Dataset on comprehensive assessment & classification of upper & lower limb pain in athletes.

Data were charted as part of a scoping review which followed the Joanna Briggs Institute (JBI) evidence synthesis guidelines and the Preferred Reporting Items for Systematic Reviews and Meta Analysis Scoping Review extension (PRISMA-SCr) guidelines. Data was extracted from 470 articles that met the inclusion criteria for the scoping review; primary research articles of athletes where upper and/or lower limb pain since database inception. A draft data charting tool was developed by the research team and piloted for feasibility, accuracy and agreement. The charting tool was updated accordingly before being applied to the entire data set. Data collected included citation details, research context, participant information and pain assessment and classification tools, categories, and additional relevant information. The raw data set was filtered, and descriptive analysis of frequencies and counts were conducted. Researchers and clinicians interested in the range and applications of different pain assessment practices in athletes may reuse this data set. Data charting was comprehensive including aspects beyond the scope of the original research that offer clinical and research potential. These include information around recommended practice, (International Olympic Committee guidance) pain classifications and definitions and the use of multi-domain pain assessment tools.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

OBJECTIVE: Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 Event-related Potential (ERP), elicited in the Rapid Serial Visual Presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection. Approach. In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording. Main results. The results, presented as Area Under the Receiver Operating Characteristic Curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials. Significance. Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

A dataset of text prompts, videos and video quality metrics from generative text-to-video AI models.

Evaluating the quality of videos which have been automatically generated from text-to-video (T2V) models is important if the models are to produce plausible outputs that convince a viewer of their authenticity. This paper presents a dataset of 201 text prompts used to automatically generate 1,005 videos using 5 very recent T2V models namely Tune-a-Video, VideoFusion, Text-To-Video Synthesis, Text2Video-Zero and Aphantasia. The prompts are divided into short, medium and longer lengths. We also include the results of some commonly used metrics used to automatically evaluate the quality of those generated videos. These include each video's naturalness, the text similarity between the original prompt and an automatically generated text caption for the video, and the inception score which measures how realistic is each generated video. Each of the 1,005 generated videos was manually rated by 24 different annotators for alignment between the videos and their original prompts, as well as for the perception and overall quality of the video. The data also includes the Mean Opinion Scores (MOS) for alignment between the generated videos and the original prompts. The dataset of T2V prompts, videos and assessments can be reused by those building or refining text-to-video generation models to compare the accuracy, quality and naturalness of their new models against existing ones.

The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems.

The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed.

A Novel Approach of Customized Pelvic Implant Design Based on Symmetrical Analysis and 3D Printing.

In pelvic trauma patients, the mismatch of complex geometries between the pelvis and fixation implant is a fundamental cause of unstable and displaced pelvic ring disruption, in which secondary intervention is strongly considered. The geometrical matching in the current customized implant design and clinical practice is through the nonfractured hemi-pelvis for the fractured pelvis. This design philosophy overlooks the anatomical difference between the hemipelves, and further, the geometrical asymmetry at local area still remains unknown. This study analyzed the anatomical asymmetry of a patient's 3D pelvic models from 13 patients. The hemipelves of each patient were registered by using an iterative closet algorithm to an optimum position with minimum deviations. The high deviation regions were summarized between the hemipelves in each case, and a color map was drawn on a hemipelvis model that identified the areas that had a high possibility to be symmetrically different. A severe pelvic trauma case was used to comprehend the approach by designing a 3D printed implant. Each fracture was then registered to the mirrored uninjured hemipelvis by using the same algorithm, and customized fixation implants were designed with reference to the fractured model. The customized fixation plates showed that the implants had lower geometrical deviation when attached onto the re-stitched fracture side than onto the mirrored nonfractured bone. These results indicate that the symmetrical analysis of bone anatomy and the deviation color map can assist with implant selection and customized implant design given the geometrical difference between symmetrical bones. The novel approach provides a scientific reference that improves the accuracy and overall standard of 3D printed implants.

Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study).

INTRODUCTION: A substantial proportion of COVID-19 survivors continue to have symptoms more than 3 months after infection, especially of those who required medical intervention. Lasting symptoms are wide-ranging, and presentation varies between individuals and fluctuates within an individual. Improved understanding of undulation in symptoms and triggers may improve efficacy of healthcare providers and enable individuals to better self-manage their Long Covid. We present a protocol where we aim to develop and examine the feasibility and usability of digital home monitoring for capturing daily fluctuation of symptoms in individuals with Long Covid and provide data to facilitate a personalised approach to the classification and management of Long Covid symptoms. METHODS AND ANALYSIS: This study is a longitudinal prospective cohort study of adults with Long Covid accessing 10 National Health Service (NHS) rehabilitation services in the UK. We aim to recruit 400 people from participating NHS sites. At referral to study, 6 weeks and 12 weeks, participants will complete demographic data (referral to study) and clinical outcome measures, including ecological momentary assessment (EMA) using personal mobile devices. EMA items are adapted from the COVID-19 Yorkshire Rehabilitation Scale items and include self-reported activities, symptoms and psychological factors. Passive activity data will be collected through wrist-worn sensors. We will use latent class growth models to identify trajectories of experience, potential phenotypes defined by co-occurrence of symptoms and inter-relationships between stressors, symptoms and participation in daily activities. We anticipate that n=300 participants provide 80% power to detect a 20% improvement in fatigue over 12 weeks in one class of patients relative to another. ETHICS AND DISSEMINATION: The study was approved by the Yorkshire & The Humber-Bradford Leeds Research Ethics Committee (ref: 21/YH/0276). Findings will be disseminated in peer-reviewed publications and presented at conferences. TRIAL REGISTRATION NUMBER: ISRCTN15022307.

Motor learning in developmental coordination disorder: behavioral and neuroimaging study.

Developmental coordination disorder (DCD) is characterized by motor learning deficits that are poorly understood within whole-body activities context. Here we present results of one of the largest non-randomized interventional trials combining brain imaging and motion capture techniques to examine motor skill acquisition and its underpinning mechanisms in adolescents with and without DCD. A total of 86 adolescents with low fitness levels (including 48 with DCD) were trained on a novel stepping task for a duration of 7 weeks. Motor performance during the stepping task was assessed under single and dual-task conditions. Concurrent cortical activation in the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). Additionally, structural and functional magnetic resonance imaging (MRI) was conducted during a similar stepping task at the beginning of the trial. The results indicate that adolescents with DCD performed similarly to their peers with lower levels of fitness in the novel stepping task and demonstrated the ability to learn and improve motor performance. Both groups showed significant improvements in both tasks and under single- and dual-task conditions at post-intervention and follow-up compared to baseline. While both groups initially made more errors in the Stroop task under dual-task conditions, at follow-up, a significant difference between single- and dual-task conditions was observed only in the DCD group. Notably, differences in prefrontal activation patterns between the groups emerged at different time points and task conditions. Adolescents with DCD exhibited distinct prefrontal activation responses during the learning and performance of a motor task, particularly when complexity was increased by concurrent cognitive tasks. Furthermore, a relationship was observed between MRI brain structure and function measures and initial performance in the novel stepping task. Overall, these findings suggest that strategies that address task and environmental complexities, while simultaneously enhancing brain activity through a range of tasks, offer opportunities to increase the participation of adolescents with low fitness in physical activity and sports.

Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.

BACKGROUND: The number of people with cognitive deficits and diseases, such as stroke, dementia, or attention-deficit/hyperactivity disorder, is rising due to an aging, or in the case of attention-deficit/hyperactivity disorder, a growing population. Neurofeedback training using brain-computer interfaces is emerging as a means of easy-to-use and noninvasive cognitive training and rehabilitation. A novel application of neurofeedback training using a P300-based brain-computer interface has previously shown potential to improve attention in healthy adults. OBJECTIVE: This study aims to accelerate attention training using iterative learning control to optimize the task difficulty in an adaptive P300 speller task. Furthermore, we hope to replicate the results of a previous study using a P300 speller for attention training, as a benchmark comparison. In addition, the effectiveness of personalizing the task difficulty during training will be compared to a nonpersonalized task difficulty adaptation. METHODS: In this single-blind, parallel, 3-arm randomized controlled trial, 45 healthy adults will be recruited and randomly assigned to the experimental group or 1 of 2 control groups. This study involves a single training session, where participants receive neurofeedback training through a P300 speller task. During this training, the task's difficulty is progressively increased, which makes it more difficult for the participants to maintain their performance. This encourages the participants to improve their focus. Task difficulty is either adapted based on the participants' performance (in the experimental group and control group 1) or chosen randomly (in control group 2). Changes in brain patterns before and after training will be analyzed to study the effectiveness of the different approaches. Participants will complete a random dot motion task before and after the training so that any transfer effects of the training to other cognitive tasks can be evaluated. Questionnaires will be used to estimate the participants' fatigue and compare the perceived workload of the training between groups. RESULTS: This study has been approved by the Maynooth University Ethics Committee (BSRESC-2022-2474456) and is registered on ClinicalTrials.gov (NCT05576649). Participant recruitment and data collection began in October 2022, and we expect to publish the results in 2023. CONCLUSIONS: This study aims to accelerate attention training using iterative learning control in an adaptive P300 speller task, making it a more attractive training option for individuals with cognitive deficits due to its ease of use and speed. The successful replication of the results from the previous study, which used a P300 speller for attention training, would provide further evidence to support the effectiveness of this training tool. TRIAL REGISTRATION: ClinicalTrials.gov NCT05576649; https://clinicaltrials.gov/ct2/show/NCT05576649. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46135.

Semi-Supervised Mixture Learning for Graph Neural Networks With Neighbor Dependence.

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data. In this article, we propose a novel framework, called graph coneighbor neural network (GCoNN), for node classification. It is composed of two modules: GCoNN Γ and GCoNN Γ° . GCoNN Γ is trained to establish the fundamental prototype for attribute learning on labeled data, while GCoNN [Formula: see text] learns neighbor dependence on transductive data through pseudolabels generated by GCoNN Γ . Next, GCoNN Γ is retrained to improve integration of node attribute and neighbor structure through feedback from GCoNN [Formula: see text] . GCoNN tends to convergence iteratively using such an approach. From a theoretical perspective, we analyze this iteration process from a generalized expectation-maximization (GEM) framework perspective which optimizes an evidence lower bound (ELBO) by amortized variational inference. Empirical evidence demonstrates that the state-of-the-art performance of the proposed approach outperforms other methods. We also apply GCoNN to brain functional networks, the results of which reveal response features across the brain which are physiologically plausible with respect to known language and visual functions.

Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection that considers both the individual variability of the self-reported variable and of the adherence. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon signed rank test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially in studies where data collection is impacted by adherence.

Learning a novel rhythmic stepping task in children with probable developmental coordination disorder.

BACKGROUND: Developmental coordination disorder affects approximately 6% of children, interfering with participation in physical activity and can persist through adulthood. However, no studies have investigated the neuromotor mechanisms of learning of a novel task with rhythmic cueing. METHODS: Movement Assessment Battery for Children-2nd edition was used to identify 48 children with probable developmental coordination disorder (13.9 ± 0.05 yrs., 27% male) and 37 typically developed (13.9 ± 0.10 yrs., 54% male). While instrumented with an inertial measurement unit, both groups performed a novel rhythmic stepping task and with a concurrent auditory stroop test (dual-task), underwent seven weeks of intervention with step training with rhythmic cuing and were tested for retention five weeks post-intervention. FINDINGS: Initially, the group with probable developmental coordination disorder had a higher variability of step timing (coefficient of variation: 0.08 ± 0.003-typically developed - 0.09 ± 0.004-probable developmental coordination disorder, p < 0.05) and a frequency of peak power spectral density further from the target 0.5 Hz (0.50 ± 0.002 Hz-typically developed - 0.51 ± 0.003 Hz-probable developmental coordination disorder, p < 0.05), and were more affected by the dual-task: power spectral density at 0.5 Hz (-7.2 ± 3.3%-typically developed - -13.4 ± 4.6%- prob_DCD, p < 0.05) and stroop test errors (6.4 ± 1.1%-typically developed - -11.1 ± 2.4%- probable developmental coordination disorder, p < 0.05). The intervention led to similar improvements in both groups in coefficient of variation of step timing (0.12 ± 0.01-Pre - 0.07 ± 0.002-Post, p < 0.05), frequency of the peak power spectral density (0.51 ± 0.005 Hz-Pre - 0.50 ± 0.001 Hz-Post, p < 0.05) and relative power spectral density bandpower (3.2 ± 0.2%-Pre - 5.9 ± 0.3%-Post, p < 0.05). All improvements were retained after five weeks post-training. INTERPRETATION: Rhythmic cueing shows strong promise for enhancing motor learning in children with probable developmental coordination disorder. TRIAL REGISTRATION: Retrospectively registered on ClinicalTrials.gov with reference: NCT03150784.

Comprehensive assessment and classification of upper and lower limb pain in athletes: a scoping review.

BACKGROUND: Upper and lower limb (peripheral) pain is prevalent in athletes. Contemporary research prioritises multidimensional pain assessment and classification. This study aims to review comprehensive athlete pain assessment practices against the reference standard (International Olympic Committee, IOC Athlete Pain framework), identifying trends and highlighting gaps. METHODS AND ANALYSIS: Six databases were searched using a comprehensive search strategy. This review followed the Joanna Briggs Institute standardised methodology for scoping reviews and is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Title and abstract, full-text screening and data charting were completed by two independent reviewers. INCLUSION CRITERIA: Original research, systematic reviews and clinical practice guidelines reporting assessment or classification of pain in athletes of any age with chronic or acute peripheral pain in English on human participants from database inception. RESULTS: 470 studies with 175 different pain assessment tools were mapped against the IOC Athlete Pain Framework. Papers included tools from neurophysiological (470/100%), biomechanical (425/90%), affective (103/22%), cognitive (59/13%) and socioenvironmental (182/39%) domains. Pain classification was included in 108 studies (23%). 4 studies (0.85%) defined pain. Athletes with physical disability were included in 13 (3%) studies and no studies included athletes with intellectual disabilities. Socioeconomic factors were addressed in 29 (6%) studies. DISCUSSION: Neurophysiological and biomechanical domains are frequently addressed. Affective, socioenvironmental and cognitive tools are under-represented. Potential tools for use by researchers and clinicians are highlighted. Defining and classifying pain and determining predominant pain mechanisms is needed in both research and clinical practice. More work on underrepresented populations is needed. CONCLUSION: This review informs researchers and clinicians working with athletes in pain how pain assessment and classification is currently conducted and highlights future priorities.

The Effects of Tinnitus in Probabilistic Learning Tasks: Protocol for an Ecological Momentary Assessment Study.

BACKGROUND: Chronic tinnitus is an increasing worldwide health concern, causing a significant burden to the health care system each year. The COVID-19 pandemic has seen a further increase in reported cases. For people with tinnitus, symptoms are exacerbated because of social isolation and the elevated levels of anxiety and depression caused by quarantines and lockdowns. Although it has been reported that patients with tinnitus can experience changes in cognitive capabilities, changes in adaptive learning via decision-making tasks for people with tinnitus have not yet been investigated. OBJECTIVE: In this study, we aim to assess state- and trait-related impairments in adaptive learning ability on probabilistic learning tasks among people with tinnitus. Given that performance in such tasks can be quantified through computational modeling methods using a small set of neural-informed model parameters, such approaches are promising in terms of the assessment of tinnitus severity. We will first examine baseline differences in the characterization of decision-making under uncertainty between healthy individuals and people with tinnitus in terms of differences in the parameters of computational models in a cross-sectional experiment. We will also investigate whether these computational markers, which capture characteristics of decision-making, can be used to understand the cognitive impact of tinnitus symptom fluctuations through a longitudinal experimental design. METHODS: We have developed a mobile app, AthenaCX, to deliver e-consent and baseline tinnitus and psychological assessments as well as regular ecological momentary assessments (EMAs) of perceived tinnitus loudness and a web-based aversive version of a probabilistic decision-making task, which can be triggered based on the participants' responses to the EMA surveys. Computational models will be developed to fit participants' choice data in the task, and cognitive parameters will be estimated to characterize participants' current ability to adapt learning to the change of the simulated environment at each session when the task is triggered. Linear regression analysis will be conducted to evaluate the impacts of baseline tinnitus severity on adapting decision-making performance. Repeated measures linear regression analysis will be used to examine model-derived parameters of decision-making in measuring real-time perceived tinnitus loudness fluctuations. RESULTS: Ethics approval was received in December 2020 from Dublin City University (DCUREC/2021/070). The implementation of the experiments, including both the surveys and the web-based decision-making task, has been prepared. Recruitment flyers have been shared with audiologists, and a video instruction has been created to illustrate to the participants how to participate in the experiment. We expect to finish data collection over 12 months and complete data analysis 6 months after this. The results are expected to be published in December 2023. CONCLUSIONS: We believe that EMA with context-aware triggering can facilitate a deeper understanding of the effects of tinnitus symptom severity upon decision-making processes as measured outside of the laboratory. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/36583.

A comparison of distributed machine learning methods for the support of "many labs" collaborations in computational modeling of decision making.

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning (FL), which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning (CL-based) and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning (FL-based), incremental learning (IL-based), and cyclic incremental learning (CIL-based). We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible.

Neuroergonomic assessment of developmental coordination disorder.

Until recently, neural assessments of gross motor coordination could not reliably handle active tasks, particularly in realistic environments, and offered a narrow understanding of motor-cognition. By applying a comprehensive neuroergonomic approach using optical mobile neuroimaging, we probed the neural correlates of motor functioning in young people with Developmental Coordination Disorder (DCD), a motor-learning deficit affecting 5-6% of children with lifelong complications. Neural recordings using fNIRS were collected during active ambulatory behavioral task execution from 37 Typically Developed and 48 DCD Children who performed cognitive and physical tasks in both single and dual conditions. This is the first of its kind study targeting regions of prefrontal cortical dysfunction for identification of neuropathophysiology for DCD during realistic motor tasks and is one of the largest neuroimaging study (across all modalities) involving DCD. We demonstrated that DCD is a motor-cognitive disability, as gross motor /complex tasks revealed neuro-hemodynamic deficits and dysfunction within the right middle and superior frontal gyri of the prefrontal cortex through functional near infrared spectroscopy. Furthermore, by incorporating behavioral performance, decreased neural efficiency in these regions were revealed in children with DCD, specifically during motor tasks. Lastly, we provide a framework, evaluating disorder impact in ecologically valid contexts to identify when and for whom interventional approaches are most needed and open the door for precision therapies.

Differences in Learning and Persistency Characterizing Behavior in Chronic Pain for the Iowa Gambling Task: Web-Based Laboratory-in-the-Field Study.

BACKGROUND: Chronic pain is a significant worldwide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional laboratory experiments to date. In such experiments, researchers have extensive control of conditions and can more precisely eliminate potential confounds. In contrast, there is much less known regarding how chronic pain affects decision-making captured via laboratory-in-the-field experiments. Although such settings can introduce more experimental uncertainty, collecting data in more ecologically valid contexts can better characterize the real-world impact of chronic pain. OBJECTIVE: We aim to quantify decision-making differences between individuals with chronic pain and healthy controls in a laboratory-in-the-field environment by taking advantage of internet technologies and social media. METHODS: A cross-sectional design with independent groups was used. A convenience sample of 45 participants was recruited through social media: 20 (44%) participants who self-reported living with chronic pain, and 25 (56%) people with no pain or who were living with pain for <6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making (ie, the Iowa Gambling Task) in their web browser at a time and location of their choice without supervision. RESULTS: Standard behavioral analysis revealed no differences in learning strategies between the 2 groups, although qualitative differences could be observed in the learning curves. However, computational modeling revealed that individuals with chronic pain were quicker to update their behavior than healthy controls, which reflected their increased learning rate (95% highest-posterior-density interval [HDI] 0.66-0.99) when fitted to the Values-Plus-Perseverance model. This result was further validated and extended on the Outcome-Representation Learning model as higher differences (95% HDI 0.16-0.47) between the reward and punishment learning rates were observed when fitted to this model, indicating that individuals with chronic pain were more sensitive to rewards. It was also found that they were less persistent in their choices during the Iowa Gambling Task compared with controls, a fact reflected by their decreased outcome perseverance (95% HDI -4.38 to -0.21) when fitted using the Outcome-Representation Learning model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. CONCLUSIONS: We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our laboratory-in-the-field experiment. In this case study, it was demonstrated that, compared with standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand, and explain the differences in decision-making behavior in the context of chronic pain outside the laboratory.

Using Methods From Computational Decision-making to Predict Nonadherence to Fitness Goals: Protocol for an Observational Study.

BACKGROUND: Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be nonadherent to personal fitness goals? Such a model may have significant value in the global battle against obesity. Despite growing awareness of the impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behavior is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the 10 leading causes of mortality and morbidity. Annually, considerable funding and countless public health initiatives are applied to promote physical fitness, with little impact on sustained behavioral change. Predictive models developed from multimodal methodologies combining data from decision-making tasks with contextual insights and objective physical activity data could be used to identify those most likely to abandon their fitness goals. This has the potential to enable development of more targeted support to ensure that those who embark on fitness programs are successful. OBJECTIVE: The aim of this study is to determine whether it is possible to use decision-making tasks such as the Iowa Gambling Task to help determine those most likely to abandon their fitness goals. Predictive models built using methods from computational models of decision-making, combining objective data from a fitness tracker with personality traits and modeling from decision-making games delivered via a mobile app, will be used to ascertain whether a predictive algorithm can identify digital personae most likely to be nonadherent to self-determined exercise goals. If it is possible to phenotype these individuals, it may be possible to tailor initiatives to support these individuals to continue exercising. METHODS: This is a siteless study design based on a bring your own device model. A total of 200 healthy adults who are novice exercisers and own a Fitbit (Fitbit Inc) physical activity tracker will be recruited via social media for this study. Participants will provide consent via the study app, which they will download from the Google Play store (Alphabet Inc) or Apple App Store (Apple Inc). They will also provide consent to share their Fitbit data. Necessary demographic information concerning age and sex will be collected as part of the recruitment process. Over 12 months, the scheduled study assessments will be pushed to the subjects to complete. The Iowa Gambling Task will be administered via a web app shared via a URL. RESULTS: Ethics approval was received from Dublin City University in December 2020. At manuscript submission, study recruitment was pending. The expected results will be published in 2022. CONCLUSIONS: It is hoped that the study results will support the development of a predictive model and the study design will inform future research approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT04783298; https://clinicaltrials.gov/ct2/show/NCT04783298.

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach.

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.