Recommender Systems: A Healthy Obsession

Smyth, Barry

doi:10.1609/aaai.v33i01.33019790

Cited by 11 publications

(12 citation statements)

References 23 publications

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“…Although such data was not available in our dataset, the increasingly widespread adoption of mobile devices, smartwatches, and wearable sensors [55,56] has the capacity to generate large volumes of additional data (heart-rate, cadence, and power), which may be useful in this regard in the future [57,58]. Already, the availability of such diverse sources of data is enabling several new types of health and fitness applications [59][60][61][62][63] and the emergence of powerful new machine learning techniques has been used to support a variety of related prediction and planning tasks in several sporting domains [64][65][66][67][68][69][70][71][72][73] It is also worth noting that the model of the wall analysed here is defined by a pair of parameters-degree of slowdown and length of slowdown-with specific values-0.25 and 5km, respectively-and it is reasonable to question whether the results would be different if different values had been chosen. We have considered several alternative sets of values and, within reasonable levels of tolerance, there is no material change to the nature of the results as presented.…”

Section: Limitationsmentioning

confidence: 99%

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Smyth

2021

PLoS ONE

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Introduction In the marathon, how runners pace and fuel their race can have a major impact on race outcome. The phenomenon known as hitting the wall (HTW) refers to the iconic hazard of the marathon distance, in which runners experience a significant slowing of pace late in the race, typically after the 20-mile mark, and usually because of a depletion of the body’s energy stores. Aim This work investigates the occurrence of significant late-race slowing among recreational marathoners, as a proxy for runners hitting the wall, to better understand the likelihood and nature of such slowdowns, and their effect on race performance. Methods Using pacing data from more than 4 million race records, we develop a pacing-based definition of hitting the wall, by identifying runners who experience a sustained period of slowing during the latter stages of the marathon. We calculate the cost of these slowdowns relative to estimates of the recent personal-best times of runners and compare slowdowns according to runner sex, age, and ability. Results We find male runners more likely to slow significantly (hit the wall) than female runners; 28% of male runners hit the wall compared with 17% of female runners, χ2(1, N = 1, 928, 813) = 27, 693.35, p < 0.01, OR = 1.43. Such slowdowns are more frequent in the 3 years immediately before and after a recent personal-best (PB) time; for example, 36% of all runners hit the wall in the 3 years before a recent PB compared with just 23% in earlier years, χ2(1, N = 509, 444) = 8, 120.74, p < 0.01, OR = 1.31. When runners hit the wall, males slow more than females: a relative slowdown of 0.40 vs. 0.37 is noted, for male and female runners, when comparing their pace when they hit the wall to their earlier race (5km-20km) pace, with t(475, 199) = 60.19, p < 0.01, d = 0.15. And male runners slow over longer distances than female runners: 10.7km vs. 9.6km, respectively, t(475, 199) = 68.44, p < 0.01, d = 0.17. Although, notably the effect size of these differences is small. We also find the finish-time costs of hitting the wall (lost minutes) to increase with ability; r2(7) = 0.91, p < 0.01 r2(7) = 0.81, p < 0.01 for male and female runners, respectively. Conclusions While the findings from this study are consistent with qualitative results from earlier single-race or smaller-scale studies, the new insights into the risk and nature of slowdowns, based on the runner sex, age, and ability, have the potential to help runners and coaches to better understand and calibrate the risk/reward trade-offs that exist as they plan for future races.

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Section: Limitationsmentioning

confidence: 99%

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Smyth

2021

PLoS ONE

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“…This short paper builds on recent research [Berndsen et al 2019;Smyth 2019;Cunningham 2017b, 2018a] on the novel application of recommender systems to marathon running. We describe how to use the raw training data routinely collected by training apps such as Strava and RunKeeper to provide supports for runners during their training.…”

Section: Discussionmentioning

confidence: 99%

Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners

Feely¹,

Caulfield²,

Lawlor³

et al. 2020

Fourteenth ACM Conference on Recommender Systems

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Millions of people participate in marathon events every year, typically devoting at least 12-16 weeks to building their endurance and fitness so that they can safely complete these gruelling 42.2km races. Most runners follow a training plan that is tailored to their expected finish-time (e.g. sub-4 hours or 4-5 hours), and these plans will prescribe a complex mixture of training sessions to help them achieve these times. However, such plans cannot adapt to the individual needs (fitness levels, changing goals, personal preferences) of runners, providing only broad training guidance rather than more personalised support. The development of wearable sensors and mobile fitness applications facilitates the collection of a large amount of training data from runners. In this paper, we propose a recommender system that utilizes such training data to deliver more personalised training advice to runners, using ideas from casebased reasoning to reuse and adapt the training habits of similar runners. Explainability plays a significant role in this type of system, and we also describe how the predictions and recommendation advice can be presented to runners. An initial off-line evaluation is presented based on a large-scale, real-world dataset. CCS CONCEPTS • Computing methodologies → Machine learning algorithms; Feature selection; • Applied computing → Health informatics.

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“…AI has emerged with particular strength in the medical domain over the recent years, where there are prolific proofs of its advances, strengths and opportunities in prognosis, diagnosis, healthcare or preventive medicine [49][50][51][52][53]. Besides, AI can be applied in the public health landscape by leveraging social network and Web 2.0 media data, which in turn can be exploited to control drug abuse [54,55], toxic substance consumption [56], sexual and reproductive health [57], as well as healthy life habits [58][59][60]. These data, along with those provided by wearable devices, constitute another valuable information source for developing personalized recommender systems to promote healthy habits and support better individual decision-making in terms of healthy choices [60][61][62].…”

Section: Analysis For the Economic Dimension: Lifementioning

confidence: 99%

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

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The 17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

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Recommender Systems: A Healthy Obsession

Cited by 11 publications

References 23 publications

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

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