Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Constructing and Predicting School Advice for Academic Achievement: A Comparison of Item Response Theory and Machine Learning Techniques

Educational tests can be used to estimate pupils’ abilities and thereby give an indication of whether their school type is suitable for them. However, tests in education are usually conducted for each content area separately which makes it difficult to combine these results into one single school advice. To this end, we provide a comparison between both domain-specific and domain-agnostic methods for predicting school advice. Both use data from a pupil monitoring system in the Netherlands, which keeps track of pupils’ educational progress over several years by a series of tests measuring multiple skills. An IRT model is calibrated from which an ability score is extracted and is subsequently plugged into a multinomial log- linear regression model. Second, we train a random forest (RF) and a shallow neural network (NN) and apply case weighting to give extra attention to pupils who switched between school types. When considering the performance of all pupils, RFs provided the most accurate predictions followed by NNs and IRT respectively. When only looking at the performance of pupils who switched school type, IRT performed best followed by NNs and RFs. Case weighting proved to provide a major improvement for this group. Lastly, IRT was found to be much easier to explain in comparison to the other models. Thus, while ML provided more accurate results, this comes at the cost of a lower explainability in comparison to IRT

m-Path: An easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice

In this paper, we present m-Path (www.m-Path.io), an online platform that provides a user-friendly and flexible framework for implementing smartphone-based ecological momentary assessment (EMA) and intervention (EMI) in both research and clinical practice in the context of blended care. Because real-time monitoring and intervention in people’s everyday lives have unparalleled benefits compared to traditional data collection techniques (e.g., experiments or retrospective surveys), EMA and EMI have become popular in recent years. On the one hand, the surge in use of these methods requires software that allows for increasingly complex designs and functionalities. On the other hand, EMA and EMI platforms should remain easy to use and accessible for researchers and clinicians with limited programming skills. m-Path accommodates to both of these needs, offering an intuitive web interface to set up highly tailorable smartphone-based EMA and EMI protocols. In this article, we review the strengths of daily life data collection and intervention in general and m-Path in particular. We discuss the regular workflow to design an EMA or EMI protocol within the m-Path framework, and summarize both the basic functionalities and more advanced features of our software

Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm

The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities clustered into four protocols (i.e., natural, strict, healthcare provider, and bed cycling). A video served as the reference standard, with two raters classifying the type and duration of all activities. This classification was reliable as intraclass correlations were all above 0.76 except for walking in the healthcare provider protocol, (0.29). The data of four participants were used to develop and optimize the algorithm by adjusting body-segment angles and rest-activity-threshold values based on percentage agreement (%Agr) with the reference. The validity of the algorithm was subsequently assessed using the data from the remaining six participants. %Agr of the algorithm versus the reference standard regarding lying, sitting activities, and transitions was 95%, 74%, and 80%, respectively, for all protocols except transitions with the help of a healthcare provider, which was 14–18%. For bed cycling, %Agr was 57–76%. This study demonstrated that the developed algorithm is suitable for identifying and quantifying activities common for intensive care patients. Knowledge on the (in)activity of these patients and their impact will optimize mobilization

Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm

The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities clustered into four protocols (i.e., natural, strict, healthcare provider, and bed cycling). A video served as the reference standard, with two raters classifying the type and duration of all activities. This classification was reliable as intraclass correlations were all above 0.76 except for walking in the healthcare provider protocol, (0.29). The data of four participants were used to develop and optimize the algorithm by adjusting body-segment angles and rest-activity-threshold values based on percentage agreement (%Agr) with the reference. The validity of the algorithm was subsequently assessed using the data from the remaining six participants. %Agr of the algorithm versus the reference standard regarding lying, sitting activities, and transitions was 95%, 74%, and 80%, respectively, for all protocols except transitions with the help of a healthcare provider, which was 14–18%. For bed cycling, %Agr was 57–76%. This study demonstrated that the developed algorithm is suitable for identifying and quantifying activities common for intensive care patients. Knowledge on the (in)activity of these patients and their impact will optimize mobilization

Designing daily-life research combining experience sampling method with parallel data

BACKGROUND: Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. METHODS: Literature and expert opinions were integrated to formulate relevant guiding principles. RESULTS: Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. CONCLUSIONS: The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures

Constructing and Predicting School Advice for Academic Achievement: A Comparison of Item Response Theory and Machine Learning Techniques

Educational tests can be used to estimate pupils’ abilities and thereby give an indication of whether their school type is suitable for them. However, tests in education are usually conducted for each content area separately which makes it difficult to combine these results into one single school advice. To this end, we provide a comparison between both domain-specific and domain-agnostic methods for predicting school advice. Both use data from a pupil monitoring system in the Netherlands, which keeps track of pupils’ educational progress over several years by a series of tests measuring multiple skills. An IRT model is calibrated from which an ability score is extracted and is subsequently plugged into a multinomial log- linear regression model. Second, we train a random forest (RF) and a shallow neural network (NN) and apply case weighting to give extra attention to pupils who switched between school types. When considering the performance of all pupils, RFs provided the most accurate predictions followed by NNs and IRT respectively. When only looking at the performance of pupils who switched school type, IRT performed best followed by NNs and RFs. Case weighting proved to provide a major improvement for this group. Lastly, IRT was found to be much easier to explain in comparison to the other models. Thus, while ML provided more accurate results, this comes at the cost of a lower explainability in comparison to IRT

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers are faced with multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Here we propose a preregistration template that is easy to use and understandable for researchers