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Human Factors and Patient Self-Care
Published in Richard J. Holden, Rupa S. Valdez, The Patient Factor, 2021
Barrett S. Caldwell, Siobhan M. Heiden, Michelle Jahn Holbrook
The potential for substantial increases in the ability to detect and collect health information via self-care devices is significant, but it also comes at a significant risk. Mobile smartphone devices (such as the iPhone) and purpose-built health devices (such as Fitbit) have enabled significant advances in the ability of patients to collect and track their own medical and health data (see Chapter 12 in this volume). These devices drastically increase the availability of self-care data for both signal detection and situation awareness regarding patients’ conditions. The movement to leverage those data has been described as “the quantified self” (Gimpel et al., 2013; Sharon, 2017; Swan, 2009). However, concerns have been raised about the usability of these devices, as well as the gains in situation awareness achieved if collected health signal data are not presented in a way that patients can understand or use (Evans et al., 2016; Martinez et al., 2018; Sun et al., 2018).
The Future of Patient Engagement
Published in Jan Oldenburg, Dave Chase, Kate T. Christensen, Brad Tritle, Engage!, 2020
Thirty years ago, if you were to ask someone whether they took vitamins, they would say, yes, but today, you may even hear them respond with precise vitamin dosages (e.g., 1,000 mg of vitamin C, 600 mg of calcium). Even without genomic data (which are also desired), patients in the quantified self movement understand that their bodies are unique and may respond differently than their spouses to a certain number of hours of sleep. Those with allergies, in particular, are monitoring pollen counts and specific allergens. Even Ford Motor Company is taking this seriously, enabling a car’s Ford SYNC to control smartphone apps, and monitor and display allergens and pollen counts for the car’s occupants. This is especially important for those suffering from asthma, as 70% of those with asthma also suffer from allergies of some kind (“Honey, the ragweed is really bad here, I think it would be better for little Johnny’s asthma if we stopped for lunch in the next town.”).34, 35 For others, it may not be a health condition, but just a desire to function at their highest capacity during the day. One thing is certain: purveyors of advancements in personalized health, mentioned earlier in this chapter, will find those in the quantified self movement to be ready and willing customers.
When the going gets tough
Published in John Wattis, Stephen Curran, Elizabeth Cotton, Practical Management and Leadership for Doctors, 2019
John Wattis, Stephen Curran, Elizabeth Cotton
This is not to suggest that well-being initiatives should be avoided, but it does suggest that to make real changes in building well-being, a much broader working definition needs to be used that looks at the internal and external risks and protective factors at play. There are a lot of well-being and resilience measurements that are similarly contested. Some argue that these measurements are reductive, focusing on internal states of mind and leading to an unhealthy sense of ourselves, what has become known as the ‘quantified self’ [4] where we either pass or fail. Other measurements take a broader approach, looking at both internal and external factors that affect mental health and the balance between them, accepting that all of us work within imperfect systems. Some well-used measurements are the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) and the New Economic Foundation’s Five Ways to Wellbeing. Both measures emphasise the importance of being able to make emotional contact with the people around us and to form relationships based on trust. Exercise 9.1 gives you an opportunity to examine risk factors and protective factors in your own workplace.
The Quantified Scientist: Citizen Neuroscience and Neurotechnology
Published in AJOB Neuroscience, 2022
This study represents an extreme example of the Quantified Self (QS) movement. This movement consists of self-tracking enthusiasts who collect data on one or more aspects of their life, often with the goal of better understanding or improving parts of their lives. An overlapping community with an even stronger emphasis on the modulation and enhancement of brain function is the biohacker/neurohacker movement. Neurohackers adapt neurotechnologies from academic research and often aim to rebuild or further develop these for everyday use (Wexler 2017). Both quantified selfers and neurohackers can be considered citizen neuroscientists: they systematically apply neurotechnologies, monitor brain-related data, and quantitatively or qualitatively assess their effects on brain function. These individuals are often at the forefront of implementing new neurotechnologies and are therefore among the first to encounter the upsides and downsides of these new technologies.
Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages
Published in Chronobiology International, 2020
Shahab Haghayegh, Sepideh Khoshnevis, Michael H. Smolensky, Kenneth R. Diller, Richard J. Castriotta
Public interest in preserving health and optimizing cognitive and physical performance, exemplified by the Quantified-Self Movement (1130 days Quantified Self Institute 2015), has spurred the development of novel and inexpensive wristband monitoring technology. Wearable sleep trackers are of high interest to consumers, because poor quality and/or insufficient quantity of sleep is problematic for some 50–70 million adults in the USA (Colten and Altevogt 2006) and million others globally (Groeger et al. 2004; Knutson et al. 2010; Stranges et al. 2012), with consequent negative impact on productivity and health. Polysomnography (PSG), considered the gold standard for measuring sleep parameters and stages, entails simultaneous electroencephalography (EEG), electromyography, electrooculography, and other monitoring. However, PSG is expensive and necessitates bulky instrumentation and special facility. Wrist actigraphy is the most well-known method for assessing sleep in the free-living condition, even though it tends to overestimate sleep time (Van de Water et al. 2011). However, although relative to PSG, the overall sensitivity of wrist actigraphy in identifying sleep epochs is high, between 87% and 99%, its specificity is low, between 28% and 67% (Van de Water et al. 2011).
The Politics of Quantified Relationships
Published in The American Journal of Bioethics, 2018
Advocates of quantified-self technologies will dismiss such objections, often with a version of the “I have nothing to hide” argument that gets made in privacy debates. Now is not the time to recite all of the problems with that line of thought (for one accessible critique, see, e.g., Solove 2007). But one problem is that their opinions about whether they have anything to hide risk being imposed on others. Current privacy rules offer little protection, especially when there are high social costs to opting out; college students have little choice but to use Facebook now, if they want to have a social life at all (Hull 2015).