JMIR Publications

ABSTRACT

Background:

Stunting remains a major public health problem worldwide and is strongly associated with impaired cognitive and language development. Although psychosocial stimulation has been shown to improve development, conventional interventions often require intensive supervision by health professionals and are difficult to sustain in community settings. Digital health technologies offer opportunities to enhance caregiver engagement; however, evidence on chatbot-based supervised self-assessment for psychosocial stimulation remains limited.

Objective:

This study aimed to develop and evaluate a WhatsApp® chatbot-based supervised self-assessment model using the Home Observation for Measurement of the Environment (HOME) Inventory to improve psychosocial stimulation practices and cognitive development among stunted children.

Methods:

This research and development study used the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. During the analysis phase, a literature review and stakeholder focus group discussions were conducted to identify intervention needs. The intervention was then developed through a participatory process involving stakeholder consultation, expert validation, and iterative refinement to ensure its relevance, feasibility, and suitability for community-based child development programs. The final intervention integrated a WhatsApp® chatbot, the Home Observation for Measurement of the Environment (HOME) Inventory, educational videos, and a monitoring dashboard. This model enabled caregivers to conduct self-assessments while receiving ongoing supervision from health personnel. The model underwent expert validation and usability testing using the Usefulness, Satisfaction, and Ease of Use (USE) Questionnaire. Its effectiveness was subsequently evaluated in a randomized controlled trial involving caregivers of stunted children at four Rumah Pelita centers in Semarang, Indonesia. Children’s cognitive development was assessed before and after the intervention using the Capute Scales, comprising the Cognitive Adaptive Test (CAT) and the Clinical Linguistic and Auditory Milestone Scale (CLAMS). Quantitative data were analyzed using Cohen’s kappa coefficient, the Wilcoxon signed-rank test, the Sign test, and the Mann–Whitney U test; qualitative data were analyzed using content analysis.

Results:

The needs assessment identified the need for a practical digital intervention integrating psychosocial stimulation assessment, caregiver education, and continuous supervision. A WhatsApp® chatbot-based supervised self-assessment model was successfully developed and validated. The model demonstrated high usability, with an overall USE-score of 87.32%, indicating excellent user acceptance. The chatbot-based HOME-Inventory assessment showed good agreement with the conventional assessment. Compared with the control group, the intervention significantly improved children's developmental outcomes, including Developmental Quotient (DQ) CAT (Δ=7.14, p = 0.012), DQ CLAMS (Δ=4.62, p = 0.049), Full-Scale Developmental Quotient (FSDQ) (Δ=5.70, p = 0.007), expressive language (Δ=8.73, p = 0.004), receptive language (Δ=4.91, p = 0.007), and overall language development (Δ=6.27, p = 0.004).

Conclusions:

The WhatsApp® chatbot-based supervised self-assessment model is a valid, feasible, and effective digital intervention for supporting psychosocial stimulation among caregivers of stunted children. Integrating standardized assessment, educational support, and continuous supervision into a widely accessible messaging platform could strengthen community-based child development programs, particularly in low-resource settings. Clinical Trial: N/A