Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention

Shull, Peter B.; Jirattigalachote, Wisit; Hunt, Michael A.; Cutkosky, Mark R.; Delp, Scott L.

doi:10.1016/j.gaitpost.2014.03.189

Cited by 342 publications

(223 citation statements)

References 98 publications

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“…Recently, investigations have emerged which used sentiment analysis from twitter feeds to identify for example high impact events during games (Buntain 2014; Yu and Wang 2015) and to predict game outcomes (Godin et al 2014). In this regard, quantified-self initiatives may also provide future opportunities to generate valuable data for scientific investigations (Appelboom et al 2014; Shull et al 2014). In summary, lack of reliable data to perform tactical analysis in elite soccer is becoming less of a problem and novel data sources are continually being discovered and developed.…”

Section: Soccer Tactics Backgroundmentioning

confidence: 99%

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

2016

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Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon.

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Section: Soccer Tactics Backgroundmentioning

confidence: 99%

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

2016

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“…The Quantified Self (QS) is one example of a community of people who use wearable devices in order to log personal information and improve various aspects of personal life, such as mood, physical and mental performance, or other aspects of everyday life, such as air quality, and it has become a subject of critical thought (Bossewitch and Sinnriech 2013, Fotopoulou 2014, Shull et al 2014. Insurance companies and employers routinely introduce wearables in the workplace as part of the well-being and health package deals they offer to their employees (Olson and Tilley, 2014).…”

Section: Reviewing Wearables: Communication Systems and Social Technomentioning

confidence: 99%

Training to self-care: fitness tracking, biopedagogy and the healthy consumer

Fotopoulou

O’Riordan

2016

Health Sociology Review

131

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Corresponding author: Aristea Fotopoulou, a.fotopoulou@brighton.ac.uk 2 Training to self-care: Fitness tracking, biopedagogy and the healthy consumer AbstractIn this article, we provide an account of Fitbit, a wearable sensor device, using two complementary analytical approaches: auto-ethnography and media analysis. Drawing on the concept of biopedagogy, which describes the processes of learning and training bodies how to live, we focus on how users learn to self-care with wearable technologies through a series of micropractices that involve processes of mediation and the sharing their own data via social networking. Our discussion is oriented towards four areas of analysis: data subjectivity and sociality; making meaning; time and productivity; and brand identity. We articulate how these micropractices of knowing one's body regulate the contemporary 'fit' and healthy subject, and mediate expertise about health, behaviour and data subjectivity.

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“…Several recent studies have addressed the concept of selftracking in different contexts [25][26][27][28]. Li et al [29] refer to personal informatics-defined as an activity where people collect and reflect on personal data to gain a better understanding of their own behavior and self-knowledge.…”

Section: Self-trackingmentioning

confidence: 99%

Exploring factors impacting sharing health-tracking records

Иванов

Sharman

Rao

2015

Health Policy and Technology

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With the introduction of modern health technologies, the phenomenon of patients tracking health records has increased considerably in the recent past. The current study aims to examine the sharing of health-tracking records (HTR) by patients with different categories of acquaintances (spouse/partner, relatives, doctors, other). In this paper, we explore the critical factors that impact sharing of self-tracked information across two levels of sensitivity of HTR. Our study investigates how health motivation (HM), perceived health status (PHS), severity of health, and age influence sharing of self-tracked information. To do so, we adapted and applied the Health Belief Model (HBM) as a theoretical framework. The study employed multinomial logistic regression analysis to investigate the various factors that influence sharing of two types of HTR with different acquaintances. The statistical results were weighted to correct for known demographic discrepancies. Results indicated that HM, severity, and age substantially increase the likelihood of sharing HTR with health or medical professionals in comparison to other categories. In contrast, PHS increases the likelihood of information sharing with "other" acquaintances as opposed to with doctors. These findings are consistent with existing theory on HBM, wherein behavior appears to be predicted on the basis of/by both the individual's motivation to achieve an outcome and the expectation that a specific action will result in that outcome.

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Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention

Cited by 342 publications

References 98 publications

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science

Training to self-care: fitness tracking, biopedagogy and the healthy consumer

Exploring factors impacting sharing health-tracking records

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