LifeSpan Digital Wellbeing
Empowering digital wellness for every stage of life
Our vision
To empower every generation with the confidence to navigate safe, inclusive digital services that foster lifelong wellbeing, independence, and meaningful support.
Our mission
We harness student innovation and lived expertise to build and evaluate accessible digital health tools and services. Our mission is to bridge digital divides, enhance wellbeing literacy, and foster intergenerational connection.
Company strategy
Our strategy unites student innovation and lived expertise to co-create accessible digital health tools built on open-source infrastructure. We focus on elevating health literacy and personal wellbeing through user-friendly tools, such as self-administered 24-hour dietary tracking and nutrient evaluation, making digital health navigation intuitive, safe, and accessible across all stages of life.
Leadership and governance
8 people
Who holds this company to account, and who runs it day to day.
Board of Directors
Dr Jue (Grace) Xie
Director · Monash University
Dr Xie is a Teaching and Research Academic in the department of Human-Centred Computing at Monash Faculty of IT, and a senior technical lead in digital health. She has more than 15 years of experience in applied systems research and practice, specialising in software architecture, information systems, knowledge engineering, applied digital health, and human-centred AI innovation. Over the past five years, she has directed the co-design, technical development, and real-world deployment of over 10 sustainable digital health interventions across various domains, including youth mental health, parenting support, preconception health, aged care, and conversational AI, and helped secure over AUD 12 million in competitive research funding. She was also recognised as Australia national "Highly Commended - Engineering" recipient at the 2022 Women Leading Tech Awards.
Pooneh Zarbafian
Director · Monash University
Pooneh is an experienced technology and academic leader with over 22 years of expertise spanning industry and higher education. Passionate about bridging academia and industry through innovative education, digital transformation, and workforce capability development, and honoured to have received the Monash Student Association (MSA) Teaching Award in 2021 for excellence in student learning and engagement.
Dr Jackie Rong
Director · Monash University
Dr. Rong is a Senior Lecturer in the Department of Data Science and Artificial Intelligence at Monash Faculty of IT. Her extensive research expertise lies at the intersection of advanced artificial intelligence and healthcare, specifically focusing on machine learning, deep learning, bio-signal processing, image processing and mutlimodal data analysis. Her dedication to educating the next generation of tech innovators, combined with her strong background in AI-driven health solutions, enables her to provide vital strategic guidance, technical insight, and academic mentorship to the interdisciplinary digital health teams.
Dr Stephen Lindsay
Director · Monash University
Dr Lindsay is a Senior Lecturer in Human–Computer Interaction at Monash University, specialising in participatory design and digital technologies that advance inclusion, agency and equity in healthcare. He brings extensive experience collaborating with healthcare providers, public-sector organisations, charities and diverse communities across Australia, the UK, Europe and Pakistan. His internationally recognised research spans digital health, assistive technology and mental health, and has influenced ethical and methodological practice in human-centred design. As a board member, Stephen contributes deep expertise in responsible innovation, stakeholder engagement and the design of accessible digital health solutions.
Prof Patrick Olivier
Director · Monash University
Prof Olivier internationally recognised expert in digital health, and the design and development of participatory digital services. He led the development of open-source platforms for measuring physical activity and dietary intake that are widely used both nationally and internationally. He brings extensive expertise in community co-design of digital health services to oversee solution design and development process.
George Gouzounis
Director
Sessional Studio Mentor
Dr Anu Ivaturi
Lead Mentor (Business) · Monash University
Dr Ivaturi is a part-time Research Fellow at the Monash Centre for Health Research and Implementation. Her work explores the intersection of public health nutrition, digital public health, and chronic disease prevention. Anu’s PhD examined dietary behaviours of adolescent school children in Delhi, India using the digital dietary assessment system Intake24. Her current research interests lie in integrating digital technologies into healthcare practice to empower non-nutrition health professionals to deliver effective dietary advice.
Joshua Ee
Lead Mentor (Technical) · Monash University
Joshua is an early career researcher and data engineer driving innovation in processing of complex longitudinal data. With his experience in Software Engineering, he works alongside clinicians and healthcare professionals to co-design and understand the blockers of everyday processes that are hindering the ability to integrate heterogeneous sources of contextual information. By using datalakes and AI agents to engineer efficient integration pipelines, his goal is to allow healthcare providers to harness the capabilities of agentic AI to enhance their ability to provide personalised recommendations within their clinical practice.
What you would work on
5 projects
5 projects students join at LifeSpan Digital Wellbeing: the problem, the technology and the roles involved.
Intake24-Clinical AU: Australian Dietary Feedback Integration
Develop the existing Intake24-Clinical prototype into an Australian clinical application. The project will connect Intake24-Clinical to Intake24 version 4 release, and its Australian locale, map dietary records to AUSNUT 2023 food codes, and convert nutrient outputs into Australian Dietary Guidelines serving information and HEIFA scores. This will enable clinicians to provide patients with actionable, Australia-specific dietary feedback. Integrations with REDCap, Qualtrics and SurveyMonkey are also within scope.
The problem
The deployed web-based application Intake24-Clinical does not yet provide Australian-specific clinical dietary feedback. The project will integrate it with Intake24 v4, AUSNUT 2023 and the existing feedback pipeline to generate actionable Australian Dietary Guidelines and HEIFA-based reports.
Technical architecture
Intake24 is a TypeScript 5.8 and Node.js 22 monorepo, managed with pnpm and Nx, with an Express REST API and Vue 3 applications for respondents and administrators. It uses PostgreSQL, Redis, OpenSearch and BullMQ, and is deployed through Docker, GitLab CI/CD and SST-managed AWS infrastructure. Students will work with modern development practices including strict type checking, automated testing with Vitest/Jest and Playwright, API security, offline-first PWA support, and GitLab-based issue and merge-request workflows.
GenAI-enabled Authoring System for Self-Directed Educational Health Interventions on Induk
Build a GenAI-enabled authoring system that converts evidence-based health-intervention documents into deployable, mobile-friendly Induk MDX modules. The system will map source content to reusable Induk components—including surveys, narrative blocks, goal-setting tools, diaries, quizzes, rich media, feedback and progress tracking—while preserving clinical intent and UX quality. It will combine an expert-reviewable component knowledge base, AI-assisted content analysis and MDX composition, automated technical validation, mandatory human-review gates, and cross-session memory that learns from approved decisions and corrections. This addresses the current reliance on developers’ tacit knowledge and reduces the risk of syntactically valid but clinically inappropriate component choices.
The problem
Developing self-directed educational health and wellbeing interventions requires translating evidence-based content into appropriate interactive digital experiences while preserving educational and clinical intent. Current authoring relies heavily on developers’ tacit knowledge, making the process slow, inconsistent, and vulnerable to technically valid but semantically inappropriate design decisions.
Technical architecture
TypeScript, React, Next.js, MDX, Induk component library, Node.js or Python for agent orchestration, structured JSON/YAML component definitions, Strapi, GraphQL, Firebase Authentication, Cloud Functions, Firestore, Firebase Hosting, Google Analytics, Git/GitLab, LLM APIs, schema validation, automated testing and QA.
Pathways Tasmania: A Digital Resource Hub for Youth Mental Health Help-Seeking
Young people in Tasmania and their families face a fragmented mental health service landscape. Those who need support most are often the least equipped to find it. This project, in collaboration with the Mental Health Council of Tasmania (MHCT), will further develop and maintain a digital system that makes appropriate youth mental health resources and services easier to find and access. Using a dynamic, searchable service registry, it supports anonymous access to lower stigma barriers using natural-language matching to match a young person's needs to relevant services.
The problem
Young people and their families in Tasmania currently face a highly fragmented mental health service landscape, where those most in need are often the least equipped to find appropriate support. The Pathways Tasmania project addresses this gap by developing a dynamic digital registry that lowers stigma and easily connects youth to relevant mental health resources.
Technical architecture
React (or Astro Starlight for the content layer), Python/Node.js API, PostgreSQL, Git. NLP/text-matching components (the students are exploring TextReasoningBench and Bag-of-Opinions approaches) are a possible extension.
MindMyPain Chronic Pain Self-Management Tool: Ongoing Development and Maintenance
Continue developing and maintaining MindMyPain, a mobile-friendly tool that helps people living with chronic pain capture, organise, and retrieve patient-held health information. The platform converts voice or text input into reviewable drafts for journal entries, medications, appointments, prescriptions and referrals, immunisations, and care-team records. The project intends to evolve the prototype into an installable Progressive Web App (PWA) or cross-platform native mobile application for iOS and Android. Ongoing work will focus on reliability, accessibility, privacy, security, offline and low-connectivity use, responsible AI behaviour, and features such as clinician-ready exports, administrative alerts, caregiver access, and a simplified interface for periods of acute symptoms.
The problem
People living with chronic pain must manage complex and fragmented health information while their physical and cognitive capacity can fluctuate significantly. MindMyPain addresses this by providing a patient-controlled digital platform for capturing, organising, and retrieving journal, medication, appointment, referral, and care-team information with reviewable AI assistance.
Technical architecture
Existing web application codebase; PWA technologies or a cross-platform framework such as React Native or Flutter; OpenAI APIs (GPT-5.2, GPT-4o, Whisper-1); structured JSON schemas; secure database and cloud hosting; Git/GitHub; automated testing and CI/CD; accessibility and mobile usability-testing tools. (The final mobile framework and deployment architecture will be selected after reviewing the existing application codebase.)
CarerConnect AI: Stroke Family and Carer Navigator
Develop a conversational navigator that helps carers understand stroke, support secondary prevention and locate appropriate services. Questions could cover medicines, appointments, communication difficulties, lifestyle support, home safety and preparation for clinical visits. Responses would draw only from approved resources and clearly differentiate education from medical advice. The platform could create question lists for healthcare appointments, summarise selected guidance and produce a family action plan. It should also recognise carer distress or emergency language and offer appropriate escalation pathways.
The problem
Regional and rural communities face persistent health disparities due to geographical isolation, limited access to local healthcare specialists, and public health messaging that fails to account for regional contexts. Standard health communication strategies often miss these underserved populations, requiring tailored, data-driven digital strategies to effectively deliver preventative stroke literacy and engagement to regional areas.
Technical architecture
The platform is built on a high-performance Python and FastAPI backend, using LlamaIndex or LangChain to orchestrate a Retrieval-Augmented Generation (RAG) pipeline powered by foundation models such as OpenAI, Azure OpenAI, or Claude. To store and query vector embeddings for verified health resources, it uses PostgreSQL equipped with pgvector. The user interface is crafted with modern web frameworks like React/Next.js or Vue/Nuxt, seamlessly connecting to government and service-directory APIs and featuring document generation capabilities to produce custom PDFs like family action plans. The entire application lifecycle is fully automated and secured using GitHub, Docker, pytest, and Cypress or Playwright for end-to-end testing and streamlined deployment.
Interested in LifeSpan Digital Wellbeing?
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