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Digital Acceptance and Commitment Therapy for Lifestyle Change in Overweight Pregnant Women: A Feasibility Pilot
Anna Elena Nicoletti1, Michele Tonelli1, Barbara Purin1
1Digital Health Research, Centre for Digital Health & Wellbeing, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy.
Overweight and obesity during pregnancy are associated with increased maternal and neonatal risks, yet scalable interventions addressing the psychological processes underlying health behaviours remain limited. This study describes the development and formative evaluation of DEMETRA, a chatbot delivering an Acceptance and Commitment Therapy (ACT)-informed intervention to promote healthier lifestyles in pregnant women. In line with Phase 1 of the Obesity-Related Behavioral Intervention Trials framework, a multidisciplinary team developed a six-session digital program delivered via a rule-based virtual assistant. A mixed-methods design was employed to assess acceptability, usability, and perceived relevance among a heterogeneous stakeholder sample. Sixteen stakeholders (psychologists, communication experts, nutritionists, clinicians, and non-overweight, expectant women or those who had recently delivered) participated in iterative testing; 15 completed quantitative measures (Semantic Differential scales, uMARS, BUS-11) and 16 completed semi-structured interviews. Non-parametric analyses indicated significantly positive evaluations across most communication and content domains, particularly clarity and language appropriateness, whereas session duration and several engagement-related dimensions did not significantly differ from neutrality. Qualitative findings confirmed strengths in clarity, non-stigmatising tone, and multimedia support, while identifying limited personalisation and message pacing as key areas for refinement. Overall, findings provide formative evidence that ACT-informed principles can be translated into a chatbot-delivered antenatal program and highlight concrete priorities for optimisation (e.g., personalisation and message pacing). Because end-user testing did not include overweight/obese pregnant women and the sample was small and heterogeneous, conclusions regarding acceptability/feasibility in the intended clinical population remain preliminary; the results primarily support iterative refinement and subsequent proof-of-concept testing in the target group.
Overweight and obesity during pregnancy are associated with increased maternal and neonatal risks, yet scalable interventions addressing the psychological processes underlying health behaviours remain limited. This study describes the development and formative evaluation of DEMETRA, a chatbot delivering an Acceptance and Commitment Therapy (ACT)-informed intervention to promote healthier lifestyles in pregnant women. In line with Phase 1 of the Obesity-Related Behavioral Intervention Trials framework, a multidisciplinary team developed a six-session digital program delivered via a rule-based virtual assistant. A mixed-methods design was employed to assess acceptability, usability, and perceived relevance among a heterogeneous stakeholder sample. Sixteen stakeholders (psychologists, communication experts, nutritionists, clinicians, and non-overweight, expectant women or those who had recently delivered) participated in iterative testing; 15 completed quantitative measures (Semantic Differential scales, uMARS, BUS-11) and 16 completed semi-structured interviews. Non-parametric analyses indicated significantly positive evaluations across most communication and content domains, particularly clarity and language appropriateness, whereas session duration and several engagement-related dimensions did not significantly differ from neutrality. Qualitative findings confirmed strengths in clarity, non-stigmatising tone, and multimedia support, while identifying limited personalisation and message pacing as key areas for refinement. Overall, findings provide formative evidence that ACT-informed principles can be translated into a chatbot-delivered antenatal program and highlight concrete priorities for optimisation (e.g., personalisation and message pacing). Because end-user testing did not include overweight/obese pregnant women and the sample was small and heterogeneous, conclusions regarding acceptability/feasibility in the intended clinical population remain preliminary; the results primarily support iterative refinement and subsequent proof-of-concept testing in the target group.
