Physical Activity Barriers: Qualitative Analysis for Pre-surgery

This post explains how a PLOS ONE qualitative study identified barriers and facilitators to physical activity among patients awaiting lumbar spine surgery, and how AI-enabled qualitative research can accelerate translation of those findings into tailored interventions. The primary keyword for this post is "physical activity barriers qualitative analysis." According to García-Moreno et al. in PLOS ONE (published August 4, 2026), 18 patients on a surgical waiting list described a complex mix of physical, emotional, and contextual determinants that shaped everyday activity. Researchers, clinicians, and qualitative teams can use AI tools to scale thematic synthesis, preserve direct quotations, and produce patient-informed intervention blueprints faster and more transparently.

Key Takeaways

According to the PLOS ONE study by García-Moreno et al. (published August 4, 2026) PLOS ONE, five key Theoretical Domains Framework domains shaped physical activity behaviour in 18 patients awaiting lumbar spine surgery: skills, beliefs about capabilities, beliefs about consequences, environmental context and resources, and emotion.

18 patients were interviewed between 21 August 2024 and 12 February 2025, with a mean age of 54.7 years (SD 17.3) and 11 patients with lumbar stenosis versus 7 with disc herniation, according to the PLOS ONE article (published August 4, 2026).
According to the PLOS ONE study (August 4, 2026), the interviews averaged 35.5 minutes (SD = 8.2), and recruitment came from 87 registry entries of which 64 met eligibility and 18 consented.
According to the PLOS ONE paper (August 4, 2026), only 17% of patients meet WHO-recommended physical activity levels while waiting for lumbar surgery, which frames the clinical gap for preoperative interventions.

What happened: study design and measures

Answer: The PLOS ONE study used data-prompted semi-structured interviews and the Theoretical Domains Framework to identify barriers and facilitators to everyday physical activity in patients awaiting lumbar surgery.

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), the researchers recruited 18 adults (8 female) from the Canadian Spine Outcomes and Research Network at Saint John Regional Hospital and conducted interviews between 21 August 2024 and 12 February 2025.

According to the PLOS ONE methods section (August 4, 2026), interviews combined each patient’s self-reported Godin-Shephard Leisure-Time Physical Activity Questionnaire responses with TDF-guided prompts, were audio recorded, transcribed verbatim, and analysed using a hybrid deductive/inductive coding approach.

Findings snapshot

Date / Source	Metric	Value	Implication
Published August 4, 2026 (PLOS ONE)	Sample size	18 patients	Qualitative depth, saturation achieved; not population-representative
Interview window Aug 21, 2024–Feb 12, 2025 (PLOS ONE)	Interview mean duration	35.5 minutes (SD 8.2)	Sufficient time for data-prompted reflection and illustrative quotes
PLOS ONE (Aug 4, 2026)	Diagnosis split	11 lumbar stenosis, 7 disc herniation	Findings likely generalise across common degenerative lumbar conditions
PLOS ONE (Aug 4, 2026)	WHO physical activity adherence cited	17% meet recommended levels while waiting	Large preoperative activity gap for intervention design

Core barriers and facilitators identified

Answer: The PLOS ONE study found barriers clustered around pain, reduced capability, fear of harm, access limitations, and negative emotions, while facilitators included motivation, professional guidance, supportive environments, and daily habits.

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), five key TDF domains were classified as central drivers: skills; beliefs about capabilities; beliefs about consequences; environmental context and resources; and emotion.

According to the PLOS ONE results (August 4, 2026), representative patient voices included: “I can’t trust my legs and hips to do anything that is [a] big exertion” (Patient 4, 70–79 years, lumbar stenosis) and “It was good to have guidance to make sure I was doing it right, they [the physiotherapists] were getting the most benefit whatever I was doing” (Patient 14, 70–79 years, lumbar stenosis).

Implications for clinical researchers and rehabilitation teams

How should researchers prioritise targets?

Answer: Researchers should prioritise interventions that address pain-related capability limits, safety beliefs, and access to guided, low-cost activity options.

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), targeting the five key TDF domains and mapping behaviour change techniques to those domains can increase relevance: for example, graded supervised activity for skills, motivational interviewing for beliefs about capabilities, and habit-forming action planning for behavioural regulation.

What outcome measures should trials include?

Answer: Trials should include objective activity measures, symptom scales, and emotion or mood measures to reflect the complex determinants described.

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), the authors recommend using accelerometers alongside patient-reported mODI and EQ-VAS and testing feasibility with control arms to detect behavioural change and postoperative outcome signals.

How Evidano helps with physical activity qualitative analysis

Problem: small qualitative samples slow translation → Solution: rapid thematic synthesis

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano can ingest interview transcripts like the PLOS ONE dataset, apply thematic coding consistent with the Theoretical Domains Framework, and produce frequency, co-occurrence, and representative quote matrices in hours rather than weeks.

Evidano’s thematic export preserves verbatim quotes with participant metadata so teams can report precisely the type of illustrative statements used in the PLOS ONE article while protecting PII via built-in redaction; see Evidano features for relevant capabilities.

Problem: personalised prompts and mixed methods integration → Solution: data-prompted interview support

Evidano supports data-prompted workflows by linking questionnaire scores to interview prompts and visualising subtheme distributions across participant subgroups.

Evidano reduces the manual workload described in the PLOS ONE methods by automating transcript ingestion, speaker separation, and preliminary TDF-aligned tagging so researchers retain human oversight while accelerating consensus coding.

Problem: transcription and multilingual data → Solution: accurate speech-to-text and translation

Evidano offers automated transcription with custom dictionaries and PII redaction and can translate phrases while preserving technical terms, enabling multi-site teams to harmonise coding without re-keying transcripts; see Evidano speech-to-text.

FAQ: physical activity barriers qualitative analysis

What were the study’s main barriers to physical activity?

Answer: The main barriers were pain-driven capability limits, fear of harm from activity, unsuitable environments, and negative emotions, according to the PLOS ONE study (August 4, 2026).

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), patients reported unpredictable pain, balance issues, and prior experiences of symptom exacerbation that led to avoidance and modification of activities.

Which facilitators are most actionable preoperatively?

Answer: Professional guidance, social support, accessible environments, and habit-focused action planning were the most actionable facilitators identified by the PLOS ONE authors (August 4, 2026).

According to García-Moreno et al. in PLOS ONE (published August 4, 2026), patients who received physiotherapy guidance described increased confidence and safer adaptation of activity.

Can AI tools reproduce the TDF coding used in the study?

Answer: AI-assisted coding can reproduce and accelerate TDF-aligned tagging but should be combined with human consensus to match the study’s methodological rigor.

According to the PLOS ONE methods (García-Moreno et al., August 4, 2026), the original analysis used manual line-by-line coding with researcher consensus; an AI-human hybrid preserves theoretical fidelity while improving speed and reproducibility.

What external guidance should teams pair with qualitative findings?

Answer: Teams should pair qualitative insights with physical activity guidelines such as WHO recommendations to set concrete targets and with behaviour-change taxonomies to map interventions.

For background, see the World Health Organization guidance on physical activity and health (WHO).

Conclusion & Next Steps

Answer: The PLOS ONE qualitative study (García-Moreno et al., published August 4, 2026) shows that preoperative physical activity is shaped by interlocking physical, emotional, and contextual factors that require tailored, theory-driven interventions.

According to the PLOS ONE authors (August 4, 2026), addressing pain, fear of harm, guidance access, and habit formation is likely to be more effective than generic advice alone.

If your team runs qualitative studies or prehabilitation trials, consider combining TDF-informed interview designs with AI-assisted coding and transcription to accelerate trial-ready insights.

To explore AI-enabled thematic synthesis and secure transcript handling, Try Evidano for free.

Topics

physical activity barriers qualitative analysis
preoperative physical activity qualitative
TDF spine surgery qualitative
AI qualitative research for healthcare

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Physical Activity Barriers: Qualitative Analysis for Pre-surgery

Key Takeaways

What happened: study design and measures

Findings snapshot

Core barriers and facilitators identified

Implications for clinical researchers and rehabilitation teams

How should researchers prioritise targets?

What outcome measures should trials include?

How Evidano helps with physical activity qualitative analysis

Problem: small qualitative samples slow translation → Solution: rapid thematic synthesis

Problem: personalised prompts and mixed methods integration → Solution: data-prompted interview support

Problem: transcription and multilingual data → Solution: accurate speech-to-text and translation

FAQ: physical activity barriers qualitative analysis

What were the study’s main barriers to physical activity?

Which facilitators are most actionable preoperatively?

Can AI tools reproduce the TDF coding used in the study?

What external guidance should teams pair with qualitative findings?

Conclusion & Next Steps

Topics

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