Research focus
Explore Masud Rabbani’s mCARE and MPredA research on mobile health for autism, caregiver-reported longitudinal data, developmental milestones, and remote monitoring in Bangladesh.
9 related publications · 1 projectResearch overview
My autism and mobile-health research examines how sustained digital contact can help families and practitioners understand developmental change between in-person visits. The central program is mCARE, a mobile-based care and remote-monitoring platform for children with autism spectrum disorder in Bangladesh. Its workflow brings together caregiver observations, remote experience sampling, structured data management, and practitioner access to longitudinal information. Rather than treating a mobile application as an isolated product, the work studies the larger relationship among family routines, reporting burden, clinical interpretation, local services, and the technologies that connect them.
The publication record follows mCARE from value-sensitive and behavior-informed design through deployment and analysis. One end-to-end study describes a two-year deployment across four specialist centers involving 300 children. Other studies examine caregiver experience, data-driven validation, developmental milestone achievement, and patterns of risk and resilience around the COVID-19 disruption. MPredA extends the program with machine-learning models that estimate improvement across milestone parameters. These models are presented as research tools for understanding longitudinal patterns—not as replacements for professional diagnosis or individualized care.
Across the program, the technical questions are inseparable from equity and context. Caregiver-reported observations may be incomplete or shaped by changing family circumstances. Connectivity, language, digital literacy, clinical workload, and cultural expectations influence whether a workflow can be sustained. The research therefore combines system design, longitudinal analysis, machine learning, and stakeholder-centered evaluation while making the limits of prediction explicit. The goal is practical evidence that can strengthen family–practitioner communication in settings where continuous specialist access may be difficult.
My contribution
My documented contribution to mCARE centered on data analysis and publications. I am the first and corresponding author of the Scientific Reports risk-and-resilience study; its author-contribution statement says that all authors contributed equally. That work used longitudinal caregiver reports to examine children’s behavior before, during, and after the COVID-19 lockdown.
For MPredA, my documented role was building the machine-learning models and designing the system. Across the broader mCARE publication record, I coauthored research on remote experience sampling, developmental milestones, family context, and caregiver–practitioner collaboration. These systems are research tools for longitudinal understanding, not substitutes for autism diagnosis or individualized care.
Key findings
Each result links to its complete publication record, abstract, authors, affiliations, and publisher source.
The study analyzed 56,290 caregiver-reported observations across 30 behavioral parameters from 150 monitored children. Outcomes were not uniformly negative during lockdown, although problematic behavior emerged as an area where additional support could reduce risk.
Read the supporting paperA study of 300 children found at least 95% accuracy for three supervised models on the tooth-brushing milestone; KNN and ANN reached 84% for asking to use the toilet, while ANN reached 91% for toileting and 76% for fastening large buttons.
Read the supporting paperThe mCARE evaluation found that benefits to mental-health professionals depended on caregiver–professional collaboration, that the platform enabled study of COVID-19’s effects, and that family values shaped its impact.
Read the supporting paperQuestions
How can repeated caregiver observations reveal developmental patterns without reducing a child to one score or interpreting ordinary variation as a diagnosis?
Which prompts, schedules, and feedback make remote reporting useful to families while limiting burden and respecting changing household circumstances?
How should machine-learning results, missing data, and uncertainty be communicated so practitioners can interpret them alongside clinical and family knowledge?
Research method
Shape the mobile workflow around caregiver routines, practitioner needs, local services, and value-sensitive design principles before selecting technical features.
Collect structured, repeated observations and document participation, missingness, and external events that may influence a developmental trajectory.
Compare models for milestone-related patterns while separating research prediction from diagnosis and retaining the context needed for interpretation.
Study usability, caregiver experience, practitioner workflow, data quality, equity, and sustained deployment alongside computational performance.
Journal article · 2025
Md M Haque, Md Ishrak Islam, Masud Rabbani, Dipranjan Das, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Shaheen Akhter, Shahana Parveen, Azima Begum, Mohammad Shaha A Patwary, Austin Schmidt, Brandon Franczak, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “Study Results of mCARE: Developing, Deploying, and Analysing the End-to-End Results of a Mobile-Based Remote Monitoring Tool for Children with Autism Spectrum Disorder in Bangladesh”. EAI Endorsed Transactions on Pervasive Health and Technology, 11. (2025). https://doi.org/10.4108/eetpht.11.9981.
Journal article · 2022
Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “A data-driven validation of mobile-based care (mCARE) project for children with ASD in LMICs”. Smart Health, 26, 100345. (2022). https://doi.org/10.1016/j.smhl.2022.100345.
Journal article · 2021
Munirul M Haque, Masud Rabbani, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach”. JMIR Medical Informatics, 9(6), e29242. (2021). https://doi.org/10.2196/29242.
Journal article · 2021
Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “An mCARE study on patterns of risk and resilience for children with ASD in Bangladesh”. Scientific Reports, 11, 21342. (2021). https://doi.org/10.1038/s41598-021-00793-7.
Journal article · 2021
Munirul M. Haque, Masud Rabbani, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Shaheen Akhter, Shahana Parveen, Mohammad Rasel, Golam Rabbani, Faruq Alam, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “Grant Report on mCARE: Mobile-Based Care for Children with Autism Spectrum Disorder (ASD) for Low- and Middle-Income Countries (LMICs)”. Journal of Psychiatry and Brain Science, 6(1), e210004. (2021). https://doi.org/10.20900/jpbs.20210004.
Conference paper · 2025
Md M. Haque, Md Ishrak Islam, Masud Rabbani, Dipranjan Das, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Shaheen Akhter, Shahana Parveen, Azima Begum, Mohammad Shaha A. Patwary, Austin Schmidt, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “mCARE: Integrating Mobile Phones, Caregivers, and Health Practitioners to Provide Regular Monitoring and Care for the Children with ASD in Bangladesh”. Pervasive Computing Technologies for Healthcare (PervasiveHealth 2024), 612, 70–88. (2025). https://doi.org/10.1007/978-3-031-85575-7_4.
Conference paper · 2020
Munirul M. Haque, Dipranjan Das Dipal, Masud Rabbani, Md Ishrak Islam Zarif, Anik Iqbal, Shaheen Akhter, Shahana Parveen, Mohammad Rasel, Basana Rani Muhuri, Tanjir Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “Towards Developing A Mobile-Based Care for Children with Autism Spectrum Disorder (mCARE) in low and middle-income countries (LMICs) like Bangladesh”. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 746–753. (2020). https://doi.org/10.1109/COMPSAC48688.2020.0-170.
Conference paper · 2022
Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “MPredA: A Machine Learning Based Prediction System to Evaluate the Autism Level Improvement”. Pervasive Computing Technologies for Healthcare (PH 2021), 431, 416–432. (2022). https://doi.org/10.1007/978-3-030-99194-4_26.
Book chapter · 2022
Masud Rabbani, Munirul M. Haque, Dipranjan Das Dipal, Md Ishrak Islam Zarif, Anik Iqbal, Shaheen Akhter, Shahana Parveen, Mohammad Rasel, Tanjir Rashid Soron, Naveen Bansal, Amy Schwichtenberg, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed. “A Mobile Health Application for Monitoring Children With Autism Spectrum Disorder: ASD Monitoring by mHealth”. AI Applications for Disease Diagnosis and Treatment, 40–65. (2022). https://doi.org/10.4018/978-1-6684-2304-2.ch002.
Responsible translation
The practical aim is a clearer and more continuous view of development that supports timely conversations among families and care teams. A useful remote-monitoring system should make uncertainty and missing information visible, preserve human judgment, and provide value even when a prediction is not appropriate.
Future translation requires evaluation with diverse families and clinical teams, careful privacy and consent practices, local governance, independent validation, and study of long-term adoption. The published work establishes a research foundation; it does not make the platform a diagnostic service.
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