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Learn the features of well-designed research on sport supplements

By Louise Burke


Feature

Strategies

1. Appropriate experimental design and sample size

• Incorporate the use of a placebo treatment or trial to overcome the psychological effect of supplementation. It is also interesting, if practical, to add a control (no treatment) trial so that the magnitude of the placebo effect can be determined.

• Where possible, use a repeated-measures or crossover design, in which each participant acts as his own control by undertaking both the treatment and placebo trials. This is a stronger statistical design than an experimental-placebo design (two separate groups of participants who receive either the treatment or the placebo) and requires a smaller sample size.

• Randomly assign participants to treatment and placebo groups, balancing for participant features (e.g., sex, age, fitness, or training characteristics) that could interact with the treatment.

• In a crossover study, provide each of the treatments to participants in a randomized counterbalanced order to remove the effect of time or training on study outcomes. In other words, have equal numbers of participants receive all treatments in the various possible sequences of order. Allow a suitable washout period in a crossover-designed study so that the effects of a treatment are removed before the next trial begins.

• Where possible, use a double-blind allocation of treatments to remove the subjective bias of both researcher and participants. The placebo effect has been most identified in terms of participant expectations. It is not always possible to blind the treatment to participants. When this is the case, use a single-blind presentation in which key researchers who measure performance outcomes are not aware of the treatment received by participants. Blinding of the researchers will help to control the occurrence of the "halo effect," where an observer who believes an effect is likely "marks up" or encourages the performance of participants.

• Choose the sample size after considering the likely range of changes in the measurements of interest. Power analysis of changes in outcome measures will determine the minimum number of participants needed to detect changes that are worthwhile.

 

2. Appropriate treatment protocol

 

• Choose a supplementation protocol-timing, amount, and duration of supplement use-that maximizes the likelihood of a positive outcome. This may not always be the dose recommended by the manufacturer. Other information can be found from pilot investigations.

• Alternatively, replicate a supplementation protocol that represents the popular use patterns among athletes.

• If a positive effect is found, manipulate doses in further trials to refine the optimal supplementation protocol.

 

3. Appropriate choice of participants

 

• Recruit participants who represent the population for whom recommendations about supplement use are needed.

• Be aware that effects seen with untrained or recreational participants may not apply to welltrained or elite athletes. Training status may affect the outcomes of supplementation. Characteristics that make athletes elite may also cause them to react differently to a treatment.

• Be aware that the performances of highly-trained athletes are typically more reliable than those of recreational participants, especially when athletes are familiar with an exercise protocol. A reduction in intra- and interparticipant variability in performance will increase the statistical power of the study and increase the chance of detecting small but worthwhile changes in performance.

 

4. Appropriate exercise stimulus and performance protocol

• Develop laboratory or field protocols that mimic the demands and environments in which a real-life sport is played.

• Ensure that the exercise protocol provides the physiological stimulus that the supplement is suggested to address.

• Ensure that athletes are familiar with the exercise or performance protocol. If this is not a field or laboratory test that is already incorporated into the athlete’s training or competition program, allow participants to undertake familiarization training until they are able to undertake reliable performances. Participants may need time to experiment with new equipment and with appropriate pace judgment.

• Consider all strategies to standardize or supervise the test protocol so that reliability is optimized. A reliable test will increase the change of detecting small changes in performance that are worthwhile in real life.

5. Consideration of variables to explain performance changes

 

• Where possible and practical, collect data that can explain or support observations of performance changes. Although the athlete may only be interested in performance outcomes, the sport scientist will be interested in physiological and psychological factors that underpin performance changes. This understanding is more than an academic interest; proof of an underlying mechanism can corroborate the observed performance benefits and offer insight to fine-tune protocols for using the supplement.

• Choose parameters that are directly relevant to the hypothesis that is being tested and sufficiently reliable to detect important changes. These parameters should only be measured if this does not interfere with the athlete’s ability to perform in the exercise protocol. Otherwise, studies should be separated into those that measure performance and those that monitor mechanisms explaining performance changes. It is not always possible to monitor both simultaneously.

6. Standardization of conditions

• Standardize extraneous variables that can affect metabolism and performance during exercise, to reduce inter- and intratrial variability and enhance the likelihood of detecting the effects of the supplement. Such variables include overall training status of participants, acute diet and training on the day(s) before each trial, pretrial hydration, participant fatigue, and environmental conditions.

• Choose standardized conditions that mimic the real-life practices in sport.

7. Appropriate analysis and interpretation of results

 

• Undertake a valid statistical analysis of the data.

• Interpret results in light of the changes that are worthwhile to an athlete and a specific sport.




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