Personal metrics: Users’ experiences and perceptions of self-tracking practices and data

Ajana, Btihaj

doi:10.1177/0539018420959522

Cited by 13 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 89 Developers and marketers of CHTs attempt to create an ‘affective atmosphere’ of community in users. 90 Referring to a claim by Lupton, 91 Ajana 92 writes that data sharing in the context of CHT use ‘appeals to a deeply felt desire (…) to create social bonds and a sense of belonging’.…”

Section: Resultsmentioning

confidence: 99%

User types, psycho-social effects and societal trends related to the use of consumer health technologies

et al. 2023

View full text Add to dashboard Cite

Objective The term consumer health technologies we use in this paper refers to fitness and health apps, wearables and other self-tracking devices that collect health-related data. Our paper aims to bridge the gap between the growing literature base of sociological research and ethical reflection on the (non-intended) effects of consumer health technology use on the psycho-social level, such as stress, responsibilization or a loss of intuitive sense for signs of health or illness. Special consideration should be given to vulnerable individuals, as the positive and negative effects of consumer health technology use may be unequally distributed. This perspective may help to guide policymaking and the responsible development of consumer health technologies. Methods Using a narrative review approach, we refer to empirical and theoretical studies dealing with user types and effects related to the use of consumer health technologies. We provide an overview of consumer health technology user typologies and evidence of the unintended psycho-social effects of consumer health technology use. On this basis, we propose a user typology that may serve as a future tool for ethical reflection on negative side effects. Results Evidence of the potential negative side effects of consumer health technology use, as presented in the literature, is inconclusive due to the high diversity of consumer health technology users and the way they use consumer health technologies. Our proposed user typology aims to more comprehensively document the diversity of users by incorporating the way in which users identify with and use their self-tracked data, attitudes towards the new technology and social interactions via consumer health technologies, and the purpose and self-determinedness of consumer health technology use. Conclusions More systematic and quantitative empirical research on the effects of consumer health technology use in diverse settings and with diverse user types is necessary to inform public health policy. In addition to evidence-based certification of medical consumer health technologies, more practical and flexible ways to protect users from side effects may have to be developed and adopted, especially regarding the increasing number of non-medical consumer health technologies.

show abstract

Section: Resultsmentioning

confidence: 99%

User types, psycho-social effects and societal trends related to the use of consumer health technologies

et al. 2023

View full text Add to dashboard Cite

show abstract

“…These uncertainties undermine the policies' trustworthiness. Although people often regard self-tracked data as non-personal 'background noise' (Ajana 2020), they express concern about data movements and leakages. Our case study highlights a generalised confusion regarding what information is being collected and by whom.…”

Section: Discussionmentioning

confidence: 99%

Climate, Society and Elemental Insurance

Booth¹,

Lucas²,

French³

2022

View full text Add to dashboard Cite

In this book, world-leading social scientists come together to provide original insights on the capacities and limitations of insurance in a changing world.Climate change is fundamentally changing the ways we insure, and the ways we think about insurance. This book moves beyond traditional economics and financial understandings of insurance to address the social and geopolitical dimensions of this powerful and pervasive part of contemporary life. Insurance shapes material and social realities, and is shaped by them in turn. The contributing authors of this book show how insurance constitutes and is constituted through the traditional elements of earth, water, air, fire, and the novel element of big data. The applied and theoretical insights presented through this novel elemental approach reveal that insurance is more dynamic, multifaceted, and spatially variegated than commonly imagined.This book is an authoritative source on the capacities and limitations of insurance. It is a go-to reference for researchers and students in the social sciences -particularly those with an interest in economics and finance, and how these intersect with geography, politics, and society. It is also relevant for those in the disaster, environmental, health, natural, and social sciences who are interested in the role of insurance in addressing risk, resilience, and adaptation.Kate Booth is a human geographer, specializing in the field of critical insurance studies. She is particularly interested in the economic and social geographies of insurance in a changing climate, and implications for inequality and inequity. Kate has also worked on projects looking at sense of place, and the role of arts and culture in urban regeneration. Her work is published in journals such as Progress in Human Geography, Environment and Planning A: Economy and Space, Environment and Planning E: Nature and Space, Urban Studies, and Qualitative Inquiry.

show abstract

“…Even though self-tracking positively affects customers' impressions of personalization [48], its data-driven and quantifiable features might occasionally result in excessive self-monitoring. This could put more pressure on the user to perform following strict health standards, which could cause feelings of inadequacy and make it difficult for them to enjoy exercising [49]. Meanwhile, several studies have noted an increase in self-efficacy in relation to selfquantification [50,51].…”

Section: Quantified Selfmentioning

confidence: 99%

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

Morandé¹,

Tewari²,

Gul³

2023

Healthcare Access - New Threats, New Approaches

View full text Add to dashboard Cite

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.

show abstract

Personal metrics: Users’ experiences and perceptions of self-tracking practices and data

Cited by 13 publications

References 42 publications

User types, psycho-social effects and societal trends related to the use of consumer health technologies

User types, psycho-social effects and societal trends related to the use of consumer health technologies

Climate, Society and Elemental Insurance

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

Contact Info

Product

Resources

About