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Auckland University (AUT) Researcher Kim Santabarbara Investigates Strength Training and the Menstrual Cycle

29.07.2022
kim_a73a25194b

Though the pandemic introduced the necessary transition to remote monitoring, companies like Wild.AI have made conducting research from afar easily accessible. No one can attest to remote research better than PhD candidate Kim Santabarbara of the Auckland University of Technology. Kim began her PhD experience in January of 2020, meaning that she had no other choice but to start her journey remotely.

The central goal of Kim’s dissertation work is to “contribut[e] to existing data on women who participate in weightlifting or resistance training.” She also hopes to “normalize the menstrual cycle experience as an athlete.” While facing the difficulty of remote collection, Kim’s research is transcontinental, providing her with an international participant pool to base her findings. She hopes that this diverse set of individuals will help to give insight into “what a common, collective experience the menstrual cycle is around the globe.”

Kim’s research couples sports psychology and physiology because it focuses on how menstrual cycle patterns drive training in resistance-trained athletes in addition to physiologic biomarkers (i.e., heart rate and hormones). Kim’s research stemmed from an observation that a majority of female driven research focuses on endurance trained individuals. She notes that this focus “is cool, and relevant, and interesting. But that body type, and those activities are so polar opposite of someone who is like a weightlifter.” Thus, came the focus of her research: What does a common menstrual cycle look like for a female weightlifter?

Picture2.png

Figure adapted from Nuckols 2022 [1]. Based on 628 studies on resistance trained athletes, less than 25% of participants contained female athletes, and less than 12% of studies were solely composed of female participants.

Aside from providing the resistance-trained female athlete information regarding common cycle patterns, Kim hopes that her research will help provide insight into some common misconceptions around weightlifting. For example, RED-S and the Female Athlete Triad are often thought of as endurance-specific disorders due to the prevalence of low energy availability in such sports. However, Kim fears that this excludes athletes that go through periods of cutting, which may also suffer the effects of low energy availability.

Though Kim originally flirted with the idea of developing something on her own, she instead decided to partner with Wild.AI because of, as she words it, “their willingness to help and support research.” Kim worked alongside Wild.AI developers to customize an app interface that was specific to her study.

Wild.AI has helped Kim in her data collection by providing a single space for data input for participants. In addition to Wild.AI’s typical check-in questions, individuals are asked to input their LH Test reading (positive/negative), their BBT measurement, and completion of study specific stretching tasks.

A second focus of Kim’s research centers on how various menstrual phases affect the rating of perceived exertion (RPE) throughout exercise sessions. Essentially, Kim says, this study aims to answer the question of “Does your perception of your ability to work[…] change throughout your cycle?” Kim believes that athletes likely cycle train by default, based on how they feel on a given day. However, only the data will show whether this hypothesis is supported or not.

The most difficult aspect of conducting research remotely is ensuring participant compliance. In hopes of easing some of the stress that stems from remote monitoring, Wild.AI created a personalized link allowing Kim to download participant responses at any time. She downloads responses biweekly to check for participant compliance and begin initial data analysis. To help ensure participant anonymity and protection, Wild.AI helped to create a participant ID system for the study. When asked what the benefit of using Wild.AI for her research was, Kim noted that “it is clear that [Wild.AI is] very dedicated to creating something that is research-focused.”

References [1] Nuckols, Greg. “Where Are All the Female Participants in Strength, Hypertrophy, and Supplement Research?” Stronger by Science, 1 June 2022, https://www.strongerbyscience.com/representation/.

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kim_a73a25194b

Though the pandemic introduced the necessary transition to remote monitoring, companies like Wild.AI have made conducting research from afar easily accessible. No one can attest to remote research better than PhD candidate Kim Santabarbara of the Auckland University of Technology. Kim began her PhD experience in January of 2020, meaning that she had no other choice but to start her journey remotely.

The central goal of Kim’s dissertation work is to “contribut[e] to existing data on women who participate in weightlifting or resistance training.” She also hopes to “normalize the menstrual cycle experience as an athlete.” While facing the difficulty of remote collection, Kim’s research is transcontinental, providing her with an international participant pool to base her findings. She hopes that this diverse set of individuals will help to give insight into “what a common, collective experience the menstrual cycle is around the globe.”

Kim’s research couples sports psychology and physiology because it focuses on how menstrual cycle patterns drive training in resistance-trained athletes in addition to physiologic biomarkers (i.e., heart rate and hormones). Kim’s research stemmed from an observation that a majority of female driven research focuses on endurance trained individuals. She notes that this focus “is cool, and relevant, and interesting. But that body type, and those activities are so polar opposite of someone who is like a weightlifter.” Thus, came the focus of her research: What does a common menstrual cycle look like for a female weightlifter?

Picture2.png

Figure adapted from Nuckols 2022 [1]. Based on 628 studies on resistance trained athletes, less than 25% of participants contained female athletes, and less than 12% of studies were solely composed of female participants.

Aside from providing the resistance-trained female athlete information regarding common cycle patterns, Kim hopes that her research will help provide insight into some common misconceptions around weightlifting. For example, RED-S and the Female Athlete Triad are often thought of as endurance-specific disorders due to the prevalence of low energy availability in such sports. However, Kim fears that this excludes athletes that go through periods of cutting, which may also suffer the effects of low energy availability.

Though Kim originally flirted with the idea of developing something on her own, she instead decided to partner with Wild.AI because of, as she words it, “their willingness to help and support research.” Kim worked alongside Wild.AI developers to customize an app interface that was specific to her study.

Wild.AI has helped Kim in her data collection by providing a single space for data input for participants. In addition to Wild.AI’s typical check-in questions, individuals are asked to input their LH Test reading (positive/negative), their BBT measurement, and completion of study specific stretching tasks.

A second focus of Kim’s research centers on how various menstrual phases affect the rating of perceived exertion (RPE) throughout exercise sessions. Essentially, Kim says, this study aims to answer the question of “Does your perception of your ability to work[…] change throughout your cycle?” Kim believes that athletes likely cycle train by default, based on how they feel on a given day. However, only the data will show whether this hypothesis is supported or not.

The most difficult aspect of conducting research remotely is ensuring participant compliance. In hopes of easing some of the stress that stems from remote monitoring, Wild.AI created a personalized link allowing Kim to download participant responses at any time. She downloads responses biweekly to check for participant compliance and begin initial data analysis. To help ensure participant anonymity and protection, Wild.AI helped to create a participant ID system for the study. When asked what the benefit of using Wild.AI for her research was, Kim noted that “it is clear that [Wild.AI is] very dedicated to creating something that is research-focused.”

References [1] Nuckols, Greg. “Where Are All the Female Participants in Strength, Hypertrophy, and Supplement Research?” Stronger by Science, 1 June 2022, https://www.strongerbyscience.com/representation/.

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