scores, or empathizing-systemizing, predict Eyes Test performance above and beyond sex differences; and vii) the female advantage is negatively linked to “prosperity” and “autonomy,” and positively linked to “collectivism,” as confirmed by exploratory country-level analyses. We conclude that the on-average female advantage on the Eyes Test is observed across ages and most countries.

Care coordination is challenging but crucial for children with medical complexity (CMC). Technology-based solutions are increasingly prevalent but little is known about how to successfully deploy them in the care of CMC. The aim of this study was to assess the feasibility and acceptability of GoalKeeper (GK), an internet-based system for eliciting and monitoring family-centered goals for CMC, and to identify barriers and facilitators to implementation. We used the Consolidated Framework for Implementation Research (CFIR) to explore the barriers and facilitators to the implementation of GK as part of a clinical trial of GK in ambulatory clinics at a children’s hospital (NCT03620071). The study was conducted in 3 phases: preimplementation, implementation (trial), and postimplementation. For the trial, we recruited providers at participating clinics and English-speaking parents of CMC<12 years of age with home internet access. All participants used GK during an initial clinic visit and for 3 months after. We conducted preimplementation focus groups and postimplementation semistructured exit interviews using the CFIR interview guide. Participant exit surveys assessed GK feasibility and acceptability on a 5-point Likert scale. For each interview, 3 independent coders used content analysis and serial coding reviews based on the CFIR qualitative analytic plan and assigned quantitative ratings to each CFIR construct (–2 strong barrier to +2 strong facilitator). Preimplementation focus groups included 2 parents (1 male participant and 1 female participant) and 3 providers (1 in complex care, 1 in clinical informatics, and 1 in neurology). From focus groups, we developed 3 implementation strategies: education (parents: 5-minute demo; providers: 30-minute tutorial and 5-minute video on use in a clinic visit; both: instructional manual), tech support (in-person, virtual), and automated email reminders for parents. For implementation (April 1, 2019, to December 21, 2020), we enrolled 11 providers (7 female participants, 5 in complex care) and 35 parents (mean age 38.3, SD 7.8 years; n=28, 80% female; n=17, 49% Caucasian; n=16, 46% Hispanic; and n=30, 86% at least some college). One parent-provider pair did not use GK in the clinic visit, and few used GK after the visit. In 18 parent and 9 provider exit interviews, the key facilitators were shared goal setting, GK’s internet accessibility and email reminders (parents), and GK’s ability to set long-term goals and use at the end of visits (providers). A key barrier was GK’s lack of integration into the electronic health record or patient portal. Most parents (13/19) and providers (6/9) would recommend GK to their peers. Family-centered technologies like GK are feasible and acceptable for the care of CMC, but sustained use depends on integration into electronic health records.

With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen’s d = 1.48) and fewer high intensity movements (p < 0.001; Cohen’s d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen’s d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71–0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67–0.92; p < 0.01), demonstrated high reliability (r: 0.86–0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.

Study participants want to receive their biomonitoring results for environmental chemicals, and ethics guidelines encourage reporting back. However, few studies have quantitively assessed participants' responses to individual exposure reports, and digital methods have not been evaluated. We isolated effects of receiving personal results vs. only study-wide findings and investigated whether effects differed for Black participants. We randomly assigned a subset of 295 women from the Child Health and Development Studies, half of whom were Black, to receive a report with personal environmental chemical results or only study-wide (aggregate) findings. Reports included results for 42 chemicals and lipids and were prepared using the Digital Exposure Report-Back Interface (DERBI). Women were interviewed before and after viewing their report. We analyzed differences in website activity, emotional responses, and intentions to participate in future research by report type and race using Wilcoxon rank sum tests, Wilcoxon-Pratt signed ranks tests, and multiple regression. The personal report group spent approximately twice as much time on their reports as the aggregate group before the post-report-back interview. Among personal-report participants ( ), 84% (78) viewed chemical group information for at least one personal result highlighted on their home page; among aggregate-report participants ( ), 66% (62) viewed any chemical group page. Both groups reported strong positive feelings (curious, informed, interested, respected) about receiving results before and after report-back and mild negative feelings (helpless, scared, worried). Although most participants remained unworried after report-back, worry increased by a small amount in both groups. Among Black participants, higher post report-back worry was associated with having high levels of chemicals. Participants were motivated by their personal results to access online information about chemical sources and potential health effects. Report-back was associated with a small increase in worry, which could motivate appropriate action. Personal report-back increased engagement with exposure reports among Black participants. https://doi.org/10.1289/EHP9072.

Researchers and clinicians in environmental health and medicine increasingly show respect for participants and patients by involving them in decision-making. In this context, the return of personal results to study participants is becoming ethical best practice, and many participants now expect to see their data. However, researchers often lack the time and expertise required for report-back, especially as studies measure greater numbers of analytes, including many without clear health guidelines. In this article, our goal is to demonstrate how a prototype digital method, the Digital Exposure Report-Back Interface (DERBI), can reduce practical barriers to high-quality report-back. DERBI uses decision rules to automate the production of personalized summaries of notable results and generates graphs of individual results with comparisons to the study group and benchmark populations. Reports discuss potential sources of chemical exposure, what is known and unknown about health effects, strategies for exposure reduction, and study-wide findings. Researcher tools promote discovery by drawing attention to patterns of high exposure and offer novel ways to increase participant engagement. DERBI reports have been field tested in two studies. Digital methods like DERBI reduce practical barriers to report-back thus enabling researchers to meet their ethical obligations and participants to get knowledge they can use to make informed choices.

Adaptive graphical user interfaces (GUIs) have the potential to improve performance and user satisfaction by automatically tailoring the presentation of functionality to each individual user. In practice, however, many challenges exist, and evaluation results of adaptive GUIs have been mixed. To guide researchers and designers in developing effective adaptive GUIs, we outline a design space and discuss three important aspects to consider when conducting user evaluations of these types of interfaces: the control and reporting of adaptive algorithm characteristics, the impact of task choice and user characteristics on the overall effectiveness of a design, and evaluation measures that are appropriate for adaptive interaction.

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and only then saw a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decision. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to include explanations together with AI-generated recommendation to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.