WEEK 3 INTERPRETATION, NORMATIVE DATA, FEEDBACK & PHYSICAL INACTIVITY

Interpretation of Test Results and Data Quality

• Assess every test for:
  • Validity & Reliability
  • Did the chosen protocol truly measure what it claimed?
  • Were procedures followed exactly as prescribed?
  • Meaningfulness of Results
  • Does the data answer the referral or training question?
  • Protocol Fidelity
  • Standardised warm-up, calibration, environmental control, equipment maintenance.
  • Accuracy of Data
  • Check for recording errors, instrumentation drift, participant effort.
• Compare every score with appropriate reference values:
  • \text{Normative\/Percentile Tables} – percentile ranks 10–90 %.
  • $\text{Criterion-Referenced Values}$ – pass/fail or health standard cut-points.
• Identify and report red flags (e.g.
  • Abnormal haemodynamic responses.
  • Pain, dizziness, excessive fatigue) → immediate referral.
• Prepare thoroughly before delivering feedback.

Normative Tables & Classification

Percentile-Rank Example – Peak Power (Wingate-style test)

• A female score of $480\,\text{W}$ = 60th %ile → better than 60 % of females → top 40 % of population.

VO₂max Norms for Men (mL·kg⁻¹·min⁻¹)

Providing Client Feedback

1. Explain the Test
   • Purpose, targeted fitness component, typical error.
2. State the Results
   • Quantitative (numbers, ranks) and qualitative meaning (What does it indicate about health/performance?).
3. Next Steps
   • Need for improvement? Strategies? Timeline? Referral?
4. If you cannot explain clearly, better not to test at all.

Facilitating Discussion with Clients

• Encourage two-way dialogue; schedule time for questions.
• Explore emotional responses; show empathy.
• Not every result is “excellent” – help client process disappointment constructively.
• Unknown answer? Say “I’ll look into that and get back to you.”

Physical Activity: Evolutionary Context

• Human genome shaped by hunter-gatherer life requiring high PA.
• “Thrifty genes” (Neel 1962) optimise energy storage during feast–famine cycles.
• Modern sedentary lifestyle creates mismatch → metabolic disease.

Measuring Population Health

• Data sources: WHO, IPAQ, ABS National Health Survey, AIHW datasets.
• Purpose: inform policy, allocate resources, monitor trends.

Definitions

• $\text{Inactivity}$: < 150 min moderate PA/week (i.e., < 30 min most days).
• Morbidity, Mortality, Disease, Chronic Disease (> 3 months), Non-Communicable Disease (NCD).

Global & Australian Inactivity Statistics

• $\frac{1}{3}$ of adults (≈ 1.8 billion) globally inactive; ↑ 5 % (2010→2022).
• WHO goal: 15 % relative reduction by 2030; interim 10 % by 2025.
• Age–sex pattern (Australia 2022):
  • 18-64 y: 37 % not meeting PA guideline (↓ from 51 % in 2017-18).
  • ≥ 65 y: 57 % inactive.
  • Youth 15-17 y: 83 % inactive (↓ from 89 %).
• Strength training (2014-15): only 25–35 % of men & 15–25 % of women met ≥ 2 days/week.
• Rural/remote adults less likely to meet aerobic guideline.

Children 5-17 y (2011-12)

• < 45 % met PA guideline; compliance declines with age.
• Only a minority met both PA and screen-time limits.
• Boys > Girls for PA; cities < regional in compliance.

Major Australian Policy Documents

• National Preventive Health Strategy 2021-2030.
• National Obesity Strategy 2022-2032.

Disease & Economic Burden of Inactivity

• Chronic inactivity = “physiologically abnormal” (Booth 2000).
• DALY framework: $DALY = YLL + YLD$ (years lost + years lived with disability).
• Inactivity = 9th leading preventable cause, $2.5\%$ of total disease burden.
  • Shares of inactivity-attributable burden:
  • $20\%$ Type 2 diabetes
  • $16\%$ Coronary heart disease
  • $16\%$ Uterine cancer
  • $12\%$ Bowel cancer
  • $12\%$ Dementia
  • $9.2\%$ Stroke
  • $3.2\%$ Breast cancer
• Contributes to $\sim5.2\%$ of Australian deaths (AIHW 2021).
• Global economic cost (2013): $53.8\,\text{billion}\,(INT\$)$.

Barriers to Physical Activity

• Environmental: urbanisation, traffic, pollution, lack of parks, violence.
• Personal: time, competing duties, motivation, cost, health issues.
• Youth-specific (13-17 y): dislike of activity (≈30 %), lack of time, laziness.

Social Determinants of Health & PA

• WHO: SDH ≈ 30–55 % of health outcomes.
• Key domains: income, education, job security, housing, food, inclusion, early childhood, health-care access.
• Determinants specific to PA: urban design, cultural norms, social support, facility access, individual motivation & self-efficacy.
• Populations at Risk: low SES, chronic illness, older adults, rural/remote residents.

Public Health & Community Strategies

• Global Action Plan on PA 2018-2030 – “More Active People for a Healthier World”.
• Evidence-based interventions:
  • Community campaigns (e.g., “Find Thirty Every Day”).
  • Point-of-decision prompts (stairs vs elevator).
  • Active transport initiatives (Ride2Work Day).
  • Local events: City to Surf, HBF Run for a Reason.
  • School programs: Jump Rope for Heart.
  • Behaviour-change media: “Swap It, Don’t Stop It”, “Get Moving”.
  • Mental-well-being synergy: Act-Belong-Commit.

Practical Implications for Exercise Professionals

• Testing is only valuable if results are interpreted, contextualised and translated into action.
• Use normative data judiciously; personalise cut-points based on risk stratification.
• Address social determinants and individual barriers when prescribing PA.
• Advocate for supportive environments and policy change.
• Maintain competence: ACSM Guidelines 10th ed., ESSA Student Manual (Coombes & Skinner 2014).