A Protocol for Digital Phenotyping: Can Methods From Computational Models of Decision-Making Be Used To Predict Those Most Likely To Be Non-Adherent to Fitness Goals? (Preprint)

2021 ◽  
Author(s):  
Marie Mc Carthy ◽  
Lili Zhang ◽  
Greta Monacelli ◽  
Tomas Ward
Keyword(s):  
Physical Activity ◽  
Decision Making ◽  
Sedentary Behavior ◽  
Predictive Model ◽  
San Francisco ◽  
Predictive Models ◽  
Computational Models ◽  
Iowa Gambling Task ◽  
Global Perspective ◽  
Combining Data

UNSTRUCTURED Can methods from computational models of decision-making be used to build a predictive model to identify individuals most likely to be non-adherent to personal physical goals? This predictive model may have significant value in the global battle against obesity. Despite the growing awareness of the considerable impact of physical inactivity on human health, sedentary behavior is increasingly linked to premature death in the developed world. The annual impact of sedentary behaviors is significant, causing an estimated 2 million deaths. From a global perspective, sedentary behavior is one of the ten leading causes of mortality and morbidity. Annually considerable funding and countless public health initiatives 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 can be used to identify those most likely to abandon their fitness goals. This information has the potential to be used to develop more targeted support to ensure those who embark on fitness programs are successful. This research aims to determine if it is possible to use decision-making tasks such as the Iowa Gambling Task (IGT) 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 application, will be used to ascertain if a predictive algorithm can identify digital personae's most likely to be non-adherent to self-determine exercise goals. If it is possible to phenotype these individuals, then it may be possible to tailor initiatives to support these individuals to stay the course. This study design is entirely virtual and based on a "Bring your own device" (BYOD) model. Two hundred healthy adults who are novice exercisers and own a FITBIT physical activity tracker (FITBIT, Inc. San Francisco, USA) will be recruited via social media for the study. Subjects will e-consent via the study app, which they will download from the Google/Apple play store. They will also consent to share their FITBIT data. Necessary demographic information concerning age and gender will be collected as part of the recruitment process. Over 12 months, scheduled study assessments will be pushed to the subjects to complete. The IGT will be administered via a web application shared via a URL. Ethics approval was received in December 2020 from Dublin City University. At manuscript submission, study recruitment is pending. Expected results will be published in 2022. This study is registered with Clinical Trials.Gov: Registration number NCT04783298

Computational basis of decision-making impairment in multiple sclerosis

2021 ◽  
pp. 135245852110593
Author(s):  
Rodrigo S Fernández ◽  
Lucia Crivelli ◽  
María E Pedreira ◽  
Ricardo F Allegri ◽  
Jorge Correale
Keyword(s):  
Multiple Sclerosis ◽  
Decision Making ◽  
Computational Models ◽  
Iowa Gambling Task ◽  
Memory Impairments ◽  
Hierarchical Bayesian Analysis ◽  
Control Subjects ◽  
Neurocognitive Impairments ◽  
Neurocognitive Evaluation ◽  
Computational Basis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Differences in Reward and Persistency characterise Behavior in Chronic Pain for the Iowa Gambling Task in a Web-based Lab-in-the-field study (Preprint)

2020 ◽  
Author(s):  
Lili Zhang ◽  
Himanshu Vashisht ◽  
Alekhya Nethra ◽  
Brian Slattery ◽  
Tomas Ward
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Chronic Pain ◽  
Iowa Gambling Task ◽  
Learning Curves ◽  
Self Report ◽  
Healthy Controls ◽  
Gambling Task ◽  
Convenience Sample ◽  
Pain Experiences

BACKGROUND Chronic pain is a significant world-wide health problem. It has been reported that people with chronic pain experience decision-making impairments, but these findings have been based on conventional lab 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 impacts decision-making captured via lab-in-the-field experiments. Although such settings can introduce more experimental uncertainty, it is believed that 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 chronic pain individuals and healthy controls in a lab-in-the-field environment through taking advantage of internet technologies and social media. METHODS A cross-sectional design with independent groups was employed. A convenience sample of 45 participants were recruited through social media - 20 participants who self-reported living with chronic pain, and 25 people with no pain or who were living with pain for less than 6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making, i.e. the Iowa Gambling Task (IGT) 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 two groups although qualitative differences could be observed in learning curves. However, computational modelling revealed that individuals with chronic pain were quicker to update their behavior relative to healthy controls, which reflected their increased learning rate (95% HDI from 0.66 to 0.99) when fitted with the VPP model. This result was further validated and extended on the ORL model because higher differences (95% HDI from 0.16 to 0.47) between the reward and punishment learning rates were observed when fitted on this model, indicating that chronic pain individuals were more sensitive to rewards. It was also found that they were less persistent in their choices during the IGT compared to controls, a fact reflected by their decreased outcome perseverance (95% HDI from -4.38 to -0.21) when fitted using the ORL 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 lab-in-the-field experiment. In this case study, it was demonstrated that compared to 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 lab.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

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