Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving

Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioural data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world

Detecting Negative Emotions During Real-Life Driving via Dynamically Labelled Physiological Data

Driving is an activity that can induce significant levels of negative emotion, such as stress and anger. These negative emotions occur naturally in everyday life, but frequent episodes can be detrimental to cardiovascular health in the long term. The development of monitoring systems to detect negative emotions often rely on labels derived from subjective self-report. However, this approach is burdensome, intrusive, low fidelity (i.e. scales are administered infrequently) and places huge reliance on the veracity of subjective self-report. This paper explores an alternative approach that provides greater fidelity by using psychophysiological data (e.g. heart rate) to dynamically label data derived from the driving task (e.g. speed, road type). A number of different techniques for generating labels for machine learning were compared: 1) deriving labels from subjective self-report and 2) labelling data via psychophysiological activity (e.g. heart rate (HR), pulse transit time (PTT), etc.) to create dynamic labels of high vs. low anxiety for each participant. The classification accuracy associated with both labelling techniques was evaluated using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Results indicated that classification of driving data using subjective labelled data (1) achieved a maximum AUC of 73%, whilst the labels derived from psychophysiological data (2) achieved equivalent performance of 74%. Whilst classification performance was similar, labelling driving data via psychophysiology offers a number of advantages over self-reports, e.g. implicit, dynamic, objective, high fidelity

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

Repeated experiences of negative emotions, such as stress, anger or anxiety, can have long-term consequences for health. These episodes of negative emotion can be associated with inflammatory changes in the body, which are clinically relevant for the development of disease in the long-term. However, the development of effective coping strategies can mediate this causal chain. The proliferation of ubiquitous and unobtrusive sensor technology supports an increased awareness of those physiological states associated with negative emotion and supports the development of effective coping strategies. Smartphone and wearable devices utilise multiple on-board sensors that are capable of capturing daily behaviours in a permanent and comprehensive manner, which can be used as the basis for self-reflection and insight. However, there are a number of inherent challenges in this application, including unobtrusive monitoring, data processing, and analysis. This paper posits a mobile lifelogging platform that utilises wearable technology to monitor and classify levels of stress. A pilot study has been undertaken with six participants, who completed up to ten days of data collection. During this time, they wore a wearable device on the wrist during waking hours to collect instances of heart rate (HR) and Galvanic Skin Resistance (GSR). Preliminary data analysis was undertaken using three supervised machine learning algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Decision Tree (DT). An accuracy of 70% was achieved using the Decision Tree algorithm

The Influence of Game Demand on Distraction from Experimental Pain: A fNIRS Study

Video games are the most effective form of distraction from procedural pain compared to other distraction techniques, such as watching television or reading a book (Hussein, 2015). The degree of cognitive engagement with the game is a strong influence on the capacity of game-playing to distract from pain. By increasing game demand to a level that demands maximum levels of attention, it is possible to optimise distraction from pain; however, if the game becomes too difficult, it will fail to act as a distraction

Intraepithelial leukocytes of the intestinal mucosa in normal man and in Whipple's disease

Intraepithelial lymphocytes (IEL) of the intestinal mucosa of normal man and of patients with Whipple's disease were studied by light microscopy of 1-μm-thick sections, and by electron microscopy of thin sections. IEL in normal human intestine tend to be elongated in outline, have few cytoplasmic organelles, have compact nuclei, and are unattached to epithelial cells. IEL in Whipple's disease are more likely to be activated in appearance, ie, to be larger and to contain more cytoplasmic organelles than IEL of normal intestine. The number of IEL/100 intestinal epithelial cells is similar in normal man and in patients with Whipple's disease. Other intraepithelial (IE) cells found in normal intestine include eosinophils and mast cells, and we note for the first time the presence of IE macrophages. There are no “globule leukocytes” in the intestine of normal man or of patients with Whipple's disease. Other IE cells found in the intestine in Whipple's disease include eosinophils, polymorphonuclear (PMN) leukocytes, and macrophages in untreated disease and intraepithelial macrophages in treated disease. These IE cells may be involved in the acute and chronic immune responses of the intestine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44392/1/10620_2005_Article_BF01296750.pd

Development and formative evaluation of the e-Health implementation toolkit

<b>Background</b> The use of Information and Communication Technology (ICT) or e-Health is seen as essential for a modern, cost-effective health service. However, there are well documented problems with implementation of e-Health initiatives, despite the existence of a great deal of research into how best to implement e-Health (an example of the gap between research and practice). This paper reports on the development and formative evaluation of an e-Health Implementation Toolkit (e-HIT) which aims to summarise and synthesise new and existing research on implementation of e-Health initiatives, and present it to senior managers in a user-friendly format.<p></p> <b>Results</b> The content of the e-HIT was derived by combining data from a systematic review of reviews of barriers and facilitators to implementation of e-Health initiatives with qualitative data derived from interviews of "implementers", that is people who had been charged with implementing an e-Health initiative. These data were summarised, synthesised and combined with the constructs from the Normalisation Process Model. The software for the toolkit was developed by a commercial company (RocketScience). Formative evaluation was undertaken by obtaining user feedback. There are three components to the toolkit - a section on background and instructions for use aimed at novice users; the toolkit itself; and the report generated by completing the toolkit. It is available to download from http://www.ucl.ac.uk/pcph/research/ehealth/documents/e-HIT.xls<p></p> <b>Conclusions</b> The e-HIT shows potential as a tool for enhancing future e-Health implementations. Further work is needed to make it fully web-enabled, and to determine its predictive potential for future implementations

The effectiveness of interventions to change six health behaviours: a review of reviews

Background: Several World Health Organisation reports over recent years have highlighted the high incidence of chronic diseases such as diabetes, coronary heart disease and cancer. Contributory factors include unhealthy diets, alcohol and tobacco use and sedentary lifestyles. This paper reports the findings of a review of reviews of behavioural change interventions to reduce unhealthy behaviours or promote healthy behaviours. We included six different health-related behaviours in the review: healthy eating, physical exercise, smoking, alcohol misuse, sexual risk taking (in young people) and illicit drug use. We excluded reviews which focussed on pharmacological treatments or those which required intensive treatments (e. g. for drug or alcohol dependency). Methods: The Cochrane Library, Database of Abstracts of Reviews of Effectiveness (DARE) and several Ovid databases were searched for systematic reviews of interventions for the six behaviours (updated search 2008). Two reviewers applied the inclusion criteria, extracted data and assessed the quality of the reviews. The results were discussed in a narrative synthesis. Results: We included 103 reviews published between 1995 and 2008. The focus of interventions varied, but those targeting specific individuals were generally designed to change an existing behaviour (e. g. cigarette smoking, alcohol misuse), whilst those aimed at the general population or groups such as school children were designed to promote positive behaviours (e. g. healthy eating). Almost 50% (n = 48) of the reviews focussed on smoking (either prevention or cessation). Interventions that were most effective across a range of health behaviours included physician advice or individual counselling, and workplace- and school-based activities. Mass media campaigns and legislative interventions also showed small to moderate effects in changing health behaviours. Generally, the evidence related to short-term effects rather than sustained/longer-term impact and there was a relative lack of evidence on how best to address inequalities. Conclusions: Despite limitations of the review of reviews approach, it is encouraging that there are interventions that are effective in achieving behavioural change. Further emphasis in both primary studies and secondary analysis (e.g. systematic reviews) should be placed on assessing the differential effectiveness of interventions across different population subgroups to ensure that health inequalities are addressed.</p

Reliability of a tool for measuring theory of planned behaviour constructs for use in evaluating research use in policymaking

<p>Abstract</p> <p>Background</p> <p>Although measures of knowledge translation and exchange (KTE) effectiveness based on the theory of planned behavior (TPB) have been used among patients and providers, no measure has been developed for use among health system policymakers and stakeholders. A tool that measures the intention to use research evidence in policymaking could assist researchers in evaluating the effectiveness of KTE strategies that aim to support evidence-informed health system decision-making. Therefore, we developed a 15-item tool to measure four TPB constructs (intention, attitude, subjective norm and perceived control) and assessed its face validity through key informant interviews.</p> <p>Methods</p> <p>We carried out a reliability study to assess the tool's internal consistency and test-retest reliability. Our study sample consisted of 62 policymakers and stakeholders that participated in deliberative dialogues. We assessed internal consistency using Cronbach's alpha and generalizability (G) coefficients, and we assessed test-retest reliability by calculating Pearson correlation coefficients (<it>r</it>) and G coefficients for each construct and the tool overall.</p> <p>Results</p> <p>The internal consistency of items within each construct was good with alpha ranging from 0.68 to alpha = 0.89. G-coefficients were lower for a single administration (G = 0.34 to G = 0.73) than for the average of two administrations (G = 0.79 to G = 0.89). Test-retest reliability coefficients for the constructs ranged from <it>r </it>= 0.26 to <it>r </it>= 0.77 and from G = 0.31 to G = 0.62 for a single administration, and from G = 0.47 to G = 0.86 for the average of two administrations. Test-retest reliability of the tool using G theory was moderate (G = 0.5) when we generalized across a single observation, but became strong (G = 0.9) when we averaged across both administrations.</p> <p>Conclusion</p> <p>This study provides preliminary evidence for the reliability of a tool that can be used to measure TPB constructs in relation to research use in policymaking. Our findings suggest that the tool should be administered on more than one occasion when the intervention promotes an initial 'spike' in enthusiasm for using research evidence (as it seemed to do in this case with deliberative dialogues). The findings from this study will be used to modify the tool and inform further psychometric testing following different KTE interventions.</p

Research, evidence and policymaking: the perspectives of policy actors on improving uptake of evidence in health policy development and implementation in Uganda

<p>Abstract</p> <p>Background</p> <p>Use of evidence in health policymaking plays an important role, especially in resource-constrained settings where informed decisions on resource allocation are paramount. Several knowledge translation (KT) models have been developed, but few have been applied to health policymaking in low income countries. If KT models are expected to explain evidence uptake and implementation, or lack of it, they must be contextualized and take into account the specificity of low income countries for example, the strong influence of donors. The main objective of this research is to elaborate a Middle Range Theory (MRT) of KT in Uganda that can also serve as a reference for other low- and middle income countries.</p> <p>Methods</p> <p>This two-step study employed qualitative approaches to examine the principal barriers and facilitating factors to KT. Step 1 involved a literature review and identification of common themes. The results informed the development of the initial MRT, which details the facilitating factors and barriers to KT at the different stages of research and policy development. In Step 2, these were further refined through key informant interviews with policymakers and researchers in Uganda. Deductive content and thematic analysis was carried out to assess the degree of convergence with the elements of the initial MRT and to identify other emerging issues.</p> <p>Results</p> <p>Review of the literature revealed that the most common emerging facilitating factors could be grouped under institutional strengthening for KT, research characteristics, dissemination, partnerships and political context. The analysis of interviews, however, showed that policymakers and researchers ranked institutional strengthening for KT, research characteristics and partnerships as the most important. New factors emphasized by respondents were the use of mainstreamed structures within MoH to coordinate and disseminate research, the separation of roles between researchers and policymakers, and the role of the community and civil society in KT.</p> <p>Conclusions</p> <p>This study refined an initial MRT on KT in policymaking in the health sector in Uganda that was based on a literature review. It provides a framework that can be used in empirical research of the process of KT on specific policy issues.</p

Task-Selective Memory Effects for Successfully Implemented Encoding Strategies

Previous behavioral evidence suggests that instructed strategy use benefits associative memory formation in paired associate tasks. Two such effective encoding strategies–visual imagery and sentence generation–facilitate memory through the production of different types of mediators (e.g., mental images and sentences). Neuroimaging evidence suggests that regions of the brain support memory reflecting the mental operations engaged at the time of study. That work, however, has not taken into account self-reported encoding task success (i.e., whether participants successfully generated a mediator). It is unknown, therefore, whether task-selective memory effects specific to each strategy might be found when encoding strategies are successfully implemented. In this experiment, participants studied pairs of abstract nouns under either visual imagery or sentence generation encoding instructions. At the time of study, participants reported their success at generating a mediator. Outside of the scanner, participants further reported the quality of the generated mediator (e.g., images, sentences) for each word pair. We observed task-selective memory effects for visual imagery in the left middle occipital gyrus, the left precuneus, and the lingual gyrus. No such task-selective effects were observed for sentence generation. Intriguingly, activity at the time of study in the left precuneus was modulated by the self-reported quality (vividness) of the generated mental images with greater activity for trials given higher ratings of quality. These data suggest that regions of the brain support memory in accord with the encoding operations engaged at the time of study