Abstract: Fusion multidisciplinary subjects in order to present a unique and complementary perspective may enhance and extend the knowledge acquisition, and consequently, the student experience. In this paper we report a case study on methodology, which interlinks various subjects creating and engaging learning process by combining Science, Technology, Engineering and Mathematics (STEM) and Design. In line with this, an Experimental Interaction Design summer course was designed and implemented based on a multi-cultural, defiant and creative environment in order to provide an effective learning on solving the real-world challenges. Twelve students were involved in this initiative, which resulted in the development of several projects such as a pets’ tracking and behavior analysis, a smart home for elderlies, and a medication reminder device, in which design, prototyping, usability evaluation and programming concepts were combined. In this report, we focus on the study design with the aim to provide scaffolding for multidisciplinary teams of students in design-based projects that require STEM competences.

Abstract:
Background
Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject’s exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.
Objective
Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.
Methods
We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.
Results
We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.
Conclusions
This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Igor Matias (April 2021)

| Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual’s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual’s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an “in the lab” experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.

Igor Matias (April 2021)

| Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual’s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual’s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an “in the lab” experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.

Igor Matias (April 2021)

| Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual’s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual’s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an “in the lab” experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.

Igor Matias (April 2021)

| Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual’s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual’s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an “in the lab” experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.

Abstract: Atrial Fibrillation (AF) is a type of arrhythmia characterized by irregular heartbeats, with four types, two of which are complicated to diagnose using standard techniques such as Electrocardiogram (ECG). However, and because smart wearables are increasingly a piece of commodity equipment, there are several ways of detecting and predicting AF episodes using only an ECG exam, allowing physicians easier diagnosis. By searching several databases, this study presents a review of the articles published in the last ten years, focusing on those who reported studies using Artificial Intelligence (AI) for prediction of AF. The results show that only twelve studies were selected for this systematic review, where three of them applied deep learning techniques (25%), six of them used machine learning methods (50%) and three others focused on applying general artificial intelligence models (25%). To conclude, this study revealed that the prediction of AF is yet an under-developed field in the context of AI, and deep learning techniques are increasing the accuracy, but these are not as frequently applied as it would be expected. Also, more than half of the selected studies were published since 2016, corroborating that this topic is very recent and has a high potential for additional research.