Boora, an AI-assisted digital platform for overweight and obesity care in Brazilian primary care: a formative mixed-methods evaluation of perceived usability and acceptability
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Objective
To evaluate the perceived usability, acceptability, and user experience (rather than the clinical effectiveness) of Boora, an AI-assisted, human-supervised digital platform prototype for longitudinal overweight and obesity care, among users and health professionals in Brazilian primary care.
Design
Convergent mixed-methods formative evaluation. Perceived usability was measured with the System Usability Scale (SUS) and summarised descriptively; semi-structured interviews conducted after hands-on use were analysed with codebook thematic analysis (Braun and Clarke); the two strands were integrated through a joint display. Qualitative reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ).
Setting
Primary health care network of Ananindeua, Pará, within the Brazilian Unified Health System (January to February 2026).
Participants
Fifteen adults with overweight or obesity (BMI at least 25 kg/m2, confirmed via electronic health records) who used the patient application on their own smartphones for 24 hours, and eight primary care professionals (nurses, physicians, and a dietitian) who used the professional dashboard for approximately 20 minutes on predefined tasks with synthetic data.
Main outcome measures
SUS scores and qualitative themes addressing usability, acceptability, perceived usefulness, barriers, and perceived clinical and workflow fit.
Results
Boora showed good perceived usability in both cohorts (users mean 76.5, SD 10.3; professionals mean 77.5, SD 4.6; both above the SUS normative average of 68). Four themes emerged per cohort. Users valued an accessible interface and visible progress but described daily logging burden, fragile anticipated engagement, and digital-literacy and accessibility barriers. Professionals valued a clear interface and the prospect of panel-managed, proactive follow-up, while requiring training, AI governance, protected time, and interoperability with the national record. Integration indicated that the disengagement users anticipated was the risk professionals perceived the dashboard could help identify, whereas the educational AI assistant was the weakest and most ambiguous component for both groups.
Conclusions
Boora was perceived as usable and acceptable, with perceived value concentrated in human-supervised, longitudinal follow-up rather than autonomous self-tracking or AI advice. These findings concern perceived usability and acceptability, not clinical effectiveness or sustained engagement. Real-world adoption would depend on accessibility refinements, electronic-record integration, and clear AI governance aligned with the principles of Brazil’s proposed risk-based AI framework and the LGPD.
Key points
What is already known on this topic
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Mobile and web applications can support weight management, but user engagement tends to decline over time without structured professional support, and most evidence comes from high-income countries.
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Autonomous AI advice in primary care can be unsafe or discordant with clinical guidelines, underscoring the need for human oversight and auditable systems.
What this study adds
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An AI-assisted, human-supervised platform prototype pairing a patient application with a professional dashboard was perceived as usable and acceptable by both patients and primary care professionals in the Brazilian public health system, based on brief hands-on use.
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Mixed-methods integration indicated that the disengagement users anticipated corresponded to the risk professionals perceived the dashboard could help identify, locating the platform’s perceived value in human-supervised longitudinal follow-up; the educational AI assistant was its weakest component.
How this study might affect research, practice or policy
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It supports designing AI in primary care obesity tools as auditable, supervised support rather than autonomous advice, consistent with the principles of Brazil’s proposed risk-based AI framework.
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Participants identified accessibility for low-literacy users, interoperability with the national electronic health record, protected time, and training as potential prerequisites for adoption, which warrant longitudinal, real-world evaluation.
