Cycling Ireland Paralympic Team Subjective Wellness Log & Load Monitoring

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1 Introduction

As part of Cycling Ireland’s athlete support a subjective wellness log is required to be filled out weekly by each athlete on the programme. This log is filled out via Microsoft Forms, and collects information such as injury/illness status, mood, sleep and other such variables. Combined with this, athletes upload their training data to a web application Training Peaks, this application stores an abundance of performance metrics specific to cycling (e.g., power data, heart rate, GPS data etc.). The collection of this data allows the coaching staff to monitor both internal and external load metrics.

The aim of this report is to retrospectively analyze the data from ten athletes leading up to the Paris 2024 Paralympic Games. A sample of this data can be seen in the Appendix.

2 Overview of Terminology Used & Metrics Collected

2.1 Load Metrics

  • Distance is expressed in meters (m) and collected via wearable GPS.
  • Energy is the amount of kilojoules (kj) expended captured from power meter data.
  • Total time in hours is the logged training hours of gym and cycling specifically.
  • Intensity Factor IF is the rating of intensity of a session, more can be found by clicking the hyperlink.
  • Training Stress Score TSS is a measure of the overall load of a session, more can be found by clicking the hyperlink.
  • Weekly Volume Rating is a basic likert scale from 1-10 rating volume of training of the past 7 days from low (1) to high (10).
  • Weekly Fatigue Rating is another likert scale from low (1) to high (10).
  • Weekly Intensity Rating again is rated from low (1) to high (10).

2.1.1 Sleep Metrics

  • Sleep Quality is rated from very poor (1) to excellent (10).
  • Sleep Quantity is rated in buckets from <6 hours, 6-8, and 8-11 hours.

2.1.2 Heath & Wellness

  • The Oslo Sports Trauma Research Center Questionnaire on Health Problems OSTRCH2 was used to assess injury/illness status. The first question acting as a filter whereby if an injury/illness was disclosed a further series of questions would be asked.
  • Menstrual Cycle Effect ranges from N/A for male athletes, No effect, mild, to severe rating.
  • Weekly diet rating asks athletes to rate their diet from poor (1) to excellent (5).
  • Weekly Stress Rating asks the athletes to rate stress from low (1) to high (10).
  • Weekly Mood Rating asks the athletes to rate mood from poor (1) to excellent (10).
  • New Health Concern was a yes/no option which then triggered a follow up from the medical team.

2.1.3 Travel

  • Long Travel asks athletes to specify if they have taken a long journey in the past 7 days.
  • Travel Mode asks what form of travel this was (e.g., plane)

2.1.4 Other

  • Para_cat is the Para-cyclists category of impairment, more on this can be found here or in the below table.
Para-cyclists Category Description
Category Description
C Athletes who are able to use a standard bicycle compete in the five sport classes C1-5. The sport class profiles include amputations, impaired muscle power, or range of motion, and also impairments affecting coordination, such as ataxia and athetosis. Sport class C1 is allocated to athletes with the most severe activity limitation, while the sport class C5 is allocated to athletes who meet the minimum impairment criteria.
B Cyclists with a visual impairment
H For athletes with impairments affecting either both legs or a combination of the upper and lower limbs (amputees, paraplegics and tetraplegics). These athletes use handcycles.
Pilot A sighted cyclist piloting a tandem.

3 Compliance

The compliance rate across the 10 athletes can be seen below. The overall compliance rate was 66.95% which is slightly lower than similar surveys within Olympic & Paralympic populations (78.4% in Clarsen et al. 2023 & 75% in Pinheiro et al. 2024). A heatmap of the compliance per athlete can be seen also, the duration of this data capture was from week 6 2024, until week 40, 2024. The heat map shows us that there were high levels of compliance during periods of the year when in-person contact time was high with the coaching team (i.e., racing or training camp). Future surveys would be served well to aim to increase response rates by means such as email prompts, reminders, generating athlete facing reports etc.

5 Charting Subjective Mood & Stress

From the below heat map we can see mood and stress on a “traffic light” style scale over the duration of the wellness monitoring survey. Generally mood was within moderate, or mid-point levels but for some athletes there were sustained periods of low mood. Stress levels tracked similar to mood. Visibly, for some athletes you can see stress levels increase as they get closer to the weeks around the Paralympic Games.

5.1 Contributors to Stress

Below we can see injury/illness status and its contribution to the stress level of the athlete. Whilst we understand that stress levels can be high due to several other causes, median values here show us that when training participation is reduced stress levels increase.

6 Subjective Markers Versus Objective

Within the monitoring survey a rating of both training intensity and volume was gathered from the athletes on a 1-10 scale. Training Peaks also collects data within this domain in the form of Intensity Factor, Training Stress Score, and Total Time in Hours.

6.1 Subjective Intensity V. IF

A linear regression analysis revealed a significant positive relationship between previous-week intensity factor and subjective intensity rating (β = 7.12, p < 0.001). Higher objective training intensity was associated with higher perceived intensity. Intensity factor explained approximately 8.8% of the variance in subjective intensity ratings (R² = 0.088). This can be seen below. In effect this tells us that an increase of 1.0 in IF, subjective intensity rating increases by 7.12 points BUT IF never increases by 1.0 in real training — IF typically ranges from about 0.6 to 0.9. Scaling this back to a realistic figure for IF we can estimate that a 0.1 increase in IF will result in a 0.71 increase in subjective fatigue.

6.2 subjective Intensity V. TSS

Previous-week Training Stress Score was significantly associated with subjective intensity rating (β = 0.00355, p < 0.001), explaining 21.2% of the variance (R² = 0.212). This relationship was stronger than that observed for intensity factor alone (R² = 0.088), suggesting that subjective intensity perception reflects overall training stress rather than intensity in isolation. In effect the regression tells us that for every one 1 point increase in TSS, weekly subjective intensity raises by 0.0035 units, scaled to a more realistic figure, this contends that for every 100 point increase in TSS the fatigue rating raises by 0.35 points.

6.3 Subjective Volume V. Total Training Time

Previous-week total training time was also significantly associated with subjective volume rating (β = 0.207, p < 0.001), explaining 25.2% of the variance in perceived training volume (R² = 0.252). Greater objective training duration was associated with higher perceived training volume. For every 1 hour increase in volume, we see an increase of 0.21 points in volume ratings.

6.4 Subjective Volume V. TSS

Previous-week Training Stress Score was significantly associated with subjective volume rating (β = 0.00408, p < 0.001), explaining 26.5% of the variance in perceived training volume (R² = 0.265). Higher objective training stress was associated with higher perceived training volume. In effect for every 100 TSS points gained we can expect to see volume ratings going up by approximately 0.4 points.

6.5 What this means for the coach?

Subjective training ratings were significantly associated with objective training load metrics, indicating that athletes’ perceptions reflect their actual training demands. This underscores the value of collecting these metrics, as if they are not broadly tracking with objective data we can direct further questioning into illness, motivation and validity of the measures calculating the objective Training Peaks data etc.

7 Sleep

Sleep quality and quantity were recorded in this monitoring questionnaire. Sleep quality was rated from 1-10 (poor-great), quantity was rated in bins of <6, 6-8, 8-11, and 11+ hours. 11+ hours was only recorded once, and so eliminated from the below graphic. We can see a general trend of greater sleep duration leading to better sleep quality. For the purpose of this visual representation we put sleep quality into bins of 1-3 = poor, 4-6 = moderate, and >7 = good. In the box plot below we can see that generally when stress levels increase, sleep is noted as poor, highlighting the impact psychological factors can have on our sleep.

8 Travel

8.1 Travel and Illness

Long travel was not significantly associated with training participation status (p = 0.392). However, descriptive trends indicated higher rates of participation with illness during travel weeks, suggesting travel may contribute to increased physiological stress. Periods of travel often are combined with sustained periods of overload (training camp) or competition suggesting these also may play a role in injury/illness. These findings highlight the importance of monitoring athlete wellness during travel periods.

Training Participation Status

No Travel (%)

Travel (%)

Cannot participate due to injury/illness

1.8

1.7

Full Participation with Injury/Illness

5.4

11.8

Full Participation without health/injury problems.

82.1

76.5

Reduced Participation due to injury/illness

10.7

10.1

8.2 Travel and Fatigue

Weekly fatigue ratings were significantly higher during travel weeks compared to non-travel weeks (Wilcoxon rank-sum test, p = 0.017). Median fatigue increased from 5 during non-travel weeks to 6 during travel weeks, indicating that travel contributes to increased physiological fatigue. In contrast, median stress ratings remained unchanged at 5, and no statistically significant difference in stress was observed (p = 0.272). These findings suggest that travel primarily impacts physical fatigue rather than perceived psychological stress, highlighting the importance of adjusting training load and prioritizing recovery strategies following travel.

8.3 What this means for the coach?

Travel within this sample has been shown to increase subjective ratings of fatigue, by one point on the likert scale, this represents an approximate 20% increase in athlete fatigue when they have had a travel week. Whilst there was no significant effect on injury/illness and travel this is likely due to the low sample size, on a practical level there was an increase in riders participating with an injury/illness after a travel week of 6.4%. As we move into the LA Olympic/Paralympic Cycle there will be much more frequent long haul travel, which highlights the importance of monitoring, and understanding travel’s impact within this cohort.

9 Conclusion

Overall this subjective wellness log proves beneficial in capturing meaningful data that correlates well with objective training data collected by Training Peaks. In order for the wellness log to achieve higher levels of impact it is imperative that athletes fill it out weekly. Whilst there is a lot of data contained here analyzing trends on an individual athlete level is difficult due to the missing data from poor adherence. We can see quite different training load characteristics between para-cyclist categories, suggesting that coaches need to meet athletes on an individual level in order to maximize training prescription and thus performance with their impairment in mind. Stress, and low mood were highlighted throughout the period of collection, periods of competition stress, and injury/illness related reductions in training participation contributed to higher levels of these markers. When sleep volume was reported as +8 hours there was a general trend of positive sleep quality, suggesting athletes should firstly aim to maximize sleep duration. As expected stress levels worsened sleep quality.

10 Appendix

10.1 Appendix 1: An Example of Data Collected

week_number

id

para_cat

date_day

month_number

timestamp

training_participation

menstrual_cycle_effect

training_modification

injury_illness_impact

symptom_experience

long_travel

travel_mode

weekly_diet_rating

weekly_stress_rating

weekly_mood_rating

weekly_volume_rating

weekly_fatigue_rating

weekly_intensity_rating

sleep_hours

sleep_quality

new_health_concern

DistanceInMeters

Energy

TimeTotalInHours

IF

TSS

completed

9

1

C

26

2

26/02/2024 14:56

Full Participation with Injury/Illness

No Reduction

To a minor extent (Reduction in performance by 1-4%)

To a mild Extent

Yes

Plane

3

2

5

8

8

8

8-11hrs

7

No

342,660.801

8,845.047

21.095969

0.5923490

982.45

true

9

2

C

26

2

26/02/2024 12:01

Full Participation without health/injury problems.

No Effect

Yes

Plane

3

2

5

5

2

9

6-8hrs

5

No

236,009.033

4,072.821

10.037701

0.6684280

350.64

true

9

3

Pilot

27

2

27/02/2024 07:28

Full Participation with Injury/Illness

No Reduction

No Reduction

No pain

Yes

Plane

4

4

7

6

6

6

8-11hrs

5

No

508,957.324

13,220.168

15.920841

0.6530768

979.24

true

9

4

B

26

2

26/02/2024 19:45

Full Participation without health/injury problems.

Yes

Plane

5

1

9

7

6

8

6-8hrs

6

No

346,971.330

10,068.027

16.396111

0.6404083

675.71

true

9

5

H

27

2

27/02/2024 01:19

Full Participation without health/injury problems.

No

4

3

6

6

4

6

6-8hrs

6

No

1,476.740

8,730.978

17.691389

0.7246629

907.63

true

9

6

Pilot

26

2

26/02/2024 16:40

Full Participation without health/injury problems.

No Effect

Yes

Plane

4

5

8

7

7

7

8-11hrs

8

Yes

445,939.613

9,035.557

17.798889

0.6550064

805.99

true

9

7

Pilot

26

2

26/02/2024 14:21

Full Participation without health/injury problems.

Mild Effect (Unable to comfortably complete some training session)

Yes

Plane

4

5

8

7

8

7

6-8hrs

7

No

338,087.812

7,747.918

13.739445

0.6893843

720.26

true

9

8

B

27

2

27/02/2024 19:26

Full Participation without health/injury problems.

No Effect

Yes

Plane

5

5

5

7

7

6

6-8hrs

6

No

380,137.936

5,121.276

16.702222

0.6647750

841.38

true

9

9

C

130,661.051

2,289.751

9.642778

0.6187162

401.72

false

9

10

B

26

2

26/02/2024 16:47

Full Participation without health/injury problems.

No Effect

Yes

Car

4

8

6

5

5

6

8-11hrs

7

No

1,105.167

8,932.481

17.520000

0.6525806

815.33

true

11 References

  • Pinheiro, L. S. P., Silva, A., Madaleno, F. O., Verhagen, E., de Mello, M. T., Ocarino, J. M., & Resende, R. A. (2024). Prevalence and incidence of health problems and their characteristics in Brazilian para athletes: A one-season single-center prospective pilot study. Disability and health journal, 17(1), 101511. https://doi.org/10.1016/j.dhjo.2023.101511
  • Clarsen, B., Berge, H. M., Bendiksen, F., Fossan, B., Fredriksen, H., Haugvad, L., Kjelsberg, M., Ronsen, O., Steffen, K., Torgalsen, T., & Bahr, R. (2023). Injury and illness among Norwegian Olympic athletes during preparation for five consecutive Summer and Winter Games. British journal of sports medicine, bjsports-2023-107128. Advance online publication. https://doi.org/10.1136/bjsports-2023-107128