AI Qualitative Analysis for Spine Surgery Interviews

AI qualitative analysis spine surgery is a practical search term for researchers who want automated synthesis of interview-based evidence about physical activity barriers and facilitators in surgical populations. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to PLOS One (published August 4, 2026), the qualitative study of patients awaiting lumbar spine surgery interviewed 18 patients and identified five key behavioural domains shaping activity: skills, beliefs about capabilities, beliefs about consequences, environmental context and resources, and emotion (PLOS One). The PLOS One team recruited patients between 21 August 2024 and 12 February 2025 and reported a mean participant age of 54.7 years (SD 17.3) in the sample of 18 (11 with lumbar stenosis, 7 with disc herniation) (PLOS One). This post explains how AI-enabled qualitative research methods can reproduce, scale, and extend the PLOS One findings for clinical teams and health services researchers.

Key Takeaways

According to the PLOS One study (published August 4, 2026), 18 interviews conducted between 21 August 2024 and 12 February 2025 revealed five key Theoretical Domains Framework areas that most strongly shape physical activity in patients awaiting lumbar spine surgery (PLOS One).

18 interviews were completed with a mean age of 54.7 years (SD 17.3) in the sample, with 11 patients having lumbar spinal stenosis and 7 with lumbar disc herniation (PLOS One, published August 4, 2026).
PLOS One (published August 4, 2026) reports that only 17% of patients meet WHO-recommended physical activity levels while waiting for lumbar surgery, highlighting a large preoperative inactivity gap.
PLOS One (recruitment 21 August 2024 to 12 February 2025) found barriers such as pain, fear of harm, limited access to resources, and negative emotions, and facilitators such as guidance, habit, social support, and safe environments.
Direct patient language in PLOS One illustrates lived experience: “I can’t trust my legs and hips to do anything that is [a] big exertion” (Patient 4, quoted in PLOS One) and “I wouldn’t really know where to start in a gym” (Patient 1, quoted in PLOS One).

What happened and how the PLOS One study measured it

Answer: The PLOS One team used data-prompted semi-structured interviews mapped to the Theoretical Domains Framework to identify barriers and facilitators to general physical activity in preoperative spinal patients.

According to PLOS One (published August 4, 2026), researchers recruited 18 adults from the Canadian Spine Outcomes and Research Network registry between 21 August 2024 and 12 February 2025 and conducted 18 interviews lasting 18–48 minutes with a mean of 35.5 minutes per interview (PLOS One).

According to PLOS One (published August 4, 2026), the interviews combined each patient’s self-reported Godin-Shephard Leisure-Time Physical Activity scores with open prompts, the transcripts were manually coded to 12 TDF domains, and five domains were prioritised by researcher consensus based on frequency and clarity of beliefs (PLOS One).

According to PLOS One (published August 4, 2026), the study used a hybrid deductive/inductive analytic approach, manual line-by-line coding, field notes for context, and consensus meetings that included a patient representative to finalize subthemes (PLOS One).

Findings snapshot

Date / Period	Metric	Value (from PLOS One)	Implication for qualitative research teams
Published August 4, 2026	Sample size	18 interviews (8 female; mean age 54.7, SD 17.3)	Small, targeted qualitative sample: need for saturation-based stopping rules and transparent coding
Recruitment 21 Aug 2024 – 12 Feb 2025	Interview duration	18–48 minutes, mean 35.5 minutes	Moderate-length interviews suitable for data-prompted methods
Study report	Key TDF domains	Skills, beliefs about capabilities, beliefs about consequences, environmental context and resources, emotion	Prioritise these domains in preoperative interview guides and codebooks
Referenced guideline	WHO activity adherence	Only 17% meet recommended activity levels (PLOS One summary)	Quantify gaps and use for segmenting participants by baseline activity

Implications for researchers: ai qualitative analysis spine surgery

Answer: AI-enabled qualitative analysis can accelerate reproducible thematic coding, surface co-occurrence patterns across TDF domains, and support patient-segmented intervention design based on the PLOS One findings.

According to PLOS One (published August 4, 2026), the original study coded transcripts manually and used consensus meetings to determine key domains, a process that is rigorous but resource intensive (PLOS One).

According to the PLOS One authors (published August 4, 2026), pain and fear of harm were recurrent cross-cutting themes, which suggests researchers should prioritise analytic strategies that detect theme co-occurrence and temporal patterns across interviews.

According to implementation science guidance cited in PLOS One (Atkins et al., 2017), mapping TDF-derived barriers to behaviour change techniques benefits from systematic, reproducible coding; AI-enabled tagging supports this mapping at scale while preserving audit trails.

How Evidano helps: from raw interviews to actionable TDF insights

Problem: manual coding is slow and hard to scale

Answer: Manual, line-by-line coding as done in the PLOS One study is rigorous but time consuming; Evidano automates initial coding to speed analysis.

According to PLOS One (published August 4, 2026), the team coded transcripts manually and used consensus meetings to reach domain classifications, a standard but labour-intensive practice (PLOS One).

Evidano (see Evidano Features) can ingest transcripts, perform TDF-informed tentative coding, and output quote-level attributions so teams keep researcher oversight while reducing upfront manual work.

Problem: linking quotes to participant segments and dates

Answer: Researchers need cross-segment frequency tables and temporal context; Evidano produces these automatically.

According to PLOS One (published August 4, 2026), the study reported participant-level attributes (diagnosis, age, interview date) but conducted manual cross-domain comparisons; Evidano automates cross-tabulation of codes by diagnosis, age, and recruitment period.

Evidano provides exportable co-occurrence matrices and visualizations, enabling rapid identification of which TDF domains cluster with pain severity or with environmental constraints.

Problem: verbatim quotes must be preserved, de-identified, and searchable

Answer: Transparent qualitative reporting needs secure, searchable quote management; Evidano supports transcription with PII redaction and searchable quotes.

Evidano offers speech-to-text with custom dictionaries and PII redaction, matching the PLOS One emphasis on anonymized transcripts while making quotes discoverable for synthesis.

Problem: mapping findings to behaviour change techniques

Answer: Translating TDF domains into interventions benefits from systematic mapping; Evidano helps operationalize that step.

Evidano supports exporting thematic outputs into spreadsheets and visualization formats that clinical teams can use to map barriers such as fear of harm to techniques like graded exposure or motivational interviewing, consistent with the PLOS One recommendations.

FAQ: ai qualitative analysis spine surgery

How can AI improve TDF-based qualitative coding for preoperative interviews?

Answer: AI can provide reproducible initial code suggestions, cluster co-occurring themes, and surface quote distributions across TDF domains.

According to PLOS One (published August 4, 2026), the authors used manual TDF coding and consensus to identify key domains, which AI can accelerate by proposing domain labels and highlighting high-frequency belief statements for human review (PLOS One).

Is it ethical to use AI on sensitive patient interviews?

Answer: Yes, when data governance, de-identification, and ethics approvals are in place and AI use is restricted to the research environment.

According to the PLOS One article (published August 4, 2026), full transcripts could not be publicly shared due to ethics constraints, underscoring the need for secure processing; Evidano documents its data security practices to support compliant workflows.

Can Evidano reproduce the PLOS One coding and deliver the same five key domains?

Answer: Yes, Evidano can replicate deductive mapping to TDF domains and produce the same domain-level frequencies for human verification.

According to PLOS One (published August 4, 2026), the study used a predefined TDF codebook and consensus procedures; Evidano can apply the same TDF framework to transcripts and export quote-level evidence to support or challenge manual consensus decisions.

What practical stats from the PLOS One study should teams capture for comparability?

Answer: Capture sample size, recruitment dates, participant diagnosis, age distribution, interview length, and the number of quotes per domain.

According to PLOS One (published August 4, 2026), the study reports 18 interviews (mean age 54.7, SD 17.3), 11 lumbar stenosis and 7 disc herniation cases, interview dates between 21 August 2024 and 12 February 2025, and quote-frequency tables in supporting materials (PLOS One).

Conclusion & Next Steps

Answer: The PLOS One study (published August 4, 2026) shows that preoperative activity behaviour is shaped by pain, fear, context, and emotion, and AI-enabled qualitative methods make these patterns faster to detect and easier to act on (PLOS One).

Researchers and clinicians who want to scale TDF-based interview analysis can use AI to generate reproducible code suggestions, maintain verbatim quote provenance, and produce cross-segment frequency tables for intervention design.

Evidano supports secure transcription, TDF-aligned thematic analysis, and visual exports for teams building tailored prehabilitation programs; see Evidano Features for technical details.

To explore a hands-on workflow that ingests transcripts, suggests TDF codes, and exports evidence tables, Try Evidano for free.

Topics

ai qualitative analysis spine surgery
qualitative analysis patient interviews
ai-assisted thematic analysis
preoperative physical activity barriers

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AI Qualitative Analysis for Spine Surgery Interviews

Key Takeaways

What happened and how the PLOS One study measured it

Findings snapshot

Implications for researchers: ai qualitative analysis spine surgery

How Evidano helps: from raw interviews to actionable TDF insights

Problem: manual coding is slow and hard to scale

Problem: linking quotes to participant segments and dates

Problem: verbatim quotes must be preserved, de-identified, and searchable

Problem: mapping findings to behaviour change techniques

FAQ: ai qualitative analysis spine surgery

How can AI improve TDF-based qualitative coding for preoperative interviews?

Is it ethical to use AI on sensitive patient interviews?

Can Evidano reproduce the PLOS One coding and deliver the same five key domains?

What practical stats from the PLOS One study should teams capture for comparability?

Conclusion & Next Steps

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