Exercise Training for Type 2 Diabetes Mellitus: Impact on
Cardiovascular Risk: A Scientific Statement From the American Heart
Association -- Marwick et al. 119 (25): 3244 -- Circulation

http://circ.ahajournals.org/cgi/content/full/119/25/3244

Abdominal obesity slide show that is downloadable

Abdominal obesity slide show

The Controversies in Obesity, Diabetes and Hypertension (CODHy) Meeting

http://care.diabetesjournals.org/content/vol31/Supplement_2/

My First Stata Program

My first Stata Program

capture program drop tabmm program tabmm version 12 syntax varlist [if][in][,col] local varnum : word count `varlist' local varnumminus1 = `varnum' -1 forvalues i=1/`varnumminus1' { local x : word `i' of `varlist' local j=`i'+1 forvalues k=`j'/`varnum' { local y: word `k' of `varlist' svy: tabulate `x' `y' } } end capture program drop tabm program tabm version 12 syntax varlist[,cell count column row se ci cv percent proportion] local varnum : word count `varlist' local x : word 1 of `varlist' forvalues i=2/`varnum' { local y: word `i' of `varlist' svy:tabulate `x' `y',`col' `cell' `se' `percent' format(%5.1f) } end tabm sex race5grp diabetes,cell se percent

The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors

http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.
1000058

Background
Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and
comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight-obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.

Methods and Findings
We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000-500,000) and 395,000 (372,000-414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight-obesity (216,000; 188,000-237,000) and physical inactivity (191,000; 164,000-222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000-107,000), low dietary omega-3 fatty acids (84,000; 72,000-96,000), and high dietary trans fatty acids (82,000; 63,000-97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000-40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000-94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis,
alcohol use disorders, road traffic and other injuries, and violence.

Conclusions
Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.

Brown Fat Identified as Heat-Yielding Cells in Humans - NYTimes.com

Brown Fat Identified as Heat-Yielding Cells in Humans
http://www.nytimes.com/2009/04/09/health/research/09fat.html?_r=2&partne
r=rss&emc=rss

Not about visceral fat or subcutaneous fat, but more information about
brown fat.

For more than 30 years, scientists have been intrigued by brown fat, a
cell that acts like a furnace, consuming calories and generating heat.
Rodents, unable to shiver effectively to keep warm, use brown fat
instead. So do human infants, who do not shiver very well. But it was
generally believed that humans lose brown fat after infancy, no longer
needing it once the shivering response kicks in.

That belief, three groups of researchers report, is wrong.

Medical Calculator/Unit Converter

Medical Calculator/Unit Converter
Clinical Analyte Unit Conversions
(http://dwjay.tripod.com/conversion.html).
Medical Algorithmas (http://www.medal.org/visitor/login.aspx): More than
12,500 Scales, Tools, Assessments, Scoring Systems, and other Algorithms
intended for Medical Education and for Biomedical Research.
Conversion & Calculation Center
(http://www.convertit.com/Go/ConvertIt/).
Converber (http://www.xyntec.com/): Converber is a unit converter. It is
a powerful software utility that will help make easy conversions between
1241 various units of measure in 33 categories. Converber converts
everything from length and force to flow and temperature. See some of
the features listed below.

The Diabetes Prevention Program: How the Participants Did It

The Diabetes Prevention Program: How the Participants Did It
http://www.medscape.com/viewarticle/587049?src=top10

Blood glucose self-monitoring in type 2 diabetes: a randomised controlled trial

Blood glucose self-monitoring in type 2 diabetes: a randomised
controlled trial

This link gets you the EXEC SUMMARY -- better than reading all 72 pages!

http://www.hta.ac.uk/execsumm/summ1315.htm

Hedgehog reappears, loses to fox

Hedgehog reappears, loses to fox
International Journal of Epidemiology 2007; 36:3-10
========================================

In a famous essay, Isaiah Berlin used a fragment from an ancient Greek poem to characterize '[O]ne of the deepest differences which divide writers and thinkers, and, it may be, human beings in general.' That fragment is: 'The fox knows many things, but the hedgehog knows one big thing.' He continued, [T]here exists a great chasm between those, on one side, who relate everything to a single central vision, one system less or more coherent or articulate, in terms of which they understand, think and feel ... and, on the other side, those who pursue many ends, often unrelated and even contradictory, connected, if at all, only in some de facto way ... The first kind of intellectual and artistic personality belongs to the hedgehogs, the second to the foxes.40 Hedgehogs are likely to think of prediction as a deductive exercise, whether based upon functionalism, free market economics or Marxism, whereas foxes are likely to make predictions based upon careful observations of particular cases. And studies of political forecasting indicate that foxes are better forecasters than hedgehogs, precisely because foxes are not committed to an overarching theory but are able to learn from their mistakes and remain open to new information. In a study of the forecasting accuracy of political experts, Philip Tetlock41 found that those who were least accurate looked very much like hedgehogs: '[T]hinkers who "know one big thing", aggressively extend the explanatory reach of that one big thing into new domains, display bristly impatience with those who "do not get it", and express considerable confidence that they are already pretty proficient forecasters, at least in the long term.'42 They are people who are likely to 'trivialize evidence that undercuts their preconceptions and to embrace evidence that reinforces their preconceptions.'43 Those who were more accurate 'look like foxes': [T]hinkers who know many small things (tricks of their trade), are skeptical of grand schemes, see explanation and prediction not as deductive exercises but rather as exercises in flexible 'ad hocery' that require stitching together diverse sources of information, and are rather diffident about their own forecasting  prowess, and ... rather dubious that the cloudlike subject of politics can by the object of a clocklike science.44 Foxes have a 'more balanced style of thinking about the world-a style of thought that elevates no thought above criticism.'45 Social epidemiology is more nearly akin to political forecasting than to physics. When considering the ssociations between sex, race and social roles on the one hand and health and disease on the other, accurate prediction is unlikely to rest upon deductive science and more likely to result from stitching together all that one can know about the context-institutional, cultural, political, epidemiological-in which particular populations live and work. Thus, social epidemiology is scientific as it reconstructs the past and explains the present, but it is not likely to be powerfully predictive. When it is successfully predictive, it is not likely to be because it is based upon deductions from scientifically valid generalizations that are true across time and place, but because analysts understand more or less intimately the people and places with which they are concerned, and because they can extrapolate sensibly from relevant experiences and groups elsewhere. 

QOL and exercise

The effects of exercise interventions on quality of life in clinical and healthy populations; a meta-analysis

Fiona Bridget Gillison, Suzanne M. Skevington, Ayana Sato, Martyn Standage and Stella Evangelidou
^aUniversity of Bath, Claverton Down, Bath BA2 7AY, United Kingdom

Available online 18 March 2009.
Abstract
The aim of the study was to provide an overview of the effect of exercise interventions on subjective quality of life (QoL) across adult clinical populations and well people, and to systematically investigate the impact of the exercise setting, intensity and type on these outcomes. From a systematic search of six electronic databases, 56 original studies were extracted, reporting on 7937 sick and well people. A meta-analysis was conducted on change in QoL from pre- to post-intervention compared with outcomes from a no-exercise control group, using weighted (by the study's sample size) pooled mean effect sizes and a fixed-effects model. Significant differences in outcome were found when treatment purpose was compared; prevention/promotion (well populations), rehabilitation, or disease management. Three to 6 months post-baseline, a moderate positive effect of exercise interventions was found for overall QoL in rehabilitation patients, but no significant effect for well or disease management groups. However, physical and psychological QoL domains improved significantly relative to controls in well participants. Psychological QoL was significantly poorer relative to controls in the disease management group. This pattern of results persisted over 1 year. With some exceptions, better overall QoL was reported for light intensity exercise undertaken in group settings, with greater improvement in physical QoL following moderate intensity exercise. The implications for future health care practice and research are discussed.

Linkage of HDR capabilities software

Linkage of HDR capabilities software

http://www.hdrlabs.com/tools/links.html

Everything you need to know about HDRI
By Christian Bloch
http://www.hdrlabs.com/news/index.php
High Dynamic Range Imaging is a method to digitally capture and edit all light in a scene. It represents a quantum leap in imaging technology, as revolutionary as the leap from Black & White to Color imaging. If you are serious about photography, you will find that HDRI is the final step that places digital ahead of analog. The old problem of over- and underexposure in analog photography, which was never fully solved, is elegantly bypassed here. A huge variety of subjects can now be photographed for the first time ever.

HDRI emerged from the movie industry, and was once Hollywood's best kept secret. It is now a mature technology available to everyone. The only problem was that it was poorly documented until now. The HDRI Handbook is the manual that was missing.

Many questions remain open even for the hip CG artists that have been using HDRI for years. This is where The HDRI Handbook comes in. Included here is everything you need to build a comprehensive knowledge base that will enable you to become really creative with HDRI. This book is packed with practical hints and tips, software evaluations, workshops, and hands-on tutorials. Whether you are a photographer, CG artist, compositor, or cinematographer, this book is sure to enlighten you.

New Guidelines for Physical Activity Intervention for Weight Loss

New Guidelines for Physical Activity Intervention for Weight Loss
The American College of Sports Medicine (ACSM) has updated its guidelines for appropriate physical activity (PA) intervention strategies for weight loss and prevention of weight regain in adults.

The Position Stand updates the 2001 ACSM recommendations. The latter document discussed identifying adults needing weight loss, the magnitude of weight loss recommended, dietary strategies, the use of resistance exercise, the use of pharmacotherapy, behavioral techniques, and other topics.

"The purpose of the current update was to focus on new information that has been published after 1999, which may indicate that increased levels of ... PA may be necessary for prevention of weight gain, for weight loss, and prevention of weight regain compared to those recommended in the 2001 Position Stand," write Joseph E. Donnelly, EdD, and colleagues from the ASCM. "In particular, this update is in response to published information regarding the amount of PA needed for weight management found in the National Weight Control Registry and by the Institute of Medicine. This update was undertaken for persons older than 18 yr who were enrolled in PA trials designed for prevention of weight gain (i.e., weight stability), for weight loss, or prevention of weight regain."

Guidelines regarding weight control are needed because more than 66% of the adult population are overweight or obese, conditions which are associated with a variety of chronic diseases. Although guidelines of the National Heart, Lung, and Blood Institute recommend a 10% reduction in weight for those who are obese, much evidence supports a lowered health risk with 3% to 5% weight loss.

To prevent weight gain, to lose weight, and to prevent weight regain after weight loss, PA is recommended as a component of weight management. Light-intensity activity is defined as 1.1 to 2.9 metabolic equivalents, moderate-intensity activity as 3.0 to 5.9 metabolic equivalents, and vigorous activity as 6 or more metabolic equivalents.

Although the 2001 ACSM guidelines recommended a minimum of 150 minutes per week of moderate-intensity PA for overweight and obese adults to improve health and 200 to 300 minutes per week for long-term weight loss, the updated guidelines suggest that moderate-intensity PA between 150 and 250 minutes per week is effective to prevent weight gain but will provide only modest weight loss.

Clinically significant weight loss has been reported with greater amounts of PA (> 250 minutes per week). In studies that use moderate but not severe diet restriction, weight loss was improved by moderate-intensity PA between 150 and 250 minutes per week. After weight loss, weight maintenance is improved with PA of more than 250 minutes per week, according to findings of cross-sectional and prospective studies, but there have been no well-designed, randomized controlled trials to determine whether PA is effective to prevent weight regain after weight loss.

Although resistance training does not increase weight loss, it may increase fat-free mass and loss of fat mass while lowering health risk. Available data suggest that endurance PA or resistance training reduces health risk even without weight loss. Evidence to date is insufficient to determine whether PA prevents or ameliorates harmful changes in the risk for chronic disease during periods of weight gain.

Few studies to date have enrolled adults older than 65 years, but this is an important population to evaluate because of concerns that weight loss in older adults may cause loss of fat-free mass and potential bone loss. The position stand reviews the available evidence as it applies to the general population, while pointing out that individuals vary in their response to PA for prevention of weight gain, for weight loss, and for weight maintenance.

Although the review did not include studies of individuals with comorbid conditions that acutely affect weight, such as AIDS and type 1 diabetes, or pharmacotherapy trials, it did include trials enrolling individuals using medication for comorbid diseases, such as hypertension, cardiovascular disease, and type 2 diabetes.

Specific clinical recommendations, and their accompanying level of evidence rating, are as follows:

• For prevention of weight gain in most adults, PA of 150 to 250 minutes per week, with an energy equivalent of 1,200 to 2,000 kcal/week, will prevent weight gain of more than 3% (level of evidence, A).
• There is a dose-response effect of PA on weight loss, with PA of less than 150 minutes per week resulting in minimal weight loss, PA of more than 150 minutes per week in modest weight loss of approximately 2 to 3 kg, and PA of more than 225 to 420 minutes per week leading to weight loss of 5 to 7.5 kg (level of evidence, B).
• To maintain weight after weight loss, some studies suggest that PA of approximately 200 to 300 minutes per week will help minimize weight regain, although "more is better." To date, no well-designed, sufficiently powered, energy-balance studies provide evidence concerning the amount of PA needed to prevent weight regain after weight loss (level of evidence, B).
• Lifestyle PA, which is an ambiguous term that should be better defined to assess available evidence in the literature, may help counteract the small energy imbalance ultimately leading to obesity in most adults (level of evidence, B).
• If diet restriction is modest but not if diet restriction is severe, PA will increase weight loss (level of evidence, A).
• Resistance training is ineffective for weight loss with or without diet restriction, according to limited research evidence. However, some limited data suggest that resistance training enhances gain or maintenance of lean mass and loss of body fat during energy restriction. Furthermore, resistance training may also ameliorate risk factors for chronic disease, such as low high-density lipoprotein cholesterol levels, high low-density lipoprotein cholesterol levels, insulin sensitivity, and blood pressure (level of evidence, B).

"On the basis of the available scientific literature, the ACSM recommends that adults participate in at least 150 min/wk of moderate-intensity PA to prevent significant weight gain and reduce associated chronic disease risk factors," the guidelines authors write. "It is recommended that overweight and obese individuals participate in this level of PA to elicit modest reductions in body weight. However, there is likely a dose effect of PA, with greater weight loss and enhanced prevention of weight regained with doses of PA that approximate 250 to 300 min/wk (approximately 2,000 kcal/wk) of moderate intensity PA."

The guidelines authors note that these recommendations are consistent with those of the US Department of Health and Human Services Physical Activity Guidelines for Americans.
Med Sci Sports Exerc. 2009;41:459-471.

Pepsi to release sodas with natural sugar

Other linkage about high fructose corn syrup (HFCS) and corn syrup:

• Wikipedia: http://en.wikipedia.org/wiki/HFCS .
• The HFCS industry: http://www.sweetsurprise.com/ .
• The USDA: Dietary Assessment of Major Trends.
• The USDA: Consumption Timeline of Events producer driven.

Hypertension Awareness, Treatment, and Control -Continued Disparities in
Adults: United States, 2005-2006

High blood pressure (BP) is a modifiable risk factor for cardiovascular disease (CVD) (1). High BP increases the risk of heart attack, heart failure, stroke, and kidney disease (2-4). Conversely, favorable BP levels are associated with a greater probability of survival to age 85 as well as increased longevity without major co-morbidities (5,6). Increasing the awareness, treatment, and control of hypertension will reduce morbidity and mortality. This is a goal of national public health programs and initiatives such as the National High Blood Pressure Education Program (7). Data on levels of this risk factor in the U.S. population help to identify subgroups where risk may be greatest and prevention efforts might be targeted. Comparison over time can also show if the population is experiencing improvement in controlling elevated levels of BP.

Comparisons of percentage body fat, body mass inde... Flegal 2009

BACKGROUND: Body mass index (BMI), waist circumference (WC), and the waist-stature ratio (WSR) are considered to be possible proxies for adiposity. OBJECTIVE: The objective was to investigate the relations between BMI, WC, WSR, and percentage body fat (measured by dual-energy X-ray absorptiometry) in adults in a large nationally representative US population sample from the National Health and Nutrition Examination Survey (NHANES). DESIGN: BMI, WC, and WSR were compared with percentage body fat in a sample of 12,901 adults. RESULTS: WC, WSR, and BMI were significantly more correlated with each other than with percentage body fat (P < 0.0001 for all sex-age groups). Percentage body fat tended to be significantly more correlated with WC than with BMI in men but significantly more correlated with BMI than with WC in women (P < 0.0001 except in the oldest age group). WSR tended to be slightly more correlated with percentage body fat than was WC. Percentile values of BMI, WC, and WSR are shown that correspond to percentiles of percentage body fat increments of 5 percentage points. More than 90% of the sample could be categorized to within one category of percentage body fat by each measure. CONCLUSIONS: BMI, WC, and WSR perform similarly as indicators of body fatness and are more closely related to each other than with percentage body fat. These variables may be an inaccurate measure of percentage body fat for an individual, but they correspond fairly well overall with percentage body fat within sex-age groups and distinguish categories of percentage body fat.

How should I calculate a within-subject coefficient of variation?
by Martin Bland
In the study of measurement error, we sometimes find that the within-subject variation is not uniform but is proportional to the magnitude of the measurement. It is natural to estimate it in terms of the ratio within-subject standard deviation/mean, which we call the within-subject coefficient of variation.
In our British Medical Journal Statistics Note on the subject, Measurement error proportional to the mean, Doug Altman and I described how to calculate this using a logarithmic method. We take logarithms of the data and then find the within-subject standard deviation. We take the antilog of this and subtract one to get the coefficient of variation.
Alvine Bissery, statistician at the Centre d'Investigations Cliniques, Hôpital européen Georges Pompidou, Paris, pointed out that some authors suggest a more direct approach. We find the coefficient of variation for each subject separately, square these, find their mean, and take the square root of this mean. We can call this the root mean square approach. She asked what difference there is between these two methods.
In practice, there is very little difference between these two ways of estimating within-subject coefficient of variation. They give very similar estimates.
This simulation, done in Stata, shows what happens. (The function invnorm(uniform()) gives a standard Normal random variable.)
. clear
Set sample size to 100.
. set obs 100
obs was 0, now 100
We generate true values for the variable whose measurement we are simulating.
. gen t=6+invnorm(uniform())
We generate measurements x and y, with error proportional to the true value.
. gen x = t + invnorm(uniform())*t/20
. gen y = t + invnorm(uniform())*t/20
Calculate the within-subject variance for the natural scale values. (Within-subject variance is given by difference squared over 2 when we have pairs of subjects.)
. gen s2 = (x-y)^2/2
Calculate subject mean and s squared / mean squared, i.e. CV squared.
. gen m=(x+y)/2
. gen s2m2=s2/m^2
Calculate mean of s squared / mean squared.
. sum s2m2
Variable Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
s2m2 100 .0021519 .0030943 4.47e-07 .0166771
The within-subject CV is the square root of the mean of s squared / mean squared:
. disp sqrt(.0021519)
.04638858
Hence the within-subject CV is estimated to be 0.046 or 4.6%.
Now the log method. First we log transform.
. gen lx=log(x)
. gen ly=log(y)
Calculate the within-subject variance for the log values.
. gen s2l = (lx-ly)^2/2
. sum s2l
Variable Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
s2l 100 .0021566 .003106 4.46e-07 .0167704
The within-subject standard deviation on the log scale is the square root of the mean within-subject variance. The CV is the antilog (exponent since we are using natural logarithms) minus one.
. disp exp(sqrt(.0021566))-1
.04753439
Hence the within-subject CV is estimated to be 0.048 or 4.8%. Compare this with the direct estimate, which was 4.6%. The two estimates are almost the same.
If we average the CV estimated for each subject, rather than their squares, we do not get the same answer.
Calculate subject CV and find the mean.
. gen cv=sqrt(s2)/m
. sum cv
Variable Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
cv 100 .0361173 .0292567 .0006682 .1291399
This gives us the within-subject CV estimate = 0.036 or 3.6%. This is considerably smaller than the estimates by the root mean square method or the log method. The mean CV is not such a good estimate and we should avoid it.
Sometimes researchers estimate the within-subject CV using the mean and within-subject standard deviation for the whole data set. They estimate the within-subject standard deviation in the usual way, as if it were a constant. They then divide this by the mean of all the observations to give a CV. This appears to be a completely wrong approach, as it estimates a single value for a varying quantity. However, it often works remarkably well, though why it does I do not know. It works in this simulation:
. sum x y s2
Variable Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
x 100 6.097301 1.012154 3.62283 8.696612
y 100 6.081827 1.000043 3.759932 8.447584
s2 100 .0823188 .1212132 .0000193 .605556
The within-subject standard deviation is the square root of the mean of s2 and the overall mean is the average of the X mean and the Y mean. Hence the estimate of the within-subject CV is:
. disp sqrt(.0823188)/( (6.097301 + 6.081827)/2)
.04711545
So this method gives the estimated within-subject CV as 0.047 or 4.7%. This can be compared to the estimates by the root mean squared CV and the log methods, which were 4.6% and 4.8%. Why this should be I do not know, but it works. I do not know whether it would work in all cases, so I do not recommend it.
We can find confidence intervals quite easily for estimates by either the root mean square method or the log method. For the root mean square method, this is very direct. We have the mean of the squared CV, so we use the usual confidence interval for a mean on this, then take the square root.
. sum s2m2
Variable Obs Mean Std. Dev. Min Max
---------+-----------------------------------------------------
s2m2 100 .0021519 .0030943 4.47e-07 .0166771
The standard error is the standard deviation of the CVs divided by the square root of the sample size.
. disp .0030943/sqrt(100)
.00030943
The 95% confidence interval for the squared CV can be found by the mean minus or plus 1.96 standard errors. If the sample is small we should use the t distribution here. However, the squared CVs are unlikely to be Normal, so the CI will still be very approximate.
. disp .0021519 - 1.96*.00030943
.00154542
. disp .0021519 + 1.96*.00030943
.00275838
The square roots of these limits give the 95% confidence interval for the CV.
disp sqrt(.00154542)
.03931183
. disp sqrt(.00275838)
.05252028
Hence the 95% confidence interval for the within-subject CV by the root mean square method is 0.039 to 0.053, or 3.9% to 5.3%.
For the log method, we can find a confidence interval for the within-subject standard deviation on the log scale. The standard error is s_w/root(2n(m-1)), where s_w is the within-subject standard deviation, n is the number of subjects, and m is the number of observations per subject.
In the simulation, s_w = root(0.0021566) = 0.0464392, n = 100, and m = 2.
Hence the standard error is 0.0464392/root(2 * 100 * (2-1)) = 0.0032837.
The 95% confidence interval is 0.0464392 - 1.96*0.0032837 = 0.0400031 to 0.0464392 + 1.96*0.0032837 = 0.0528753.
Finally, we antilog these limits and subtract one to give confidence limits for the CV: exp(0.0400031)-1 = 0.040814 and exp(0.0528753)-1 = 0.05429817, so the 95% confidence interval for the within-subject CV is 0.041 to 0.053, or 4.1% to 5.3%. These are slightly narrower than the root mean square confidence limits, but very similar.
I would conclude that either the root mean square method or the log method can be used.

A List of Statistics Notes of the British Medical Journal

A List of Statistics Notes of the British Medical Journal

• A list of notes on BMJ website
• Doug Altman nicely lists all the notes on one webpage. He died on June 3rd 2018 Sadly.

What Is Gestational Diabetes?

see Diabetes Care

2009 Diabetes Clinical Practice Recommendations

2009 Diabetes Clinical Practice Recommendations

http://care.diabetesjournals.org/content/vol32/Supplement_1/