Att springa fort utför och samtidigt titta på klockan för att se aktuell hastighet är klurigt.

I söndags sprang jag Icebug Backyard trail, ett 16km fantastiskt vackert trail-lopp i skogarna kring Härkeshult och Maderna med start vid Icebugs HQ i Jonsereds Fabriker. Ett verkligen välorganiserat och härligt lopp. Gänget på Icebug vet hur trail ska organiseras.

Loppet var i sin mittsektion och avslutande del en härlig löpning i utförsläge där det fanns mycket goda möjligheter att verkligen trycka på. I en av dessa underbara utförslöpor fick jag ett trängande behov av att se hur snabbt jag verkligen sprang. Dock visade det sig vara mycket svårt att både springa fort utan att snubbla rätt ut i skogen och att snabbtitta på min Garmin Fenix3 för att där se hastigheten. Det fanns naturligtvis en mängd faktorer som påverkade min förmåga att göra så, där min bristande koordination, dåliga syn, maxade puls samt mitt allmänna euforiska läge gjorde att en snabbtitt var klurig att få till.

Svårt att se på klockan när pulsen är på topp och hastigheten hög.

Kanske är det så att detta är något som löpare behöver träna på samt organisera på något sätt. Det finns ju rikligt med generösa inställningsmöjligheter på en Garmin. Kanske kan en mer anpassad design än det som jag hade valt vara en väg framåt. Kan det vara så att framtidens sportklockor behöver känna av vad jag gör och vill, utöver att den ska känna av vad för aktivitet som jag utför.

Det finns lite roliga studier som gjorts kring design av sportklockors grafiska och fysiska gränssnitt.

Stapeldiagram är effektivare vid en snabbtitt på klockan
Bland annat tycks diagramtypen spela roll för hur snabbt och korrekt som vi kan uppfatta värden.
Blascheck, T., Besançon, L., Bezerianos, A., Lee, B., & Isenberg, P. (2019). Glanceable Visualization- Studies of Data Comparison Performance on Smartwatches. IEEE transactions on visualization and computer graphics, 25(1), 630-640

Abstract—We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14–1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary. 

Att pilla med klockan medan man springer
I en annan studie har forskare undersökt hur löpningen påverkas när löparen interagerar med klockan. Seuter, M., Pfeiffer, M., Bauer, G., Zentgraf, K., & Kray, C. (2017). Running with Technology: Evaluating the Impact of Interacting with Wearable Devices on Running Movement. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies1(3), 101.

Abstract: The use of wearable devices during running has become commonplace. Although there is ongoing research on interaction techniques for use while running, the effects of the resulting interactions on the natural movement patterns have received little attention so far. While previous studies on pedestrians reported increased task load and reduced walking speed while interacting, running movement further restricts interaction and requires minimizing interferences, e.g. to avoid injuries and maximize comfort. In this paper, we aim to shed light on how interacting with wearable devices affects running movement. We present results from a motion-tracking study (N=12) evaluating changes in movement and task load when users interact with a smartphone, a smartwatch, or a pair of smartglasses while running. In our study, smartwatches required less effort than smartglasses when using swipe input, resulted in less interference with the running movement and were preferred overall. From our results, we infer a number of guidelines regarding interaction design targeting runners.

Behov av studier av användning när folk springer med sina klockor
Det behövs mer forskning kring användning av sportklockor / smart watches. Hur idrottare använder sina klockor i sin träningstillvaro och under högprestation i samband med lopp är hyperintressant men ganska utmanande att studera.

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Digital technologies for endurance athletics

In order to develop the skills and capacity in endurance athletics such as long-distance running, open water swimming and cycling, digital technologies have become an increasingly important aspect during practice and racing.

Heart-rate monitoring, watt-power meters, GPS-based distance measurement are among the core technologies that has opened up a wast field of opportunities in learning more about how to further develop as an athlete. Today, amateur athletes are able to track, analyse and adapt their training in similar ways as professional athletes. 

There are some great research studies and peer-reviewed papers that cover different aspects of this development. Some of the papers are descriptive and explain how technologies has become a vital part of the sport, other papers suggest new technical possibilities.

Düking, P., Hotho, A., Holmberg, H. C., Fuss, F. K., & Sperlich, B. (2016). Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Frontiers in physiology7, 71.

Fister Jr, I., Ljubič, K., Suganthan, P. N., Perc, M., & Fister, I. (2015). Computational intelligence in sports: challenges and opportunities within a new research domain. Applied Mathematics and Computation262, 178-186.

Hassan, M., Daiber, F., Wiehr, F., Kosmalla, F., & Krüger, A. (2017). Footstriker: An EMS-based foot strike assistant for running. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies1(1), 2.

Ianella, F., & Morandini, A. (2017). Digital innovation in the sport industry: the case of athletic performance.

Lee, V. R., & Drake, J. (2013). Digital physical activity data collection and use by endurance runners and distance cyclists. Technology, Knowledge and Learning18(1-2), 39-63.

Lee, V. R., & DuMont, M. (2010). An exploration into how physical activity data-recording devices could be used in computer-supported data investigations. International Journal of Computers for Mathematical Learning15(3), 167-189.

Malkinson, T. (2009, September). Current and emerging technologies in endurance athletic training and race monitoring. In Science and Technology for Humanity (TIC-STH), 2009 IEEE Toronto International Conference (pp. 581-586). IEEE.

Nylander, S., Tholander, J., Mueller, F., & Marshall, J. (2014). HCI and sports. CHI’14 Extended Abstracts on Human Factors in Computing Systems, 115-118.

Temir, E., O’Kane, A. A., Marshall, P., & Blandford, A. (2016, May). Running: A Flexible Situated Study. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 2906-2914). ACM.

Tholander, J., & Nylander, S. (2015, April). Snot, sweat, pain, mud, and snow: Performance and experience in the use of sports watches. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 2913-2922). ACM.

Wakefield, B., Neustaedter, C., & Hillman, S. (2014). The informatics needs of amateur endurance athletic coaches. CHI’14 Extended Abstracts on Human Factors in Computing Systems, 2287-2292.

Woźniak, P., Knaving, K., Björk, S., & Fjeld, M. (2015, August). RUFUS: remote supporter feedback for long-distance runners. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 115-124). ACM.