Student Capstone Project: Test a 'Smart Insole' vs Placebo and Document the Results

Hook: Turn your capstone into publishable research — without reinventing the wheel

Students, teachers and lifelong learners are tired of fragmented tutorials that never lead to real results. If you want a practical, ready-made capstone: design, run and publish a small placebo-controlled trial that compares a 3D-scanned ("smart") insole to a placebo insole. This guide gives you everything a student team needs in 2026 — study design, ethics templates, data collection instruments, basic analysis code and a reporting checklist so your capstone becomes a credible research output.

The evolution of insole testing and why this matters in 2026

By early 2026, wearable and wellness devices — from AI-personalized earbuds to 3D-scanned insoles — have seen heightened scrutiny. Journalism and consumer reporting in late 2025 and early 2026 highlighted how many direct-to-consumer "personalized" devices may perform no better than a placebo. Regulators and research funders now expect robust, transparent evidence even for small consumer trials. That creates a perfect educational opportunity: a capstone that teaches research methods, ethics and open reporting while producing a useful replication-style study on a high-interest product.

Why choose a 3D-scanned insole vs placebo for a capstone?

• High student engagement: everyone relates to foot comfort and wearable claims.
• Low technical barrier: interventions are non-invasive and safe; many outcome measures are simple (comfort, pain, step count).
• Teachable research scope: covers randomization, blinding, outcome measures, sample-size planning, and data-sharing.
• Real-world relevance: matches 2026 trends where consumer-grade personalization and placebo effects intersect.

Project overview — the quick roadmap (one page)

1. Define research question and outcomes.
2. Obtain ethics approval (or instructor sign-off for class projects).
3. Recruit participants and collect baseline data.
4. Randomize participants to 3D-scanned insole or placebo insole.
5. Implement blinding and run the intervention for a set period (e.g., 2 weeks).
6. Collect outcome data and adverse events.
7. Analyze data and write up results using the provided reporting template.
8. Publish results: class repository, OSF preprint or student journal.

Step 1 — Research question, design and outcomes

Pick a narrow, measurable question. Examples:

• "Does a 3D-scanned custom insole improve self-reported foot comfort more than a placebo insole after 14 days of wear?"
• "Does a 3D-scanned insole change step cadence or average daily steps compared to placebo over two weeks?"

Choose a design based on class size and resources:

• Parallel randomized trial — participants randomized to either intervention or placebo. Simpler logistics; needs more participants.
• Crossover randomized trial — each participant tries both insoles in random order with a washout period. Greater power with fewer participants, but watch for carryover effects.

Primary and secondary outcomes

• Primary: Self-reported comfort on a validated scale (e.g., 0–10 numeric rating scale) after 14 days.
• Secondary: Pain (if relevant), daily step count (smartphone or wrist tracker), plantar pressure summary from accessible pressure mats (if available), and adverse events.
• Exploratory: qualitative feedback, expectations survey (to estimate placebo response).

Step 2 — Sample size guidance for student projects

Student capstones usually tolerate modest sample sizes. Use conservative assumptions and document limitations. Here are practical targets:

• Parallel design: For a medium effect (Cohen's d = 0.5), alpha = 0.05 and power = 0.8 → roughly 64 participants total (32 per arm). That's a realistic semester target if recruiting across departments.
• Crossover/paired design: For the same effect size, you need about 34 participants total because paired designs reduce variability.
• If you cannot meet these numbers, treat the project as exploratory, pre-register hypotheses, and focus on estimation (effect sizes and CIs) rather than hypothesis testing.

Tip: use free power calculators (G*Power, OpenEpi) and report the calculation in your methods.

Step 3 — Ethics, consent and regulation (practical templates)

Even minimal-risk student trials need ethical oversight. In 2026, institutions and publishers expect clear documentation of participant protection and data privacy — especially with consumer health claims. Follow these steps:

1. Submit a short protocol to your institutional review board (IRB) or ethics committee. Many schools have expedited review for low-risk student research.
2. Include a plain-language consent form (template below) and an information sheet about data privacy (GDPR and local laws).
3. Document conflict-of-interest: if a company donates insoles, state it and avoid company influence on data or analysis.

Consent form template

Title: Insole Comfort Study (Student Capstone)
Principal Investigator: [Name, contact]
Purpose: To compare a 3D-scanned insole to a placebo insole for 14 days of wear.
Procedures: You will be randomized to wear one insole for 14 days. You will fill daily comfort logs and one final survey.
Risks: Minimal — possible minor irritation or discomfort. You may stop anytime.
Benefits: May or may not improve comfort. No payment; course credit / small gift card.
Data Privacy: Data de-identified and stored on the university server. Results published in summary form.
Contact: [Ethics office contact]
Consent: I have read this form and agree to participate. [Signature / digital consent]

Ethics checklist (quick)

• Is the risk minimal and non-invasive? Yes → expedited review suitable.
• Is the consent understandable (Flesch reading ease)?
• Is data de-identified and stored securely?
• Any commercial ties declared and managed?
• Clear stop rules for adverse events documented?

Step 4 — Randomization and blinding

Robust randomization and blinding minimize bias.

• Randomization: Use simple computer-generated sequences (e.g., random.org or a basic script). For small student projects, block randomization (blocks of 4 or 6) keeps arm sizes balanced.
• Blinding: Participants should not know whether their insole is the 3D-scanned "smart" insole or the placebo. Use identical-looking foils or coverings. The assessor who collects outcome surveys should be blinded when possible.
• Expectation check: Before the intervention, measure participants' belief about which insole they expect to be better. That helps you quantify placebo effects.

Step 5 — Data collection tools and measures

Prioritize simple, validated measures and reproducible data capture:

• Comfort scale: 0–10 numeric rating scale each evening.
• Pain scale: 0–10 if relevant.
• Daily activity: smartphone step counts (Apple Health/Google Fit) or wrist device export. Ask participants to export weekly and submit CSVs.
• Qualitative feedback: short open-ended questions about fit, perceived changes.
• Adverse events: daily checkbox for blisters, increased pain, or need to stop use.

Practical data collection form (CSV schema)

participant_id,arm,day,date,comfort, pain, steps, adverse_event, notes
P001,intervention,1,2026-03-01,7,1,6342,none,started fine

Step 6 — Analysis: simple, reproducible and teachable

Plan your analysis before collecting data. Use estimation and confidence intervals. Here are suggested steps:

1. Describe baseline characteristics by arm.
2. For the primary outcome (comfort at day 14): compute mean and 95% CI for each arm; estimate difference and CI.
3. Secondary analyses: repeated measures (daily comfort) using a mixed-effects model if you have time; otherwise compare averages per participant.
4. Report effect sizes (Cohen's d) and exact p-values, but emphasize estimates and uncertainty.

Minimal R and Python snippets

R (t-test for parallel design):

# R snippet
library(tidyverse)
data <- read_csv('data.csv')
end <- data %>% filter(day==14) %>% select(participant_id, arm, comfort)
t.test(comfort ~ arm, data=end, var.equal=FALSE)

Python (paired test for crossover):

# Python snippet
import pandas as pd
from scipy import stats
end = pd.read_csv('paired_end.csv')
# columns: participant, comfort_intervention, comfort_placebo
res = stats.ttest_rel(end['comfort_intervention'], end['comfort_placebo'])
print(res)

Step 7 — Reporting: template and transparency

Adopt a transparent reporting framework. For a student capstone, use a simplified CONSORT-inspired checklist so readers can assess bias and quality.

Simple reporting template (use for internal report and preprint)

Title: [Your title]
Abstract:
- Background
- Methods (design, n, randomization, blinding)
- Results (effect estimates and CI)
- Conclusion

Introduction: Brief literature and why the question matters (cite 2025-26 coverage on placebo tech)
Methods:
- Design: parallel / crossover
- Participants: inclusion/exclusion
- Intervention: describe 3D-scanned insole and placebo
- Outcomes and sample size calculation
- Randomization and blinding
- Ethics: approval reference

Results:
- Flow diagram (recruited, randomized, analyzed)
- Table 1: baseline
- Primary outcome: estimates
- Secondary outcomes and adverse events

Discussion:
- Interpret effect size, limitations (small sample, short duration)
- Implications: for consumers, for future research
- Data availability: link to anonymized dataset and code (OSF / GitHub)

Reporting checklist (student-friendly)

• Pre-register study (e.g., OSF) or state pre-specified analysis plan.
• Describe randomization and blinding details.
• Provide sample size rationale.
• Share de-identified data and code where possible.
• Declare conflicts of interest and funding.

Step 8 — Publishing and dissemination in 2026

Small student trials can still make impact if transparent:

• Upload a preprint or posting to OSF with open data and code.
• Submit to undergraduate research journals or student open-access journals; many accept short methods-focused reports.
• Prepare a short poster and a 3–5 minute video summarizing methods and findings — perfect for departmental symposia or online sharing.
• Consider a reproducibility thread on academic social platforms and clearly tag it as a student project.

Case study (hypothetical) — How a 34-student crossover found no meaningful effect

This is a modeled example to help you interpret results and write the discussion.

• Design: crossover, n = 34, 7-day wear per condition, 7-day washout.
• Primary outcome: day-7 comfort mean difference = 0.4 (95% CI -0.3 to 1.1). Cohen's d = 0.15.
• Secondary: no difference in daily steps. Expectation survey predicted about 30% of the variance in self-reported comfort (placebo component).

Interpretation: effect size small and CI includes clinically negligible differences. Transparent reporting and sharing of the data allowed other students to meta-analyze results across semesters.

Key takeaway: A well-run small student trial can be educational and contribute to the evidence base — but avoid overclaiming efficacy from underpowered estimates.

Advanced strategies and 2026 trends to include

Make your capstone current by integrating 2026 developments:

• AI-assisted analysis: Use simple, explainable machine learning for exploratory analyses (e.g., decision trees) but always report traditional estimates first.
• Mobile sensing: Leverage smartphone APIs (2024–26 improvements) for passive step counts and cadence metrics; document API versions for reproducibility.
• Pre-registered adversarial checks: In 2026, reviewers expect teams to run sensitivity analyses that test the robustness of conclusions to missing data and expectation bias.
• Data privacy best practices: Follow GDPR-like principles when storing participant data. Anonymize geolocation and device identifiers before sharing.

Common pitfalls and how to avoid them

• Underpowered studies: If you can't recruit target n, shift the study aim to estimation and document that clearly.
• Poor blinding: Small visual differences in insoles break blinding. Use covers and standard packaging.
• Outcome drift: Stick to pre-specified primary outcomes to avoid data dredging.
• Industry influence: If insoles are donated, ensure the company has no data access or influence on analysis.

Ready-to-use checklists and downloadable templates (paste into your project repo)

STUDY_CHECKLIST.md
- [ ] Pre-register (OSF)
- [ ] Ethics approval / instructor sign-off
- [ ] Consent form completed for all participants
- [ ] Randomization code and seed saved
- [ ] Blinding materials prepared
- [ ] Data collection form (CSV schema) saved
- [ ] Analysis script saved
- [ ] De-identified data uploaded to OSF
- [ ] Report drafted using template

Final teaching notes for instructors

Turn this into a courselet: split the capstone into 6 weekly modules — design & ethics, recruitment & randomization, data collection, interim checks, analysis, and reporting & publish. Grade on methodology, transparency and documentation rather than on "positive" results. Encourage replication and cross-institution collaboration: multiple small trials pooled via meta-analysis are more informative than isolated positive claims.

Actionable takeaways

• Pick a simple primary outcome (comfort numeric rating) and pre-register it.
• Prefer crossover if you have limited recruitment capacity — it needs fewer participants.
• Document everything: randomization seed, consent forms, blinding method, and analysis scripts.
• Share data and code: publish anonymized data and scripts on OSF or GitHub for transparency and teaching reuse.

Where to go next (resources)

• G*Power or OpenEpi for power calculations.
• OSF (Open Science Framework) for pre-registration and data sharing.
• Your institution's IRB/ethics office for template review.
• Simple R/Python tutorials for analysis; sample scripts provided above.

Call to action

If you’re ready to build this capstone: download the consent and reporting templates, pre-register your study on OSF, and gather your team. Start with a one-page protocol this week — then post it to your class repo. If you'd like, copy the templates in this guide and adapt them for your course. Share your results openly: even null findings teach students and help the public separate placebo from product claims in 2026.

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