Knowledge quiz

Forty questions, eight for each module, with the answer and a short explanation for every one.

Choose what to test

All five modules together are the full quiz. Only the full quiz gives the score you track over time.

Module 1: Fundamentals of Wearable Technology8 items
Module 2: Real-Time Data Tracking8 items
Module 3: Performance Optimization8 items
Module 4: Injury Prevention8 items
Module 5: Integration into Coaching Practice8 items

40 items

Module 1: Fundamentals
1.1 Which of the following is a measured physical quantity, not a derived metric?
Why
Acceleration is the raw signal a sensor captures; the others are computed from it.
Read again: Module 1, 1.2 Sensor types and what they actually measure
Module 1: Fundamentals
1.2 Under GDPR, an athlete's heart-rate data is most appropriately classified as:
Why
Health data falls under Article 9 special categories and needs stricter handling.
Read again: Module 1, 1.5 First-look ethics and GDPR: the consent baseline
Module 1: Fundamentals
1.3 A wearable that is accurate in the lab but unreliable during outdoor team training lacks:
Why
Ecological validity asks whether lab performance holds up in real coaching conditions.
Read again: Module 1, 1.4 Validity and reliability for coaches who are not researchers
Module 1: Fundamentals
1.4 The optical PPG heart-rate signal is MOST sensitive to:
Why
Wrist PPG is degraded by motion, skin factors and how tightly it is worn.
Read again: Module 1, 1.2 Sensor types and what they actually measure
Module 1: Fundamentals
1.5 GPS-derived metrics such as total distance are most robust:
Why
GNSS needs clear sky view; it degrades or fails indoors and in dense structures.
Read again: Module 1, 1.2 Sensor types and what they actually measure
Module 1: Fundamentals
1.6 The most durable way to teach the device market is by:
Why
Specific models change quickly; categories and the questions they invite are stable.
Read again: Module 1, 1.3 Mapping the device market: category-based comparison
Module 1: Fundamentals
1.7 True/False: If two devices report different PlayerLoad for the same drill, one is simply wrong.
Why
Usually they use different proprietary derivation formulas, not right vs wrong.
Read again: Module 1, 1.2 Sensor types and what they actually measure
Module 1: Fundamentals
1.8 Before turning a device on, the Module-1 operational rule is to:
Why
Do not deploy a device without an informed-consent process in place.
Read again: Module 1, 1.5 First-look ethics and GDPR: the consent baseline
Module 2: Data tracking
2.1 External load is best defined as:
Why
External load is the prescribed dose (distance, sprints, reps); internal load is the response.
Read again: Module 2, 2.1 Internal load versus external load: the Impellizzeri-Marcora framework
Module 2: Data tracking
2.2 Session RPE (sRPE) is computed as:
Why
sRPE = RPE × session duration in minutes.
Read again: Module 2, Practical lab: Stat Pack #1
Module 2: Data tracking
2.3 For a 60-minute session at RPE 7, sRPE equals:
Why
7 × 60 = 420 arbitrary units.
Read again: Module 2, Practical lab: Stat Pack #1
Module 2: Data tracking
2.4 If acute load is 3,300 AU and chronic load is 2,400 AU, ACWR equals approximately:
Why
ACWR = acute / chronic = 3300 / 2400 = 1.375 (upper edge of the sweet spot).
Read again: Module 2, Practical lab: Stat Pack #1
Module 2: Data tracking
2.5 In real time (within seconds), a coach should most trust:
Why
Trust simple metrics live; trust composites at session debrief.
Read again: Module 2, 2.2 Live data feeds: what dashboards actually offer
Module 2: Data tracking
2.6 A drill shows a player covering 50 m in 10 minutes. The best action is to:
Why
Such a value is almost certainly indoor GPS dropout; treat the metric as missing.
Read again: Module 2, 2.4 Data quality, anomalies, and the discipline of scepticism
Module 2: Data tracking
2.7 True/False: A scatter plot showing correlation is sufficient to act on by itself.
Why
Correlation is not causation; never act on a scatter pattern alone.
Read again: Module 2, 2.3 Dashboard literacy: reading five visualisation families
Module 2: Data tracking
2.8 The best moment to give an athlete brief metric-anchored feedback is:
Why
During execution, metric talk distracts; feedback belongs between drills or at block end.
Read again: Module 2, 2.5 Real-time feedback to athletes: the timing question
Module 3: Performance
3.1 A meaningful athlete baseline is best described as:
Why
A baseline is a distribution: mean and standard deviation over several weeks.
Read again: Module 3, 3.1 Building an athlete baseline that is actually meaningful
Module 3: Performance
3.2 HRV interpretation must be rooted in:
Why
Between-athlete variability dwarfs within-athlete variability: compare to themselves.
Read again: Module 3, 3.1 Building an athlete baseline that is actually meaningful
Module 3: Performance
3.3 Minimum data collection before drawing baseline inferences is about:
Why
Collect data for at least 4-6 weeks before drawing inferences.
Read again: Module 3, 3.1 Building an athlete baseline that is actually meaningful
Module 3: Performance
3.4 Sleep-stage classification from consumer wearables should be treated as:
Why
Track total sleep time and efficiency; treat stage data as suggestive only.
Read again: Module 3, 3.3 Recovery metrics: what they show and what they do not
Module 3: Performance
3.5 The discipline of 'triangulation' means:
Why
Never act on one metric alone; combine HRV, sleep, wellness and performance signals.
Read again: Module 3, 3.3 Recovery metrics: what they show and what they do not
Module 3: Performance
3.6 During a well-executed taper you would expect HRV to:
Why
HRV rises as parasympathetic recovery exceeds training-induced sympathetic load.
Read again: Module 3, 3.4 Tapering with wearable confirmation
Module 3: Performance
3.7 True/False: Adding a new metric is worthwhile even if it will change no decision.
Why
If a metric changes no decision, it should not be in the system.
Read again: Module 3, 3.5 The measurement-burden trade-off
Module 3: Performance
3.8 A single day's HRV reading should:
Why
A single day should never trigger action; the 5-7 day trend is what matters.
Read again: Module 3, 3.3 Recovery metrics: what they show and what they do not
Module 4: Injury prevention
4.1 Which is a MODIFIABLE injury risk factor?
Why
Load progression is modifiable; age, injury history and anatomy are not.
Read again: Module 4, 4.1 Injury epidemiology and the role of monitoring
Module 4: Injury prevention
4.2 The often-cited ACWR 'sweet spot' is approximately:
Why
Gabbett proposed ~0.8-1.3 as the commonly cited sweet spot.
Read again: Module 4, 4.2 Load management and the injury-training paradox
Module 4: Injury prevention
4.3 The honest synthesis on ACWR for a practising coach is that it is:
Why
ACWR is a useful prompt; use it with history, wellness and training context.
Read again: Module 4, 4.2 Load management and the injury-training paradox
Module 4: Injury prevention
4.4 Training monotony is defined as:
Why
Monotony = mean daily load divided by the standard deviation of daily load (Foster, 1998).
Read again: Module 4, 4.2 Load management and the injury-training paradox
Module 4: Injury prevention
4.5 Jump-landing asymmetry is commonly flagged for follow-up above approximately:
Why
Asymmetries above ~10-15% are commonly flagged for follow-up.
Read again: Module 4, 4.3 Asymmetry and movement-quality red flags
Module 4: Injury prevention
4.6 A coach's role with asymmetry data is best described as:
Why
Coaches triage; refer for fuller assessment if asymmetry is large, persistent or symptomatic.
Read again: Module 4, 4.3 Asymmetry and movement-quality red flags
Module 4: Injury prevention
4.7 True/False: Wearable monitoring can eliminate injury risk.
Why
Monitoring reduces risk; it does not eliminate it.
Read again: Module 4, 4.1 Injury epidemiology and the role of monitoring
Module 4: Injury prevention
4.8 In wheelchair basketball (para-sport), the key load to monitor is:
Why
The propulsive joint is the playing joint; monitor stroke counts and shoulder load.
Read again: Module 4, 4.5 Five worked case studies
Module 5: Integration
5.1 PDCA stands for:
Why
Deming's PDCA = Plan, Do, Check, Act.
Read again: Module 5, 5.1 The Plan-Do-Check-Act integration framework
Module 5: Integration
5.2 The most important 'Act' option in PDCA is often to:
Why
Being willing to stop a low-value effort earns credibility for the next attempt.
Read again: Module 5, 5.1 The Plan-Do-Check-Act integration framework
Module 5: Integration
5.3 The most common special-category lawful basis in sport coaching is:
Why
Health data typically relies on explicit consent under Article 9(2)(a).
Read again: Module 5, 5.2 Data governance and GDPR in coaching practice
Module 5: Integration
5.4 Under GDPR, certain data breaches must be reported within:
Why
Article 33: certain breaches must be reported to the authority within 72 hours.
Read again: Module 5, 5.2 Data governance and GDPR in coaching practice
Module 5: Integration
5.5 'Your HRV says you're not ready' vs 'Your HRV suggests recovering, let's adjust' is best described as:
Why
Frame metrics as information, not verdicts; the framing changes the conversation.
Read again: Module 5, 5.3 Talking to athletes about their numbers
Module 5: Integration
5.6 A RACI matrix in a multi-disciplinary staff mainly prevents:
Why
RACI clarifies who owns each data domain, preventing conflicts and neglected data.
Read again: Module 5, 5.4 Integrating into a multi-disciplinary staff
Module 5: Integration
5.7 True/False: In PDCA, the 'Do' step should roll out across the whole organisation in week one.
Why
Implement at small scale first (one cohort, one season) before scaling.
Read again: Module 5, 5.1 The Plan-Do-Check-Act integration framework
Module 5: Integration
5.8 Limiting athlete-facing metrics for a perfectionism-prone adolescent addresses:
Why
Some athletes fixate on metrics unhelpfully; limit athlete-facing numbers where this risk exists.
Read again: Module 3, 3.5 The measurement-burden trade-off, Module 5, 5.3 Talking to athletes about their numbers

Knowledge quiz

1.1 Which of the following is a measured physical quantity, not a derived metric?

1.2 Under GDPR, an athlete's heart-rate data is most appropriately classified as:

1.3 A wearable that is accurate in the lab but unreliable during outdoor team training lacks:

1.4 The optical PPG heart-rate signal is MOST sensitive to:

1.5 GPS-derived metrics such as total distance are most robust:

1.6 The most durable way to teach the device market is by:

1.7 True/False: If two devices report different PlayerLoad for the same drill, one is simply wrong.

1.8 Before turning a device on, the Module-1 operational rule is to:

2.1 External load is best defined as:

2.2 Session RPE (sRPE) is computed as:

2.3 For a 60-minute session at RPE 7, sRPE equals:

2.4 If acute load is 3,300 AU and chronic load is 2,400 AU, ACWR equals approximately:

2.5 In real time (within seconds), a coach should most trust:

2.6 A drill shows a player covering 50 m in 10 minutes. The best action is to:

2.7 True/False: A scatter plot showing correlation is sufficient to act on by itself.

2.8 The best moment to give an athlete brief metric-anchored feedback is:

3.1 A meaningful athlete baseline is best described as:

3.2 HRV interpretation must be rooted in:

3.3 Minimum data collection before drawing baseline inferences is about:

3.4 Sleep-stage classification from consumer wearables should be treated as:

3.5 The discipline of 'triangulation' means:

3.6 During a well-executed taper you would expect HRV to:

3.7 True/False: Adding a new metric is worthwhile even if it will change no decision.

3.8 A single day's HRV reading should:

4.1 Which is a MODIFIABLE injury risk factor?

4.2 The often-cited ACWR 'sweet spot' is approximately:

4.3 The honest synthesis on ACWR for a practising coach is that it is:

4.4 Training monotony is defined as:

4.5 Jump-landing asymmetry is commonly flagged for follow-up above approximately:

4.6 A coach's role with asymmetry data is best described as:

4.7 True/False: Wearable monitoring can eliminate injury risk.

4.8 In wheelchair basketball (para-sport), the key load to monitor is:

5.1 PDCA stands for:

5.2 The most important 'Act' option in PDCA is often to:

5.3 The most common special-category lawful basis in sport coaching is:

5.4 Under GDPR, certain data breaches must be reported within:

5.5 'Your HRV says you're not ready' vs 'Your HRV suggests recovering, let's adjust' is best described as:

5.6 A RACI matrix in a multi-disciplinary staff mainly prevents:

5.7 True/False: In PDCA, the 'Do' step should roll out across the whole organisation in week one.

5.8 Limiting athlete-facing metrics for a perfectionism-prone adolescent addresses:

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