This real‐data‐guided simulation study systematically evaluated the decision accuracy of complex decision rules combining multiple tests within different realistic curricula. Specifically, complex decision rules combining conjunctive aspects and compensatory aspects were evaluated. A conjunctive aspect requires a minimum level of performance, whereas a compensatory aspect requires an average level of performance. Simulations were performed to obtain students' true and observed score distributions and to manipulate several factors relevant to a higher education curriculum in practice. The results showed that the decision accuracy depends on the conjunctive (required minimum grade) and compensatory (required grade point average) aspects and their combination. Overall, within a complex compensatory decision rule the false negative rate is lower and the false positive rate higher compared to a conjunctive decision rule. For a conjunctive decision rule the reverse is true. Which rule is more accurate also depends on the average test reliability, average test correlation, and the number of reexaminations. This comparison highlights the importance of evaluating decision accuracy in high‐stake decisions, considering both the specific rule as well as the selected measures.

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Background Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique. Objective The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level. Methods The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the <T5 (midthoracic and lower) group, T5-T1 (high-thoracic) group, and >T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise. Results With an overall MAPEall lesions of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPEno SCI=8.09%; MAPE<T5=11.16%; MAPET1−T5=10.5%; and MAPE>T1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPErest=6.5%, MAPEactivity=12.97%, and MAPEstrength=14.2%). Conclusions HR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.

Background: Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. Objective: This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. Methods: This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. Results: This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. Conclusion: mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

In this study, the consequences of allowing course compensation in a higher education academic dismissal policy are evaluated by examining performance on a second-year follow-up (i.e. sequel) course that builds on material from a first-year precursor course. Up to now, differences in the consequences of compensation on student performance across groups of students who portray different unobserved study processes were not considered. In this study we used a latent class regression model to distinguish latent groups of students. Data from two undergraduate curricula were used and latent classes were formed based on similar patterns in averages, variability in grades, the number of compensated courses, and the number of retakes in the first year. Results show that students can be distinguished by three latent classes. Although the first-year precursor course is compensated in each of these latent classes, low performance on the precursor course results in low performance on the second-year sequel course for psychology students who belong to a class in which the average across first-year courses is low and the average number of compensated courses and retakes are high. For these students, compensation on a precursor course seems more likely to relate to insufficient performance on a sequel course.