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Drop it straight into the free SEHS IA frame. The full Research Design section is free; unlock the paid Data analysis, Conclusion & Evaluation — mean ± SD, error bars and a valid statistical test — to take it to the top band.
Start this IA in the SEHS frame →CARDIOVASCULAR & RESPIRATORY
Heart-rate and breathing studies are reliable, data-rich and feasible with a monitor and a step — the SEHS classics.
1 · How does stepping rate affect recovery heart rate in 16–18-year-old participants?
The model SEHS investigation: a clean dose–response, a clear physiological mechanism (oxygen demand → cardiac output → HR), and a one-way ANOVA across the four rates that reaches the top of Data Analysis.
2 · How does recovery position (standing vs seated vs supine) affect heart-rate recovery after submaximal exercise?
Links posture to venous return and parasympathetic reactivation — a genuine mechanism to explain, with a tidy three-level comparison for an ANOVA.
3 · How does exercise intensity affect breathing rate and rate of perceived exertion (RPE)?
Two measures from one protocol let you correlate an objective and a subjective variable — a Pearson correlation gives sophisticated analysis beyond a simple comparison.
ENERGY SYSTEMS & EXERCISE PHYSIOLOGY
Studies of fatigue, warm-up and intensity link the energy systems to a measurable performance drop-off.
4 · How does warm-up duration affect time to exhaustion in a submaximal step test?
A clear physiological story (raised muscle temperature and oxygen kinetics) with a measurable performance outcome — and an honest evaluation about order/fatigue carry-over.
5 · How does recovery interval length affect repeated sprint or step performance?
Connects PCr resynthesis to a measurable fatigue index — repeated-measures data with a clear, explainable trend across the three intervals.
6 · How does step height affect heart rate and RPE at a fixed stepping rate?
Holding rate constant while varying height isolates workload (force × distance) cleanly — a controlled way to vary intensity and a strong controlled-variable discussion.
7 · How does active versus passive recovery affect blood-lactate clearance proxy (heart-rate recovery)?
Uses HR recovery as a school-feasible proxy for clearance — a paired t-test on the same participants under both conditions controls individual variation.
BIOMECHANICS & MOVEMENT
Force, angle and range-of-motion studies give measurable, repeatable data and link directly to mechanics.
8 · How does grip width affect peak handgrip force and time to fatigue?
A clean length–tension relationship from cheap apparatus — precise force data with small uncertainties and a clear optimum to explain.
9 · How does knee/squat depth affect vertical jump height?
Links the stretch–shortening cycle and joint angle to a measurable output — a tidy three-level ANOVA with an easily quantified DV.
10 · How does run-up distance affect throwing or kicking velocity?
Free video analysis turns a sports skill into quantitative biomechanics — momentum transfer gives a strong mechanism for the conclusion.
11 · How does stride length affect running economy (heart rate at a fixed pace)?
A neat economy study: deviating from the preferred stride should raise HR, giving a U-shaped trend that demonstrates an optimum — sophisticated to analyse and explain.
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Open the SEHS IA frame →SKILL ACQUISITION & REACTION
Reaction time, accuracy and feedback studies are highly repeatable and produce large, clean datasets.
12 · How does visual versus auditory stimulus affect simple reaction time?
Lots of trials per participant gives a rich dataset and a paired t-test; the modality difference has a clear neural explanation for the conclusion.
13 · How does exercise intensity affect reaction time (arousal and performance)?
Tests the inverted-U arousal hypothesis with a measurable outcome — a three-level study that can reveal an optimum rather than a simple line.
14 · How does distributed versus massed practice affect motor-skill accuracy?
A genuine skill-acquisition design with a measurable accuracy DV — an unpaired t-test on two matched groups and a clear learning-theory link.
15 · How does feedback type (knowledge of results vs none) affect rate of skill improvement?
Tracks a learning curve rather than a single score — gradient of improvement is a richer DV that lifts the analysis above a one-shot comparison.
16 · How does a dual task (cognitive load) affect balance time?
A clean attention/automaticity study with a simple measurable DV — paired data controls individual differences in balance ability.
NUTRITION, HYDRATION & RECOVERY
Intake and recovery studies have a strong real-world hook — keep them low-risk, with food-based, sub-maximal protocols.
17 · How does carbohydrate intake before exercise affect recovery heart rate and RPE?
An everyday nutrition question with a measurable physiological response and a paired design — emphasise the ethics of ordinary, well-tolerated foods.
18 · How does hydration status affect heart rate and perceived exertion during submaximal exercise?
A topical, examinable relationship — design it as compare-with-vs-without-water rather than dehydrating anyone, which keeps it ethical and school-feasible.
19 · How does caffeine intake affect reaction time and resting heart rate?
Clear stimulant pharmacology with measurable effects — restrict to a single ordinary dose and exclude anyone caffeine-sensitive in the consent and PAR-Q.
20 · How does sleep duration (self-reported) relate to reaction time and resting heart rate?
A correlational design needing no intervention at all — entirely ethics-light, with a Pearson r and an honest evaluation of self-report limitations.
21 · How does a cool-down (active vs none) affect next-day perceived muscle soreness?
A recovery question with a simple measurable DV — use a rating scale and a fixed sub-maximal bout to keep risk and soreness low.
22 · How does music tempo affect treadmill/step cadence and RPE?
A motivation/psychophysics angle with measurable cadence — a three-level repeated-measures study with a clear, engaging hypothesis.
23 · How does body position affect resting blood pressure?
A baroreflex study with precise, repeatable readings and a clear mechanism — no exertion needed, so it is low-risk and accessible.
24 · How does training status relate to heart-rate recovery after a standard step test?
Connects fitness to autonomic recovery using a single standardised test — a correlational design with a strong physiological mechanism and easy participant standardisation.
From a topic to a top-band IA
An idea is the easy part — the marks are in how you build it. The SEHS IA is scored out of 24 across four equal criteria: Research Design, Data Analysis, Conclusion and Evaluation. Whichever topic you pick, the same moves win: a focused, physiologically grounded research question with named variables and a testable hypothesis (H₀ / H₁); well-sampled, standardised participants screened with a PAR-Q and consented; data processed as mean ± standard deviation with error bars and a valid statistical test; a conclusion justified against the physiology and literature; and an evaluation that weighs your errors (individual variability, control of confounds) and proposes realistic improvements and extensions.
Build your chosen idea into a full IA
The examiner-written SEHS IA writing frame takes you through every section with the rubric, worked examples and the traps that cost marks. Research Design is free — unlock Data analysis, Conclusion & Evaluation to finish the whole investigation and export it to Word or PDF.
Open the SEHS IA frame →SEHS IA ideas — FAQ
What makes a good IB SEHS IA topic?
A clear physiological or biomechanical relationship at its heart, a named independent variable (with range and units), and a measurable physiological dependent variable such as recovery heart rate, RPE or reaction time (with the instrument and units). It must control the obvious participant variables, be carried out ethically with informed consent and PAR-Q screening, and be feasible with school equipment. Phrase it as "How does … affect …?".
How do I handle participant variability and sample size?
Individual physiology varies, so use enough participants (around 10–15) and enough repeats per condition that you can report a mean ± standard deviation, plot error bars and run a valid statistical test. A within-participant (repeated-measures) design — testing the same participants under every condition — controls between-person variation and is usually the most powerful choice for a school study. Standardise on age, fitness, warm-up and prior food or caffeine.
What ethics and consent does a SEHS IA need?
Obtain written informed consent after explaining the procedure, risks and purpose, plus parental or guardian consent for under-18s. Screen every participant with a PAR-Q and exclude anyone flagged. Allow withdrawal at any time, anonymise data by code (P1, P2 …), keep protocols sub-maximal and low-risk, and follow the IB ethical guidelines for human participants with your supervisor's approval.
How do I turn the idea into a top-band IA?
Build it section by section in the free SEHS IA writing frame — research question and hypothesis, variables, sampling and standardisation, method, data processed with SD and error bars, a valid statistical test, a conclusion against the physiology, and an evaluation with realistic improvements and extensions.
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