running online injury prevention feasibility study

Conditions: Musculoskeletal Injury

Summary: the main purpose of this study is to test the feasibility of a larger scale interventional study. the investigators want to understand whether data can be reliably collected data various different streams while participants follow a standard 12-week half-marathon training program, and whether participants follow training recommendations.

research procedures will include an online eligibility questionnaire, a demographic intake questionnaire, a weekly injury questionnaire, and an exit questionnaire (hosted on the sfu survey monkey platform). this study involves an initial in-person 1-hour orientation session. multiple dates will be offered in order to accommodate participants. participants will be randomly allocated to either the control or intervention group. all participants will be given a pair of running shoes (hettas sport ltd.). half of the participants in each group will also be outfitted with a pair of shoe-embedded sensors (plantiga technologies, inc.). all participants will also be given access to a menstrual cycle tracking app (mynormative). if participants have a strava profile already, this will be linked to the study third-party data extraction api called "strava api" to give the research team access to select workout data. if participants do not have a strava profile, an anonymous profile will be created and linked to the api. participants will be oriented on how to use the shoe-embedded sensors and mynormative app. following a one to two week familiarization period in which the participants can become comfortable with the protocol, participants will commence a 12-week half-marathon training program guided by a coach. the training program will consist of four runs per week, including two optional in-person group runs, and will culminate with a half-marathon race. over the course of the training program, participants will be responsible for uploading the data from the shoe-embedded sensors after each run (for those that have them), tracking their menstrual cycle with the mynormative app, and recording their runs with their gps-enabled smartwatch which will automatically upload training data to strava. participants in the intervention group will be given individualized training recommendations based on their training data uploaded to strava (e.g., "training load is high, take a rest day tomorrow" or "training load is appropriate, continue as planned"). training load will be calculated from average distance, speed, step count, and heart rate. participants in the control group will receive generic training recommendations (e.g., "keep up the good work" or "make sure to balance training with enough recovery time"). communication with the participants will occur via text message on their personal cell phone. following completion of the study, participants in the intervention group will be asked to complete an online questionnaire about why the participants did/did not follow the individualized training advice. participants will be permitted to keep the running shoes but will be required to return the shoe-embedded sensors at study completion.

Eligibility criteria:
inclusion criteria: * cis-gender female (someone who was born female and currently identifies as a woman) runners * over the age of 18 * have not been injured in the last 3 months (\*\*for the purpose of this study, an injury is defined as "musculoskeletal (originating from the bones, muscles, joints) that has restricted the ability to run in any way (distance, speed, duration) for at least 7 days or for 3 consecutive training sessions"\*\*) * have run on average 3 times/week for the last 6 months * own a gps-enabled smart watch with an optical wrist heart rate sensor * fit shoe size between 6.5 and 8.5 us women's sizing * able to attend one of the in-person orientation sessions * able to understand written and spoken english exclusion criteria: * any current pain with running

Keywords: running wearable sensors biomechanics training education female athlete