Creating “automatic subjects”: corporate wellness and self-tracking

The use of self-tracking (ST) devices has increased dramatically in recent years with enthusiasm from the public as well as public health, healthcare providers and workplaces seeking to instigate behaviour change in populations. Analysis of the ontological principles informing the design and implementation of the Apple Watch and corporate wellness (CW) programmes using ST technologies will suggest that their primary focus is on the capture and control of attention rather than material health outcomes. Health, wellness and happiness have been conflated with productivity which is now deemed to be dependent on the harnessing of libidinal energy as well as physical energy. In this context ST technologies and related CW interventions, have been informed by “emotional design”, neuroscientific and behavioural principles which target the “pre subjective” consciousness of individuals through manipulating their habits and neurological functioning. The paper draws on the work of Bernard Stiegler to suggest framing ST as “industrial temporal objects”, which capture and “short circuit” attention. It will be proposed that a central aim is to “accumulate the consciousnesses” of subjects consistent with the methods of a contemporary “attention economy”. This new logic of accumulation informs the behaviour change strategies of designers of ST devices, and CW initiatives, taking the form of “psychotechnologies” which attempt to reconstruct active subjects as automatic and reactive “nodes” as part of managed networks

The Wellbeing Index: A Landscape of Worldwide Measures and the Potential for Large-Scale Change

Around the world, across a spectrum of disciplines and by many different pathways, measures of wellbeing are emerging as a means for institutions and individuals to join forces in their efforts to balance material growth and development with the rights of humans to preserve, protect, and pursue those interests that lead to wellbeing, for both individuals and for society. Wellbeing indices are an important and innovative addition to the global conversation about the economics of happiness. Their rising viability with nations, communities, Nobel laureates, ordinary citizens, academics, economists, and policymakers, speaks to a growing questioning of the validity and adequacy of traditional measures of national progress – notably, the gross domestic product. Through the lens of positive psychology, this capstone provides an overview of the landscape of wellbeing indices, identifying in one place who is measuring what, by what indicators, and why. As scientific interest in the measurement of population wellbeing and national performance begins to deliver and document empirical results, this capstone makes a case for the wellbeing index as an instrument of massively disruptive and contagious change –a grand-scale positive intervention that has the potential to change the world

N-of-1 : better living through self-experimentation

This project's aim was to create a platform for personalized health data analysis, testing, and prediction, making it easier for ordinary people who are interested in N-of-1 trials to do their own self-experiments and take control of their mental and physical health. In these studies a single subject is observed and different interventions are systematically evaluated on them over time. These are typically longitudinal, occurring over weeks or months, with several rounds of treatments and evaluations in the form of a number of AB assignments. In these studies wearable technology, trackers, apps, sensors, and other IoT devices may be used to record information about the subject multiple times per day or week, if not constantly. In this study a singular self-experimenter collected data on themselves from several different sources such as mood questionnaires and a Fitbit wearable, among others. This data from the various sources was merged so that a variety of statistical methods could be performed. A few different modes of experimenting went into this study. One experiment tested the claim that spending 15 minutes per day writing in a gratitude journal had an effect on the subject's mood. This was achieved through a BABABA crossover phase design study, with each of the three phases being 28 days, for a total of 84 days in the experiment. The tests done for this experiment were the more traditional ANOVARM and ANCOVA, which were used to discover whether the intervention (B) phases were significantly different from the baseline (A), with relation to the subject's mood. Another test compared the claim that there was a difference in the subject's mood between the two groups of pre-experiment and during the experimental phase, through a Mann-Whitney U test. The last part of the study was a more complex machine learning (ML) pipeline that sought to predict the subject's mood based on over 3 years of daily collected data. The ML pipeline ingested the data, created several different ML models such as random forests and support vector machines, and compared which model was best at predicting the subject's mood. Feature importance was extracted from the best model through SHapley Additive exPlanations (SHAP), where the weight of the various feature effects on the target, in this case the subject's mood, was obtained. This notified the subject which behaviors had an effect on their mood. These different modes of experimenting were then compared, to see which was easier to implement or understand for future self-experimenters.Includes bibliographical references

Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare

Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Early intervention for obsessive compulsive disorder : An expert consensus statement

© 2019 Elsevier B.V.and ECNP. All rights reserved.Obsessive-compulsive disorder (OCD) is common, emerges early in life and tends to run a chronic, impairing course. Despite the availability of effective treatments, the duration of untreated illness (DUI) is high (up to around 10 years in adults) and is associated with considerable suffering for the individual and their families. This consensus statement represents the views of an international group of expert clinicians, including child and adult psychiatrists, psychologists and neuroscientists, working both in high and low and middle income countries, as well as those with the experience of living with OCD. The statement draws together evidence from epidemiological, clinical, health economic and brain imaging studies documenting the negative impact associated with treatment delay on clinical outcomes, and supporting the importance of early clinical intervention. It draws parallels between OCD and other disorders for which early intervention is recognized as beneficial, such as psychotic disorders and impulsive-compulsive disorders associated with problematic usage of the Internet, for which early intervention may prevent the development of later addictive disorders. It also generates new heuristics for exploring the brain-based mechanisms moderating the ‘toxic’ effect of an extended DUI in OCD. The statement concludes that there is a global unmet need for early intervention services for OC related disorders to reduce the unnecessary suffering and costly disability associated with under-treatment. New clinical staging models for OCD that may be used to facilitate primary, secondary and tertiary prevention within this context are proposed.Peer reviewe

Models of Care for musculoskeletal health: Moving towards meaningful implementation and evaluation across conditions and care settings

Models of Care (MoCs) are increasingly recognised as a system-level enabler to translate evidence for ‘what works’ into policy and, ultimately, clinical practice. MoCs provide a platform for a reform agenda in health systems by describing not only what care to deliver but also how to deliver it. Given the enormous burden of disease associated with musculoskeletal (MSK) conditions, system-level (macro) reform is needed to drive downstream improvements in MSK healthcare – at the health service (meso) level and at the clinical interface (micro) level. A key challenge in achieving improvements in MSK healthcare is sustainable implementation of reform initiatives, whether they be macro, meso or micro level in scope. In this chapter, we introduce the special issue of the Journal dedicated to implementation of MSK MoCs. We provide a contextual background on MoCs, a synthesis of implementation approaches across care settings covered across the chapters in this themed issued, and perspectives on the evaluation of MoCs

Employing Information and Communications Technologies in Homes and Cities for the Health and Well-Being of Older People

YesHe X and Sheriff RE (Eds.) Employing ICT in Homes and Cities for the Health and Well-Being of Older People. Workshop Proceedings of ICT4HOP’16. 15-17 Aug 2016. Sichuan University, Chengdu, China.British Council, Researcher Links, Newton Fund, NSF