Dear all,

I would like to bring the following to your attention.

The School of Sport and Exercise Sciences in the College of Life and
Environmental Sciences at University of Birmingham (UK) currently has 9 School
funded and 5 BBSRC funded PhD Scholarships open to applicants from the European
Union (EU; due to fee funding constraints), and a further 25 bursaries available
to applicants from outside of the EU.

The School is one of the leading research departments in the UK, as reflected by
the recent Research Assessment Exercise that rated 60% of the research activity
of world-leading standard. As part of the School's commitment to research
excellence, it is offering these awards for three years. The starting date is
aimed at October 2009, although programmes of study commencing at a later date
will be considered in exceptional circumstances. We require that applicants have
a good Honours degree (at least 2.1 or equivalent) and an English qualification
(e.g., GCSE, IELTS etc.).

Details of these funds are presented on our website:
http://www.sportex.bham.ac.uk/postgrad/prospective/studentships.shtml

General enquiries should be directed to Dr. Martin Edwards, Postgraduate
Admissions Tutor (M.Edwards.1@...) or to Val Queeley, Admissions
Secretary (tel: 0121 414 4115).

Many thanks for reading this posting.
Yours Sincerely
Martin Edwards

I want to correct any misconceptions that Sam Callum might have or might
engender in others about constant-power tests to exhaustion.  It is often
claimed that these tests are nothing like real events, but nothing could be
further from the truth.  If you want to do a personal-best endurance time,
you do it at close to constant pace, and you reach maximum effort as you
complete the distance.  That's exactly what happens in a time-to-exhaustion
test.  Now, I grant you that the distance you complete in a
time-to-exhaustion test is never going to be exactly the distance of a real
time trial, but that doesn't matter, provided it takes approximately the
time of the time trial.

Erica Hinckson and I marshaled this and other arguments in favor of
time-to-exhaustion tests in our MSSE paper and in the letters to the editor
that followed it (see below).   Of course we recognized the limitations, the
worst of which is finding the intensity that produces something like the
target time to exhaustion for each subject.  But that isn't so bad if you
are going to do a series of times to exhaustion of increasing duration, and
you will indeed want to do a series of tests, even if they are time trials
over the same distance. To be confident about trivial or small effects on
athletic performance, you absolutely have to do more than one test after
each treatment in a crossover or before and after each treatment in a
parallel-groups controlled trial, then average the outcomes.  Either that,
or test hundreds of subjects.  One way or another you have to do something
approaching 50-100  tests all up in a  crossover, or four times as many in a
parallel-groups trial, if your treatment effect is actually trivial and you
want to be sure about saying it is.  So, if you do a series of times to
exhaustion of increasing duration, you get the extra precision arising from
combining the tests for the estimate of performance in the middle of the
range of performance times, plus you get some indication of the effect of
the treatment over a range of durations (although the precision for the
shortest and longest durations won't be as good as for the mid duration).

Finally, please note that most sport scientists are happy with the notion
that an incremental to exhaustion is a contender for the single best test of
athletic performance, yet there is no event anything like an incremental
test.  The peak speed or power in the test correlates highly with time-trial
time, and it has one of the smallest errors of measurement. Most
importantly, the athlete doesn't have to guess the pace that she or he
thinks he can sustain optimally.  The same applies to time to exhaustion,
and that test is a lot closer to real events.

Hopkins WG, Hinckson EA (2005). Should time trial performance be predicted
from several serial time-to-exhaustion tests?--Response. Medicine and
Science in Sports and Exercise 37, 1821

Hopkins WG, Hinckson EA (2005). Mathematical constants that vary? --
Response. Medicine and Science in Sports and Exercise 37, 1823

At the risk of a reprimand from the publisher for violating the copyright
they winkled out of me, I have reproduced these two letters below.

Will


Should Time Trial Performance Be Predicted from Several Serial
Time-to-Exhaustion Tests?--Response

Our response is a plausible conversation between two sport scientists at a
future ACSM meeting.

Kim:  Hey, I hear you've switched to times to exhaustion with your endurance
athletes.  How come?  Jeukendrup and Currell reckon those tests are
unrealistic and unreliable (4).

Kylie:  Yeah, we used to do time trials, but we suspected the athletes were
just remembering their usual race pace, or even something more comfortable,
from trial to trial.  After all, they usually pick up the pace towards the
end of a time trial (e.g., 6), so they could have gone faster earlier.

Kim:  That change of pace in time trials is a worry.  A more even pace may
be optimal for longer endurance (1).  So athletes don't usually do their
best in time trials?

Kylie:  Exactly!  And it's probably worse when you do something that changes
their ability to produce power.  How well do athletes set their pace early
in a time trial to suit their new state?  Who knows?  With constant-power
and incremental tests to maximum effort, athletes don't have to set the
right pace-they just have to keep going as long as possible.

Kim: But isn't winning races all about pacing?

Kylie:  Pacing can be important, and we'll use time trials when we want to
study self-selected pacing. But usually an athlete tries to hang in with the
front runners.  Either way, the more power the athlete can sustain, the
better-and that's why just about everything we try with athletes is aimed at
increasing power output.  So when we test athletes, we want to measure their
change in power output accurately.  Allowing athletes to pace themselves
adds noise.  It also makes enhancements and impairments of maximum
sustainable power look smaller than they really are, but that's another
story.

Kim:  How many times to exhaustion?  Jeukendrup and Currell said there was
no point in doing several when one time trial will do.

Kylie:  Well, you need two to get around the problem of individual
differences in the power-duration curve, and we're checking that two work
properly with log-log modeling for longer tests, as Hinckson and Hopkins
suggested (2).  But we used to do two time trials anyway, because we wanted
to track endurance capability over short and long durations.  What's better
for that: two time trials or two times to exhaustion?

Kim:  Time trials, surely, because times to exhaustion are unreliable?

Kylie:  Look, Hopkins and colleagues (3) had already shown that times to
exhaustion are reliable over everything from minutes to hours.  Hinckson and
Hopkins put it on a solid theoretical and experimental footing with tests
lasting 1 to 10 minutes.  Jeukendrup and Currell's failure to recognize that
was disappointing.  They could have written something like this: "We admit
Jeukendrup and colleagues (5) were wrong in 1996. Hinckson and Hopkins have
explained clearly how it's all a matter of signal to noise arising from the
shape of the power-duration curve.  And their use of calculus with modeling
shows a commendable level of scholarship.  However."

Will G. Hopkins, PhD
Erica A. Hinckson, PhD
Division of Sport and Recreation
Auckland University of Technology
Auckland, NZ

1. Foster, C., M. Schrager, A. C. Snyder, and N. N. Thompson. Pacing
strategy and athletic performance. Sports Med. 17:77-85, 1994.
2. Hinckson, E. A. and W. G. Hopkins. Reliability of time to exhaustion
analyzed with critical-power and log-log modeling. Med. Sci. Sports Exerc.
37:696-701, 2005.
3. Hopkins, W. G., E. J. Schabort, and J. A. Hawley. Reliability of
power in physical performance tests. Sports Med. 31:211-234, 2001.
4. Jeukendrup, A. E. and K. Currell. Should time trial performance be
predicted from several serial time-to-exhaustion tests? Med. Sci. Sports
Exerc. 37:000-000, 2005.
5. Jeukendrup, A. E., W. H. Saris, F. Brouns, and A. D. Kester. A new
validated endurance performance test. Med. Sci. Sports Exerc. 28:266-270,
1996.
6. Schabort, E. J., W. G. Hopkins, and J. A. Hawley. Reproducibility of
self-paced treadmill performance of trained endurance runners. Int. J.
Sports Med. 19:48-51, 1998.


Mathematical Constants That Vary?-Response

"Constants that vary" (1) elegantly summarizes the fact that parameters in
any fixed-effects model are constants only in the sense of representing
values of population means, because values of the parameters for individuals
always vary from the mean.  We ourselves pointed out how the mean value for
a factor converting changes in time to exhaustion into changes in time-trial
time provides only an approximation for individuals, and we explained how to
use several times to exhaustion for more precise conversion (2).  The
approximation is not as bad as Atkinson and Nevill think. Consider a study
in which a single time to exhaustion is used pre and post a treatment, and
assume that the underlying mean change in power output is typical for
interventions aimed at athletic performance, say 2.0%.  The apparently large
between-subject variation of 28% in the conversion factor produces an error
of typically only 0.5% in an individual's 2.0% change.  This error is small
compared with the standard error of measurement of power output in time
trials, which is ~2-3% (4); it may also be negligible compared with the
variation arising from individual responses to the treatment.  This insight
helps explain why interventions producing small changes in power output have
been studied successfully with times to exhaustion.

Atkinson and Nevill requested analysis of reliability of time-trial times
predicted from times to exhaustion "using estimates of model parameters
specific to a particular subject at a specific time".  We reported such
predictions and analyses in Figure 1 and Table 3 of our paper.  The
outcome-low within-runner variability-was our empirical evidence that times
to exhaustion are inherently reliable.

Atkinson and Nevill's concerns about pacing are addressed partly in our
reply (3) to Jeukendrup and Currell (5).  Their suggestion that boredom
contributes to the large within-subject variability in time to exhaustion
echoes that in a paper coauthored by one of us in 1998 (6).  We now know
that the variability is due mainly to the shape of the power-duration curve,
but the extent to which boredom contributes for anyone making near-maximal
effort in any kind of long endurance test is unclear.  For this and other
reasons, the best endurance test for elite athletes is still not known.
Atkinson and Nevill's suggestion that measurement of physiological variables
at set times could give clues to subjects about elapsed time in
time-to-exhaustion tests is insightful, and measurement protocols need to be
devised to avoid this potential problem.

As with Jeukendrup and Currell, we are disappointed with Atkinson and
Nevill's failure to recognize our achievements.  The casual reader of these
two letters and of our paper-like the authors of the letters themselves-may
not appreciate that, amongst much else, we have derived factors that allow a
quick and reasonably accurate conversion of a percent change in a time to
exhaustion into a practical percent change in time-trial time.  These
factors, and those for longer tests (4), are particularly useful for
interpreting outcomes in published studies where time to exhaustion is the
outcome measure.

Will G. Hopkins, PhD
Erica A. Hinckson, PhD
Division of Sport and Recreation
Auckland University of Technology
Auckland, NZ

1. Atkinson, G. and A. Nevill. Mathematical constants that vary? Med.
Sci. Sports Exerc. 37:000-000, 2005.
2. Hinckson, E. A. and W. G. Hopkins. Reliability of time to exhaustion
analyzed with critical-power and log-log modeling. Med. Sci. Sports Exerc.
37:696-701, 2005.
3. Hopkins, W. G. and E. A. Hinckson. Should time trial performance be
predicted from several serial time-to-exhaustion tests?--Response. Med. Sci.
Sports Exerc. 37:000-000, 2005.
4. Hopkins, W. G., E. J. Schabort, and J. A. Hawley. Reliability of
power in physical performance tests. Sports Med. 31:211-234, 2001.
5. Jeukendrup, A. E. and K. Currell. Should time trial performance be
predicted from several serial time-to-exhaustion tests? Med. Sci. Sports
Exerc. 37:000-000, 2005.
6. Schabort, E. J., W. G. Hopkins, and J. A. Hawley. Reproducibility of
self-paced treadmill performance of trained endurance runners. Int. J.
Sports Med. 19:48-51, 1998.

And now for something almost completely different. One of my PhDs, Tom
Vandenbogaerde, and I are doing a meta-analysis on effects of carbohydrate
on performance.  In one of the studies (Murray et al., 1991) the effects are
for time-trial time on a Velodyne trainer.  We've tried unsuccessfully to
find a data file on the Web from any testing with this erg that would allow
us to translate effects on time-trial time into effects on mean power.  All
we need is a few observations on any one individual for speed and power.  We
can do the rest, which is simply to plot logs of both and get the slope
therefrom, which is the factor we need.  Can anyone help?  Or does anyone
know the factor or have a reference to it?

Will (and Tom)

Dear Colleagues

I have recently posted a job opening within the Exercise Biology lab here at
PBRC:

http://www.pbrc.edu/Careers.asp?jobid=868

Apply:

http://lsusystemcareers.lsu.edu

If you know anyone who might be interested or have avenues to pass it around
please do.

Thanks,

Conrad
___________________
Conrad Earnest, PhD
Director: Exercise Biology Laboratory
Pennington Biomedical Research Center
6400 Perkins Road Baton Rouge, LA 70808-4124
Tel. (225) 763-2923
Fax: (225) 763-0905
EM. Conrad.Earnest@...

Description:

The Pennington Biomedical Research Center, a research facility of Louisiana
State University System, seeks a qualified applicant for the position of
Research Associate for the Exercise Testing Core Laboratory. The primary
responsibilities for this position include the performance of ECG monitored
graded exercise tests, the writing of subsequent exercise prescriptions and
conducting dynamic isokinetic dynamometer testing for various research studies
conducted at Pennington. Other testing needs will be addressed on a
study-to-study basis.

Qualifications:

Applicants should have a Masters (preferred) or a bachelor's degree (minimum
qualification) in exercise physiology, nursing or a related health science
field. Experience in the following areas include: at least 1 or more years of
experience supervising exercise tests in healthy and at-risk populations (i.e.
cardiac rehab, etc.), CPR/AED certified, experience conducting dynamic
isokinetic dynamometer testing, and capable of reading EKG's. Preference will be
given to those individuals certified as an ACSM Exercise Specialist, Registered
Clinical Exercise Physiologist, or Licensed in Louisiana as an Exercise
Physiologist. Interested applicants should be motivated and able to work with
minimum supervision. Experimental design and planning, as well as accurate
recording of data are desired.

I just received this message and offered to help by forwarding it to this list.  It’s from a writer who wants the latest info on athlete genes and gene testing for an article in a fashion magazine, Marie Claire.  I’ve highlighted in red the key issues and questions in her message.   I haven’t kept up with this stuff lately, so if you know of any recent interesting developments I would love to hear about them.  Either send something to the list or to me and I will assemble into a summary for the list.  Alternatively if you know of someone who could help her, let her or me know. 

Will

(A devotee of America’s Next Top Model and Project Runway)

From: joanne chen [mailto:joannelchen@...]
Sent: Saturday, 18 April 2009 6:37 a.m.
To: will.hopkins@...
Subject: interview request/marie claire

Dear Dr. Hopkins:

I'm a writer in New York who is working on a story for Marie Claire about personalized fitness, and in it, I want to talk a little bit about the new companies that assess your genes for inherent athletic abilities, and tell you whether you'd be better at power sports or endurance sports. As you can imagine, these are popular with parents trying to find out how best to bring out their kid's talents. I was wondering whether this was something you could comment on--I was doing some research on the web and came across your 2001 paper in Sportscience on "Genes and Training for Athletic Performance."

Would you have a few minutes to respond to a few questions via email (below) or chat on the phone next week  (I think you are 14 hours ahead)? I'd like to find out: Are these tests helpful? Are they a waste of time and money?

How much do genes really affect athletic performance, and is it possible to change what

you're born with? And to what extent? What has changed since you've written that paper?

Thanks so much for considering this. I'm eager to hear your thoughts.

Best,

Joanne Chen

Contributing Editor

Marie Claire

212-987-8588

Hi,

I've recently switched from running, to cycling, and have been
adjusting to the different demands of the event.  Running is pretty
much a continous activity, and I can understand the nature of fatigue.
  However cycle races relies on continous bouts of sprinting along with
a base of aerobic work, and I am struggling to understand the nature
of the fatigue here.

What is it that slowly drains away the ability to sprint?

Is it caused by reduced energy supply - the body stops being able to
replenish sufficient ATP for the type II fibres to use?  Or is it
inhibited by the presence of some waste product - cyclists often swear
by using a lactic acid buffer such as sodium phosphate, but I don't
know if this specifically relates to improving the repeated sprint
ability.  Or is it something else.

My searching has turned up many interesting articles, but little that
has helped me in resolving my questions.  If anyone knows more, or
could point me in some good directions, it would be appreciated!

Cheers,

Jim.

Hello Jim,

If you are looking for reasing I would suggest reading the articles of the
CGM. Noakes describes the most complex/overall model of different forms of
fatigue. They describe several possibilities where fatigue can be sensed. I
could also do some mathematical modeling for fatigue. In my own mathematical
models I use a slightly differed definitions namely:

preserved intensity =The time an athlete can keep going on while the neural
input is at its maximum (this is not entirely correct translated but do not
know the right English words). This is not a form of fatigue (no power loss)
an can be set by a CGM.
Fatigue = unwilling los in maximum power output there are 3 forms off
fatigue:
1. Fat 1      = power loss due decrease in Ca release: due to???
Possibilities are
	 1; not all Ca2+ is reabsorbed by the SR but diffused in to the
blood,
	 2; a deficiency of neural transmitters,
	 3; a decline of sensitivity of the receptors in the central nerve
system (CNS),
	 4; a decline in the amount of motor neurons that are receiving
neural input
2. Fat 2      = power loss due decrease in glycogen
3. Fat 3      = power loss due metabolic state (muscle and blood are
combined even do the equation is based on a decrease in binding capacity of
actine/myosin (in literature pH, ADP, Pi AMP and some other variables are
reported to influence the pCa-force relation which does not accrue in the
blood compartment but in my metabolic models the activation of the
cardiovascular response is a slave to the metabolic state in the muscle)

Of course this is just one set of definitions and there are probably many
more ways to look at fatigue. But If you sent me powertap/SRM file ore other
description of the type of exercise I tell where the cyclist was performing
at its maximum performance (I need some points to make a fit on the most
clear fit is made to do a couple of sort all out paces) I can calculate for
you the power loss due the each off the 3 forms fatigue. Of course this is
just a global estimation because I don't have the whole metabolic profile of
the cyclist.

Martijn Carol

Techniek en Conditie Training
Bezoek adres:
Kleverlaan 204, Haarlem
Post adres:
Vondelweg 2
2025 AA Haarlem
tel: 06-44074291
fax: 023-5376280
info@...
www.stct.nl

Lecturer in Coach Education

              Apply Now

Organisation Advertising Description

School of Education

Full Time/Part Time

Full Time

Post is open to:

External and internal candidates

Grade

8

Salary

Starting salary in the range of £36,532 to £43,622 a year (potential progression on performance once in post to £49,096).

Additional Information

Terms and Conditions

Academic Teaching Staff (non-clinical)

Job Purpose

Role Purpose
A suitable candidate would assume responsibility for the management and lead delivery of a number of SPECS modules. These specifically include pedagogical and theoretical modules such as: (Applied Coaching Theory & Advanced Kinesiology). The ability to contribute to the delivery of physical education within the School's PGCE primary and secondary PE programmes would be an advantage. Additional contributions will be negotiated on the basis of each individual's background and expertise. The post will include supervisory work on placement and dissertation modules, as well as a contribution to postgraduate programmes (MPhil B in Coaching and PE). Lecturers appointed to the College of Social Sciences are required to maintain an active research profile.

Person Specification

Main Responsibilities
• To teach and examine courses at all levels, ie undergraduate, postgraduate and/or higher research degree students, through lectures, seminars and personal supervision.
• To plan and review own teaching approach
• To develop and apply innovative teaching approaches and materials to enable learning and enthuse students
• To develop programme proposals and contribute to the wider design of the School's teaching programme
• To review on a regular basis course content and materials, updating when required
• To undertake the full range of responsibilities in relation to supervision, marking and examining to ensure that students progress is being monitored and reported in line with the School's procedures
• To develop approaches to teaching and learning which are appropriate for the University and subject area and reflect developing practice elsewhere
• To disseminate innovative practices through appropriate media
• To engage in scholarly activity that will enhance the School's and University's reputation such as membership of academic bodies and external examining bodies.
• Contribution to the running of programmes, as well as modules delivered and/or assessed by them;
• To develop innovative research proposals and lead funding bids which develop and sustain research support in the specialist area
• To secure research funding for innovative projects

Planning and Organising
• Plan for and set teaching objectives over a number of years
• Plan and manage own teaching and tutorials as agreed with the Head of School.
• As with all other members of staff, the Lecturer will be expected to contribute to the wider life of the School and College of Social Sciences and undertake duties as required by the Head of School.

Potential applicants seeking further information about the post are invited to contact Dr Symeon Dagkas, Programme Coordinator Sport Physical Education and Community Studies at s.dagkas@... ; tel: 00441214158389

Knowledge, Skills, Qualifications and Experience Required
• PhD and preferably experience in teaching in higher education
• Good communication and presentational skills;
• The ability to work flexibly as part of a team and to lead initiatives;
• A willingness to contribute to and develop cross disciplinary research and teaching;
• A commitment to equal opportunities and democratic means of academic planning and working as a member of a team;

NOTE TO CANDIDATES

In the absence of being awarded a PhD to date or if working towards a PhD it will be possible to consider an application, but on the basis that the duties and terms of employment would be amended to reflect this. Further details to be made available.

Internal/External Relationships
• Communicating complex and conceptual ideas
• Participate in and develop external networks

Closing Date

22 May 2009

Grading

Grade 8

A full job description is available in PDF format

http://www.download.bham.ac.uk/vacancies/jd/32032.pdf

                           Apply Now

Dear all,
If you're interested in research design, measurement, and biostatistics, and are
planning to attend the forthcoming ACSM Annual Meeting in Seattle, please come
along to the Biostatistics Interest Group Meeting. The meeting is on Wed 27 May
from 5.45-7.15 PM in Room 201 at the Convention Center. We would be glad to see
you if you have any exciting ideas for session content for the 2010 meeting, or
if you just want to chat more generally.
We look forward to seeing you in Seattle.
Best wishes,
Alan.

Alan M Batterham PhD FACSM
Teesside University, UK.

See below for the contents of the developing 2009 issue of Sportscience.
Access these articles and more at http://sportsci.org/2009 .

NEWS & COMMENT-In Brief
Editorial: Sportscience Reformatted and Revisited
Progressive Statistics Updated

PERSPECTIVES
Fourier Series Approximations and Low Pass Filtering. Steve Elmer, Jim
Martin
The Improbable Central Governor of Maximal Endurance Performance. Will
Hopkins
The Second International Congress of Complex Systems in Sport. James Croft,
Chris Button, Matt Dicks

Will G Hopkins, PhD FACSM
Contact info: http://www.sportsci.org/will
Sportscience: http://sportsci.org
Statistics: http://newstats.org
Be creative: break rules.

The issues surrounding concurrent training are that endurance based
activity can limit hypertrophy and strength development within  athletes. If the
aim for the athlete isn't to increase lean mass but
increase relative strength through neural mechanisms then is
concurrent training such an issue?

I understand also that AMPK(activated through endurance based activity) can
inhibit mTOR and thus the protein synthesis pathway but again is the blocking of
this process such an issue with athletes where the aim is to maintain lean
tissue mass and improve strength/power of the individual? Could this blocking of
mTOR actually be used in training of athletes so that the loaded weights session
is immediately followed by a period of endurance based activity to limit the
protein synthesis due to occur in the body?

Thanks,
Barry Shillabeer
Barry.shillabeer@...

Dear Ted,

I can suggest you to take a look at two papers:

1-       Muscular function during ergometer cycling. Ericson (1988).
Scandinavian Journal of Rehabilitation Medicine 20 (1), pp. 35-41.

2-       Power output and work in different muscle groups during ergometer
cycling. Ericson et al. (1986). European Journal of Applied Physiology and
Occupational Physiology 55 (3), pp. 229-235.

In both papers the authors report mechanical workload with no external load.
I would also suggest the review paper:

1-       Biomechanical Determinants of Pedaling Energetics: Internal and
External Work Are Not Independent. Kautz & Neptune (2002). Exerc. Sport Sci.
Rev., Vol. 30, No. 4, pp. 159-165.

This last one focus on the mechanical energy transfer between segments and
how does it relate to internal work (i.e. work to move the legs).

I hope they all help you.

Regards,

Rodrigo Bini
________________________________________________
Rodrigo Rico Bini, PhD Student
Institute of Sport and Recreation Research New Zealand
School of Sport and Recreation
AUT University
90 Akoranga Drive, Northcote
North Shore City, Auckland 0637
New Zealand
Phone: +64 9 921 9999 ext 7295
  <http://www.ufsm.br/gepec> www.ufsm.br/gepec
  <http://sites.google.com/site/binirodrigo/>
http://sites.google.com/site/binirodrigo/

Hello there, from rainy South Florida.

Our department does have three visiting lines for Fall 2009, starting in
August (health promotion, need minimum masters; ABD or PHD for these
two, exercise physiology and strength/conditioning).  If you know of
anyone who might be interested, please forward this email to them.  I
have listed the positions with FAU numbers below.  They can go to
http://jobs.fau.edu to apply.

I will be at ACSM next week and can meet with anyone you suggest or just
have them contact me directly on my cell, 954-675-5782.

Regards, Sue

SGRAVES@...
B. Sue Graves, Ed.D., FACSM, HFS, FISSN
Chair, Department of Exercise Science
     and Health Promotion
Florida Atlantic University

Change of address and new telephone numbers are below:
777 Glades Road, FH-11
Boca Raton, Florida 33431
561-297-2938 (secretary)
561-297-2790 (direct)
561-297-2839 (fax)
www.coe.fau.edu/eshp

Sport neuroscience is likely to be a next big thing!

Understanding how the brain relates to sport / enhances performance remains
poorly understood. If you are interested in this area, and would be interested
in carrying out research for a PhD (or know someone that would), then please
consider the following opportunity.

We have a scholarship available that will fund PhD research into the
neuroscience of movement imagery. The scholarship is £12.5k tax free and will
be run at the University of Bangor, UK
(http://www.bangor.ac.uk/cohabs/funded_studentships.php.en).

Let me know if you would like further details.

Yours Sincerely
Martin Edwards

--
Martin Gareth Edwards (PhD)
School of Sport and Exercise Sciences
College of Life and Environmental Sciences
University of Birmingham
Birmingham
B15 2TT

(44) (0)121 4144109
MartinGEdwards@...
http://psy-naps.org

An article on the latest journal impact factors is now available at
http://sportsci.org .  It includes a description of a new citation
statistic, the H index, and an analysis of Tom Reilly's value.

Will G Hopkins, PhD FACSM
Contact info: http://www.sportsci.org/will
Sportscience: http://sportsci.org
Statistics: http://newstats.org
Be creative: break rules.

My report on the annual meeting of the European College of Sport Science is
now available at Sportscience http://sportsci.org .  If you attended this
conference and feel that I have got something wrong or left out something
important, please get back to me ASAP and I will update the report.

I have also made some minor updates/corrections to the H index section of
the article on impact factors.  Theodoras Bompouras sent me Hirsch's
original scholarly paper on the H index, in which he did actually account
for the effect of years of productivity, so I have added that info and
calculated Tom Reilly's H "rate" (H per productive year).  Also, the H index
is based on a publication count greater than or equal to the cites, not
greater than the cites. Tom Reilly's H index therefore goes up a notch.

Will
Will G Hopkins, PhD FACSM
Contact info: http://sportsci.org/will
Sportscience: http://sportsci.org
Statistics: http://newstats.org
Be creative: break rules.

Someone recently sent me a question about reliability.  She said that it would be OK to send her question and my reply to this list. (Arenda, I have added a bit more to the reply I sent you.) The question is about comparing the reliability of two measures.  See below for the original question, then my answer.  Please feel free to post a different point of view.  I should send such messages to the list more often… 

I am playing with the format of my email messages to try to eliminate the double-line spacing that somehow gets inserted sometimes.  If this message is hard to read on your mailer, please let me know.

Will

From: A.vanBeek@... [mailto:A.vanBeek@...]
Sent: Sunday, 12 July 2009 1:02 a.m.
To: will@...
Subject: realibility measurements

Dear Professor Hopkins,

…I'm a PhD student interested in the regulation of cerebral blood flow in patients with Alzheimer's disease. For my measurements, I've first performed a study on reproducibility of my measurement technique in old subjects.  Therefore, I've used the information on your website, your article in SportScience, and your excel sheet.

For my measurements, I've two methods of which I tested reproducibility in a small group, using the typical error expressed as coefficient of variation and the intraclass correlation.

I'd like to ask you the following question: how can I test if the difference between the coefficient of variations (and the intraclass correlations) of the two methods is significant?

I'm looking forward to your reaction.

Yours sincerely,

Arenda van Beek
PhD student
Department of Geriatric Medicine
Radboud University Nijmegen Medical Center

Arenda, hi.  How to compare reliability of two  measures of is a good basic question.  I have addressed this question in various places for the situation when the measures come from different subjects, but I may have left people without guidance for when they come from the same subjects.

First, let’s get validity vs reliability out of the way.  When you have two measures and one of them is a criterion, their appropriate comparison is a validity study, in which you regress the criterion (on the Y axis) against the other one (on the X).  A major problem here is what to do about random error in the criterion, and it’s a problem I have solved only recently.  I presented the solution at a conference last month, and I have the spreadsheets for dealing with it.  As yet they are unpublished, but I am happy to send them.

But let’s assume that both measures are measuring what you are interested in and that the only difference between the measures is the magnitude of the random error.  They will therefore both give the right answer on average across the range of values (that is,  neither has proportional bias), so a simple comparison of their errors of measurement and/or intraclass correlation coefficients derived from a reliability study is therefore appropriate.  Fine, but how do you do it? 

If you have taken the measurements of each measure with different subjects (one group of subjects for Measure A, another group for Measure B), then the estimates of error are independent, so you can use the section of my confidence-limits spreadsheet (Confidence limits & clinical chances) for comparing two standard deviations.  You can compare the intraclass correlation  coefficients with my spreadsheet Combine/compare effects. 

If you have taken the measurements on the same subjects in separate trials (e.g., two trials for Measure A then two for Measure B, or maybe A,B, A, B), then you can be reasonably but not overly confident that the change scores for A are uncorrelated with the changes scores for B, so it’s probably still OK to use those spreadsheets.  But if the measurements for both measures were taken in the same two trials, you have a problem, because change scores for A will be correlated with change scores for B, so you CAN’T use my spreadsheets.  There are two solutions: mixed modeling and bootstrapping. 

With mixed modeling you set up a simple repeated-measures model in which you allow for extra error variance with one of the measures, then get confidence limits for the extra variance.  This approach has three drawbacks: (1) you can derive confidence limits only for the extra variance and not for any other way of comparing the reliability (the other two are ratio of errors and differences in intraclass correlation coefficients), (2) you need access to a package that does mixed modeling, and (3) the confidence limits for the difference in the errors are only approximate when the sample is small.  I’m not sure what “small” here is, and it depends on the magnitude of the extra variance, but in my experience a sample size of 30 is usually adequate.

With bootstrapping you can use any statistic you like to compare the measures (difference in errors, ratio of errors, differences in correlation coefficients).  You derive the statistic from each of several thousand bootstrapped samples, which are samples drawn from your original sample and with the same number of subjects as in your original sample.  The confidence limits come directly from the percentiles of the bootstrapped samples (e.g., 90% confidence limits are the 5th and 95th percentiles). This method works no matter what’s going on in your data, and it has only two drawbacks: (1) you have to generate and process the bootstrapped samples somehow, and (2) as with the mixed-modeling approach, the confidence limits are approximate for small sample sizes.  “Small” here is anything less than ~30.

Finally, please don’t “test if the difference… is significant”.  Instead, decide how big or small the difference could be.

OK if I sent this to the Sportscience mailing list? Let me know if you want me to anonymize it.

Will

Will G Hopkins, PhD FACSM
Contact info: http://sportsci.org/will
Sportscience: http://sportsci.org
Statistics: http://newstats.org
Be creative: break rules