Process Evaluation of HIIT: Qualitative Playbook

Evidano is an AI-powered qualitative data analysis platform that ingests logbooks, HR/RPE CSVs, and transcripts and supports transcription, coding, and reproducible analyses. The PRO‑HIIT process evaluation (Published 30 June 2026) explains how a 12-week school HIIT programme was delivered, adapted and experienced by 369 Year‑7 students (March–June 2024). If you run process evaluations or mixed‑methods studies, this post shows practical ways to convert training logbooks, HR/RPE data, and small focus‑group transcripts into reproducible thematic and cross‑segment findings using Evidano (Evidano). We use concrete numbers from the PLOS One paper to illustrate where AI reduces manual drag and increases transparency for stakeholders: average sessions delivered ≈26.5, mean HR 146 bpm (71% HRmax), peak HR 175 bpm (85% HRmax), RPE mean 5 (range 3–8). Source: PLOS One.

Key Takeaways

PRO‑HIIT delivered fewer HIIT sessions than planned but achieved very high retention and sampled sessions reached moderate-to-high intensity.

PRO‑HIIT reached n = 369 Year‑7 students with T2 retention = 100% and T3 retention = 96%, despite lower delivered sessions per class (26, 25, 24, 31 vs planned 60).
Intensity sampling showed mean session HR = 146 bpm (71% HRmax) and peak HR = 175 bpm (85% HRmax), with sessional RPE mean = 5 (range 3–8).
Main barriers were competing priorities, weather, repetition and measurement burden, facilitators were peer coaches, space efficiency and teacher participation, with no injuries reported.
Mixed‑methods process data explained implementation deviations: systematic capture of adaptations, timestamps and sampling frames is essential for interpretation.

Fast take: PRO‑HIIT process evaluation (June 30, 2026)

This fast take summarises the PRO‑HIIT process evaluation findings published 30 June 2026 and the data sources used.

The PRO‑HIIT study embedded 6–10 minute HIIT at the start of PE/PA lessons for 12 weeks in a Chinese secondary school. The trial reported strong reach and retention (n=369; T2 retention 100%, T3 retention 96%), but delivered fewer sessions than planned (class-level delivered sessions: 26, 25, 24, 31 vs planned 60). Implementation fidelity was partially measured: mean session HR=146 bpm (71% HRmax), peak HR=175 bpm (85% HRmax), sessional RPE mean=5 (3–8).

Qualitative inputs: 2 student focus groups (n=9 students), teacher focus group; transcripts translated and coded in NVivo.
Quantitative inputs: teacher training logbooks, HR subsample (127 measures across 13 sessions), RPE (225 measures across 5 sessions).
Main barriers: competing priorities, weather, repetition, measuring RPE; facilitators: peer coaches, space efficiency, teacher participation; no injuries reported.

Findings snapshot (key numbers & dates)

Item	Value	Note / Source
Publication date	30 June 2026	PLOS One
Registered / protocol	NCT06374732; protocol published	Registration 18/04/2024
Sample	n = 369 Year‑7 students (12–13 yrs)	Eight classes, 3 PE teachers
Planned sessions per class	60 sessions (5/week × 12 weeks)	Protocol target
Delivered (class-level)	26, 25, 24, 31 (avg ≈26.5)	Teacher logbooks
Retention	T2 = 100%; T3 = 96%	15 lost at T3
Intensity (mean HR)	146 bpm (71% HRmax)	127 HR measures across 13 sessions
Intensity (peak HR)	175 bpm (85% HRmax)	79% of peaks met HIIT threshold
Session RPE	Mean = 5 (range 3–8)	225 RPE measures

What happened, methods & data pipeline (plain English)

The PRO‑HIIT process evaluation used the UK MRC framework and a convergent parallel mixed‑methods design to explain how PRO‑HIIT was implemented and why results occurred.

Three domains (implementation, mechanisms of impact, context) were operationalised as 12 measures captured via teacher logbooks, HR/RPE sampling, and semi‑structured focus groups.

Logbooks recorded session count, adaptations, attendance and teacher quality scores.
Intensity monitoring was limited: HR from 10 participants per monitored session and RPE in selected PE classes only (resource constraint).
Qualitative recordings were transcribed in Chinese, translated, and coded deductively against MRC constructs then inductively for emergent themes; NVivo and SPSS were used for analysis.
Limitations note: non‑blinding of teachers/students, limited intensity sampling, small qualitative sample (9 students, 2–3 teachers).

Implications for qualitative researchers & evaluators

So what for mixed‑methods teams?

Mixed‑methods teams should use process data to explain deviations: PRO‑HIIT’s lower dose delivered was driven by predictable school calendar disruptions, weather, and staffing.

The PRO‑HIIT evaluation shows that teams should capture these reasons in timestamps and structured reason codes to preserve interpretability.

Small qualitative samples can still reveal mechanistic signals, treat small sample findings as exploratory inputs to triangulate with attendance and intensity metrics.

Analytic risks to watch

Analytic teams must account for partial measurement, which can introduce bias when intensity is sampled from a subsample only.

Partial measurement (e.g., HR in a subsample) creates potential bias, report sampling frames and integrate uncertainty in interpretation.

Social desirability can affect teacher‑rated session quality and attendance, use unobtrusive measures where possible (for example, automated accelerometer logs).

Design decisions worth documenting

Teams should record adaptations systematically, noting who changed what, when, and why, because PRO‑HIIT shortened sessions to 6–8 minutes and altered work: rest ratios.

Capture contamination and blinding status: PRO‑HIIT teachers were not blinded, which likely influenced differences in intensity.

Do more, faster with Evidano, map PRO‑HIIT data to AI workflows

Ingest & harmonise: logbooks, HR CSVs, transcripts

Evidano ingests teacher logbooks and HR/RPE spreadsheets so teams can join time‑stamped delivery records with intensity measures and attendance.

Import teacher logbooks and HR/RPE spreadsheets into Evidano to join time‑stamped delivery records with intensity measures and attendance.

Upload focus‑group audio and use Evidano to transcribe with a custom dictionary (for example, 'HIIT', 'SAAFE', local PE terms) and apply automated PII redaction where required.

Thematic + frequency + cross‑segment analysis

Evidano supports AI‑assisted thematic coding that combines deductive MRC codes with inductive subthemes to speed qualitative analysis.

Run AI‑assisted thematic coding on transcripts (deductive MRC codes → inductive subthemes). Evidano generates theme frequencies and co‑occurrence networks to show which barriers co‑occur with low dose.

Compare segments (by class, by week, by peer‑coach vs teacher‑led) with cross‑segment analysis to spot where dose delivered and dose received diverge.

Visualise and hand off

Evidano produces exportable visuals, such as word clouds and co‑occurrence networks, for stakeholder briefs and infographics.

Produce exportable visuals (word clouds, co‑occurrence networks, hierarchical code → subcode trees) for stakeholder briefs and infographics.

Use AI chat over your imported documents to write an evidence‑backed implementation brief or to extract verbatim quotes tied to themes for reports.

Security & reproducibility

Evidano encrypts data end‑to‑end and does not train third‑party models on your corpus, which supports handling school data and parental consents.

Codebooks, versions and analytic decisions are saved in the project history for auditability and replication.

Checklist: reproduce PRO‑HIIT style process analysis in 7 steps

This checklist lists seven practical steps to reproduce a PRO‑HIIT style process analysis using the same inputs and AI workflows.

Step 1: Gather inputs, teacher logbooks (CSV), HR/RPE data (CSV), focus group audio/video, consent forms.

Step 2: Upload to Evidano, enable transcription with a custom dictionary (local terms) and PII redaction.
Step 3: Auto‑code with an MRC‑guided codebook (import your deductive codes), then run AI‑assisted inductive coding for emergent themes.
Step 4: Run frequency and cross‑segment analyses (by class, week, peer‑coach vs teacher‑led) to quantify where fidelity and dose diverge.
Step 5: Generate co‑occurrence networks and hierarchical code trees to visualise barriers vs facilitators.
Step 6: Use AI chat to draft an implementation brief with verbatim quotes linked to timestamps for easy stakeholder review.
Step 7: Export reproducible outputs (figures, CSVs, codebook) and archive the analytic history.

FAQ: Process evaluation of HIIT

What was the PRO‑HIIT programme and who participated?

PRO‑HIIT was a 12-week school HIIT programme delivered to Year‑7 students in a Chinese secondary school.

The PRO‑HIIT study embedded 6–10 minute HIIT at the start of PE/PA lessons for 12 weeks and reached n = 369 Year‑7 students (12–13 yrs) across eight classes taught by three PE teachers.

What were the main implementation findings?

The main implementation finding was that fewer sessions were delivered than planned despite high retention.

Planned sessions per class were 60 (5/week × 12 weeks) but delivered class‑level sessions were 26, 25, 24, 31 (avg ≈26.5); retention remained high with T2 = 100% and T3 = 96%.

How was intensity measured and what did it show?

Intensity was measured with heart rate sampling and sessional RPE, showing moderate-to-high intensity in sampled sessions.

Intensity monitoring included 127 HR measures across 13 sessions with mean HR = 146 bpm (71% HRmax) and peak HR = 175 bpm (85% HRmax); session RPE mean = 5 (range 3–8).

What qualitative methods were used and what did they reveal?

The qualitative component used small focus groups and deductive then inductive coding to identify barriers and facilitators.

Two student focus groups (n=9 students) and a teacher focus group were transcribed in Chinese, translated, and coded in NVivo; main barriers included competing priorities and repetition, facilitators included peer coaches and teacher participation.

How can researchers reproduce PRO‑HIIT style analyses?

Researchers can reproduce PRO‑HIIT style analyses by systematically combining logbooks, HR/RPE data and transcripts and using reproducible AI workflows for coding and cross‑segment analysis.

Follow the seven-step checklist: gather inputs, upload and transcribe, auto‑code with an MRC codebook, run frequency and cross‑segment analyses, visualise co‑occurrence networks, draft an implementation brief, and export reproducible outputs.

Topics

process evaluation HIIT
PRO-HIIT study
mixed-methods process evaluation
school HIIT implementation
qualitative data analysis

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Process Evaluation of HIIT: Qualitative Playbook

Key Takeaways

Fast take: PRO‑HIIT process evaluation (June 30, 2026)

Findings snapshot (key numbers & dates)

What happened, methods & data pipeline (plain English)

Implications for qualitative researchers & evaluators

So what for mixed‑methods teams?

Analytic risks to watch

Design decisions worth documenting

Do more, faster with Evidano, map PRO‑HIIT data to AI workflows

Ingest & harmonise: logbooks, HR CSVs, transcripts

Thematic + frequency + cross‑segment analysis

Visualise and hand off

Security & reproducibility

Checklist: reproduce PRO‑HIIT style process analysis in 7 steps

Conclusion, next steps

FAQ: Process evaluation of HIIT

What was the PRO‑HIIT programme and who participated?

What were the main implementation findings?

How was intensity measured and what did it show?

What qualitative methods were used and what did they reveal?

How can researchers reproduce PRO‑HIIT style analyses?

Topics

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