Cover lesson · PE · Year 9 · Advanced

Evaluating wearable fitness tracker data and privacy

DCF: Citizenship → Health and well-being

Also assessed, LNF: Numeracy → Learning that statistics represent data and that probability models chance helps us make informed inferences and decisions → Interpreting data

Name: Class: Date:

Cover teacher: you need no subject knowledge

What this practises: digital health, privacy and data interpretation in PE. Pupils evaluate 6 sessions of wrist fitness tracker data from an invented Year 9 sprinter against chest-strap and stopwatch benchmarks. They calculate mean heart-rate error, recovery rate per minute, and percentage inaccuracy on short sprints versus steady runs, audit 4 privacy and well-being risks in the companion app, and write recommendations for safe use in school sport. No computers are needed.

Board: open the supply teacher presentation for board slides, timers and spoken script.

Print: this worksheet, one per pupil, and the stimulus sheet “PulseBand Go: Sprint Log, Benchmarks and App Privacy Settings”, one between two. Hand out calculators.

Run: 5 min settling starter · 5 min formulae and brief · 10 min Step 1 (heart-rate error and recovery rate) · 10 min Step 2 (GPS percentage inaccuracy) · 12 min Step 3 (privacy and well-being audit) · 5 min Step 4 (PE department recommendations) · 3 min plenary review. Total 50 minutes.

Collect: this worksheet. Quick checks: Step 1 heart-rate errors are 12, 11, 2, 15, 2, 12 bpm (total 54, mean 9 bpm; sprint mean 12.5 bpm vs steady mean 2 bpm); recovery rates are 24, 25, 22, 26, 20, 26 bpm/min. Step 2 GPS inaccuracies are 8%, 9%, 6%, 1%.

What you are doing

Wearable fitness trackers are popular in athletics, but optical wrist sensors and GPS chips have physical limits, and social fitness apps can expose personal data or encourage overtraining. You will test the accuracy of Nia’s PulseBand Go watch against benchmark equipment and audit her app settings.

Step 1: calculate heart-rate error and recovery rate per minute

(a) Use Table 1 on the stimulus sheet to complete the table below.

Session Type Chest-strap peak (bpm) Wrist peak (bpm) Error: Chest − Wrist (bpm) 2-min recovery HR (bpm) Recovery rate per min: (Peak − Rec) ÷ 2
1 Short sprint 184 172 12 136 24 bpm/min
2 Short sprint 188 177 138
3 Steady run 170 168 126
4 Short sprint 192 177 140
5 Steady run 156 154 116
6 Short sprint 186 174 134

(b) Calculate the mean heart-rate error across all 6 sessions, then compare the mean error for the 4 short sprint sessions (Sessions 1, 2, 4 and 6) against the 2 steady runs (Sessions 3 and 5). Why does the wrist sensor struggle on sprints?

Step 2: calculate percentage inaccuracy on GPS sprint distances

(a) Use Table 2 on the stimulus sheet to calculate the distance error and percentage inaccuracy for each run.

Run description True distance (m) Wrist GPS (m) Distance error (m) Percentage inaccuracy: (Error ÷ True) × 100
100 m straight sprint 100 92 8 m 8%
200 m bend sprint 200 182
400 m one-lap sprint 400 376
2,000 m straight park path 2,000 1,980

(b) Why is the percentage inaccuracy highest on the 200 m bend sprint (9%) and lowest on the 2,000 m straight park path (1%)? Why should Nia keep using a stopwatch for 100 m timing?

Step 3: audit privacy and well-being risks in the companion app

Read Section 3 of the stimulus sheet. For each setting, explain the safeguarding or well-being risk and write the safe setting Nia should use instead.

App setting Why this is a privacy or well-being risk Safe setting to choose instead
A: Public profile (name, age 14, school, photo)
B: Full street GPS map (14 Heol y Castell, Tue/Thu 07:15)
C: Live Location Beacon (enabled for 312 followers)
D: 21:30 “Streak lost!” alerts (exercising with a sore ankle)

Step 4: write recommendations for the school PE department

Write a 4 to 5 sentence recommendation to the Head of PE explaining when wearable wrist trackers are accurate enough to use, when benchmark equipment is needed instead, and what privacy and well-being rules pupils must follow. Quote at least two figures from Steps 1 and 2.

Step by step

  1. 5 min Settling starter: find the Session 1 heart-rate error from Table 1.
  2. 5 min Check the formulae for recovery rate and percentage inaccuracy on the board.
  3. 10 min Complete Step 1: calculate heart-rate errors, means and recovery rates per minute.
  4. 10 min Complete Step 2: calculate GPS distance errors and percentage inaccuracies.
  5. 12 min Complete Step 3: audit the four privacy and well-being risks in Nia’s app settings.
  6. 5 min Write your data-backed recommendation for the PE department in Step 4.
  7. 3 min Check all calculations against the plenary slide.

Success criteria

If you finish early