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Duncan Simpson, Phillip G. Post, Greg Young, and Peter R. Jensen

Ultramarathon (UM) running is a rapidly growing sport throughout the world, yet to date it has received little attention in sport psychology literature. To obtain further insight into this sport, the current study examined the training and competition experiences of UM runners. Phenomenological interviews were conducted with 26 participants ranging in age from 32 to 67 years (M = 44.1 yrs, SD = 8.1). Qualitative analysis of the interview data identified meaning units, which were grouped into major themes. A final thematic structure revealed five major themes that characterized the participant’s experience of UM running: preparation and strategy, management, discovery, personal achievement, and community. Taken together, the present results extend previous research on UM running and provide a number of suggestions for sport psychology consultants working with UM runners.

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Greg Henry, Brian Dawson, Brendan Lay, and Warren Young

Purpose:

To study the validity of a video-based reactive agility test in Australian footballers.

Methods:

15 higher performance, 15 lower performance, and 12 nonfootballers completed a light-based reactive agility test (LRAT), a video-based reactive agility test (VRAT), and a planned test (PLAN).

Results:

With skill groups pooled, agility time in PLAN (1346 ± 66 ms) was significantly faster (P = .001) than both reactive tests (VRAT = 1550 ± 102 ms; LRAT = 1572 ± 97 ms). In addition, decision time was significantly faster (P = .001; d = 0.8) in LRAT (278 ± 36 ms) than VRAT (311 ± 47 ms). The correlation in agility time between the two reactive tests (r = .75) was higher than between the planned and reactive tests (r = .41–.68). Higher performance players had faster agility and movement times on VRAT (agility, 130 ± 24 ms, d = 1.27, P = .004; movement, 69 ± 73 ms, d = 0.88, P = .1) and LRAT (agility, 95 ± 86 ms, d = 0.99, P = .08; movement, 79 ± 74 ms; d = 0.9; P = .08) than the nonfootballers. In addition, higher (55 ± 39 ms, d = 0.87, P = .05) and lower (40 ± 57 ms, d = 0.74, P = .18) performance groups exhibited somewhat faster agility time than nonfootballers on PLAN. Furthermore, higher performance players were somewhat faster than lower performance for agility time on the VRAT (63 ± 85 ms, d = 0.82, P = .16) and decision time on the LRAT (20 ± 39 ms, d = 0.66, P = .21), but there was little difference in PLAN agility time between these groups (15 ± 150 ms, d = 0.24, P = .8).

Conclusions:

Differences in decision-making speed indicate that the sport-specific nature of the VRAT is not duplicated by a light-based stimulus. In addition, the VRAT is somewhat better able to discriminate different groups of Australian footballers than the LRAT. Collectively, this indicates that a video-based test is a more valid assessment tool for examining agility in Australian footballers.

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Ben Desbrow, Katelyn Barnes, Caroline Young, Greg R. Cox, and Chris Irwin

Immediate postexercise access to fruit/fluid via a recovery “station” is a common feature of mass participation sporting events. Yet little evidence exists examining their impact on subsequent dietary intake. The aim of this study was to determine if access to fruit/water/sports drinks within a recovery station significantly alters dietary and fluid intakes in the immediate postexercise period and influences hydration status the next morning. 127 (79 males) healthy participants (M ± SD, age = 22.5 ± 3.5y, body mass (BM) = 73 ± 13kg) completed two self-paced morning 10km runs separated by 1 week. Immediately following the first run, participants were randomly assigned to enter (or not) the recovery station for 30min. All participants completed the alternate recovery option the following week. Participants recorded BM before and after exercise and measured Urine Specific Gravity (USG) before running and again the following morning. For both trial days, participants also completed 24h food and fluid records via a food diary that included photographs. Paired-sample t tests were used to assess differences in hydration and dietary outcome variables (Recovery vs. No Recovery). No difference in preexercise USG or BM change from exercise were observed between treatments (p’s > .05). Attending the recovery zone resulted in a greater total daily fluid (Recovery = 3.37 ± 1.46L, No Recovery = 3.16 ± 1.32L, p = .009) and fruit intake (Recovery = 2.37 ± 1.76 servings, No Recovery = 1.55 ± 1.61 servings, p > .001), but had no influence on daily total energy (Recovery = 10.15 ± 4.2MJ, No Recovery = 10.15 ± 3.9MJ), or macronutrient intakes (p > .05). Next morning USG values were not different between treatments (Recovery = 1.018 ± 0.007, No Recovery = 1.019 ± 0.009, p > .05). Recovery stations provide an opportunity to modify dietary intake which promote positive lifestyle behaviors in recreational athletes.