Better Care: Qualitative Hypertension Research with AI

The PLOS One study protocol, published on November 10, 2026, proposes a sequential mixed-methods study of how stress, anxiety, depression, coping, and social support influence hypertension-related health behaviour among adults in Southeastern Nigeria.

The protocol shows why qualitative hypertension research matters: interviews can explain the cultural, social, and personal meanings behind statistical patterns in medication adherence, lifestyle, and engagement with care.

• According to the November 2026 PLOS One protocol, hypertension prevalence in Nigeria increased from 8.2% in 1990 to 32.5% in 2020.
• According to the study protocol, Southeastern Nigeria has a documented hypertension prevalence of approximately 52.8% in community samples.
• The protocol requires a minimum quantitative sample of 147 participants after adjusting an initial power calculation of 98 participants by a design effect of 1.5.
• The protocol plans a qualitative sample of 30 interview participants, with six participants from each of the five Southeastern states.
• The protocol states that interviews will last between 30 minutes and one hour and that main data collection was anticipated to conclude by July 2026.

The 2026 PLOS One protocol uses a sequential explanatory mixed-methods design in which quantitative survey findings guide the selection and focus of later qualitative interviews.

According to the authors, the quantitative phase measures perceived stress, anxiety, depression, coping style, social support, demographic factors, and a Health Behaviour Index covering medication adherence, lifestyle practices, and engagement with hypertension treatment.

According to the protocol, the qualitative phase uses semi-structured interviews to explore how adults experience psychological distress, manage hypertension, understand treatment, interact with healthcare services, and draw on social support.

The authors describe the study as addressing a gap in existing evidence because quantitative scales can identify relationships without fully explaining how personal experiences and social-cultural contexts shape health behaviour.

The protocol is guided by the biopsychosocial model, the Health Belief Model, social support theory, and the transactional model of stress and coping. The authors write that the study aims to develop "individualised and culturally informed approaches to hypertension management."

The protocol also includes important constraints. Participants must be adults living in Abia, Anambra, Ebonyi, Enugu, or Imo, and interviews and surveys are conducted in English. The authors acknowledge that English-language eligibility may underrepresent adults with very low levels of formal education.

This is a research protocol rather than a completed results paper. The authors write that "The Southeastern region of Nigeria faces a severe public health challenge from hypertension due to its high prevalence, " but the protocol does not yet report final interview findings or causal conclusions.

AI-enabled qualitative analysis can help health researchers move from large collections of interview transcripts and survey comments to a transparent map of themes, differences, and supporting evidence.

The 2026 PLOS One protocol is particularly suitable for this workflow because its design connects structured survey variables with qualitative explanations. Researchers could compare interview themes across stress levels, age groups, treatment status, states, settlement types, or reported social support.

• Researchers can use thematic analysis to identify recurring experiences such as medication barriers, stress responses, family support, cultural beliefs, and healthcare access.
• Researchers can use cross-segment analysis to examine whether the same theme appears differently across urban, semi-urban, and rural participants.
• Researchers can use frequency analysis to identify repeated concepts, while reviewing quotations and surrounding context to avoid treating frequency as importance.
• Researchers can use co-occurrence analysis to examine relationships between concepts such as stress and adherence or social support and treatment engagement.
• Researchers should treat AI-generated codes as analytic support rather than as a substitute for reflexivity, ethical judgment, cultural interpretation, or supervisory review.

The protocol’s planned use of Braun and Clarke’s six-phase thematic analysis framework also highlights the importance of maintaining a human-led analytic process. AI can accelerate familiarisation, initial coding, comparison, and retrieval, while researchers remain responsible for refining themes, checking extracts, documenting decisions, and interpreting meaning.

Because the source study concerns hypertension and psychological distress, AI analysis should remain non-diagnostic and research-focused. Researchers should protect participant confidentiality, avoid unsupported clinical inferences, and interpret culturally sensitive measures such as depression scales with appropriate caution.

The November 2026 PLOS One protocol demonstrates how qualitative hypertension research can add context to statistical measures of stress, coping, social support, and treatment behaviour in Southeastern Nigeria.

The protocol’s planned 147-person minimum survey sample and 30-person interview sample create a clear opportunity for structured mixed-methods analysis across states and settlement types.

AI-enabled tools can reduce manual synthesis time while helping researchers organize themes, compare participant segments, retrieve supporting evidence, and maintain a more consistent analytic workflow.

Researchers who want to analyze interview transcripts, survey responses, and health documents in one workspace can try Evidano for free.