The Ubiquitous Cognitive Assessment Tool for Smartwatches: Design, Implementation, and Evaluation Study.

BACKGROUND: Cognitive functioning plays a significant role in individuals' mental health, since fluctuations in memory, attention, and executive functions influence their daily task performance. Existing digital cognitive assessment tools cannot be administered in the wild and their test sets are not brief enough to capture frequent fluctuations throughout the day. The ubiquitous availability of mobile and wearable devices may allow their incorporation into a suitable platform for real-world cognitive assessment. OBJECTIVE: The aims of this study were threefold: (1) to evaluate a smartwatch-based tool for the assessment of cognitive performance, (2) to investigate the usability of this tool, and (3) to understand participants' perceptions regarding the application of a smartwatch in cognitive assessment. METHODS: We built the Ubiquitous Cognitive Assessment Tool (UbiCAT) on a smartwatch-based platform. UbiCAT implements three cognitive tests-an Arrow test, a Letter test, and a Color test-adapted from the two-choice reaction-time, N-back, and Stroop tests, respectively. These tests were designed together with domain experts. We evaluated the UbiCAT test measures against standard computer-based tests with 21 healthy adults by applying statistical analyses significant at the 95% level. Usability testing for each UbiCAT app was performed using the Mobile App Rating Scale (MARS) questionnaire. The NASA-TLX (Task Load Index) questionnaire was used to measure cognitive workload during the N-back test. Participants rated perceived discomfort of wearing a smartwatch during the tests using a 7-point Likert scale. Upon finishing the experiment, an interview was conducted with each participant. The interviews were transcribed and semantic analysis was performed to group the findings. RESULTS: Pearson correlation analysis between the total correct responses obtained from the UbiCAT and the computer-based tests revealed a significant strong correlation (r=.78, P<.001). One-way analysis of variance (ANOVA) showed a significant effect of the N-back difficulty level on the participants' performance measures. The study also demonstrated usability ratings above 4 out of 5 in terms of aesthetics, functionality, and information. Low discomfort (<3 out of 7) was reported by our participants after using the UbiCAT. Seven themes were extracted from the transcripts of the interviews conducted with our participants. CONCLUSIONS: UbiCAT is a smartwatch-based tool that assesses three key cognitive domains. Usability ratings showed that participants were engaged with the UbiCAT tests and did not feel any discomfort. The majority of the participants were interested in using the UbiCAT, although some preferred computer-based tests, which might be due to the widespread use of personal computers. The UbiCAT can be administered in the wild with mentally ill patients to assess their attention, working memory, and executive function.

The Internet-Based Cognitive Assessment Tool: System Design and Feasibility Study.

BACKGROUND: Persistent cognitive impairment is prevalent in unipolar and bipolar disorders and is associated with decreased quality of life and psychosocial dysfunction. The screen for cognitive impairment in psychiatry (SCIP) test is a validated paper-and-pencil instrument for the assessment of cognition in affective disorders. However, there is no digital cognitive screening tool for the brief and accurate assessment of cognitive impairments in this patient group. OBJECTIVE: In this paper, we present the design process and feasibility study of the internet-based cognitive assessment tool (ICAT) that is designed based on the cognitive tasks of the SCIP. The aims of this feasibility study were to perform the following tasks among healthy individuals: (1) evaluate the usability of the ICAT, (2) investigate the feasibility of the ICAT as a patient-administered cognitive assessment tool, and (3) examine the performance of automatic speech recognition (ASR) for the assessment of verbal recall. METHODS: The ICAT was developed in a user-centered design process. The cognitive measures of the ICAT were immediate and delayed recall, working memory, and psychomotor speed. Usability and feasibility studies were conducted separately with 2 groups of healthy individuals (N=21 and N=19, respectively). ICAT tests were available in the English and Danish languages. The participants were asked to fill in the post study system usability questionnaire (PSSUQ) upon completing the ICAT test. Verbal recall in the ICAT was assessed using ASR, and the performance evaluation criterion was word error rate (WER). A Pearson 2-tailed correlation analysis significant at the .05 level was applied to investigate the association between the SCIP and ICAT scores. RESULTS: The overall psychometric factors of PSSUQ for both studies gave scores above 4 (out of 5). The analysis of the feasibility study revealed a moderate to strong correlation between the total scores of the SCIP and ICAT (r=0.63; P=.009). There were also moderate to strong correlations between the SCIP and ICAT subtests for immediate verbal recall (r=0.67; P=.002) and psychomotor speed (r=0.71; P=.001). The associations between the respective subtests for working memory, executive function, and delayed recall, however, were not statistically significant. The corresponding WER for English and Danish responses were 17.8% and 6.3%, respectively. CONCLUSIONS: The ICAT is the first digital screening instrument modified from the SCIP using Web-based technology and ASR. There was good accuracy of the ASR for verbal memory assessment. The moderate correlation between the ICAT and SCIP scores suggests that the ICAT is a valid tool for assessing cognition, although this should be confirmed in a larger study with greater statistical power. Taken together, the ICAT seems to be a valid Web-based cognitive assessment tool that, after some minor modifications and further validation, may be used to screen for cognitive impairment in clinical settings.

Predicting Implantation Outcome of In Vitro Fertilization and Intracytoplasmic Sperm Injection Using Data Mining Techniques.

BACKGROUND: In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are two important subsets of the assisted reproductive techniques, used for the treatment of infertility. Predicting implantation outcome of IVF/ICSI or the chance of pregnancy is essential for infertile couples, since these treatments are complex and expensive with a low probability of conception. MATERIALS AND METHODS: In this cross-sectional study, the data of 486 patients were collected using census method. The IVF/ICSI dataset contains 29 variables along with an identifier for each patient that is either negative or positive. Mean accuracy and mean area under the receiver operating characteristic (ROC) curve are calculated for the classifiers. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios of classifiers are employed as indicators of performance. The state-of-art classifiers which are candidates for this study include support vector machines, recursive partitioning (RPART), random forest (RF), adaptive boosting, and one-nearest neighbor. RESULTS: RF and RPART outperform the other comparable methods. The results revealed the areas under the ROC curve (AUC) as 84.23 and 82.05%, respectively. The importance of IVF/ICSI features was extracted from the output of RPART. Our findings demonstrate that the probability of pregnancy is low for women aged above 38. CONCLUSION: Classifiers RF and RPART are better at predicting IVF/ICSI cases compared to other decision makers that were tested in our study. Elicited decision rules of RPART determine useful predictive features of IVF/ICSI. Out of 20 factors, the age of woman, number of developed embryos, and serum estradiol level on the day of human chorionic gonadotropin administration are the three best features for such prediction.