Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data is collected on individuals. Deploying online AI presents a key challenge: balancing adaptability of online AI with reproducibility. Online AI in digital interventions is a rapidly evolving area, driven by advances in algorithms, sensors, software, and devices. Digital health intervention development and deployment is a continuous process, where implementation - including the AI decision-making algorithm - is interspersed with cycles of re-development and optimization. Each deployment informs the next, making iterative deployment a defining characteristic of this field. This iterative nature underscores the importance of reproducibility: data collected across deployments must be accurately stored to have scientific utility, algorithm behavior must be auditable, and results must be comparable over time to facilitate scientific discovery and trustworthy refinement. This paper proposes a reproducible scientific workflow for developing, deploying, and analyzing online AI decision-making algorithms in digital health interventions. Grounded in practical experience from multiple real-world deployments, this workflow addresses key challenges to reproducibility across all phases of the online AI algorithm development life-cycle.

Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this paper is to provide guidelines for enabling effective monitoring of online decision-making algorithms with the goal of (1) safeguarding individuals and (2) ensuring data quality. We elucidate guidelines and discuss our experience in monitoring online decision-making algorithms in two digital intervention clinical trials (Oralytics and MiWaves). Our guidelines include (1) developing fallback methods, pre-specified procedures executed when an issue occurs, and (2) identifying potential issues categorizing them by severity (red, yellow, and green). Across both trials, the monitoring systems detected real-time issues such as out-of-memory issues, database timeout, and failed communication with an external source. Fallback methods prevented participants from not receiving any treatment during the trial and also prevented the use of incorrect data in statistical analyses. These trials provide case studies for how health scientists can build monitoring systems for their digital intervention. Without these algorithm monitoring systems, critical issues would have gone undetected and unresolved. Instead, these monitoring systems safeguarded participants and ensured the quality of the resulting data for updating the intervention and facilitating scientific discovery. These monitoring guidelines and findings give digital intervention teams the confidence to include online decision-making algorithms in digital interventions.

The detrimental effects of air pollutants on human health have prompted increasing concerns regarding indoor air quality (IAQ). The emergence of digital health interventions and citizen science initiatives has provided new avenues for raising awareness, improving IAQ, and promoting behavioural changes. The Technology Acceptance Model (TAM) offers a theoretical framework to understand user acceptance and adoption of IAQ technology. This paper presents a case study using the COM-B model and Internet of Things (IoT) technology to design a human-centred digital visualisation platform, leading to behavioural changes and improved IAQ. The study also investigates users' acceptance and adoption of the technology, focusing on their experiences, expectations, and the impact on IAQ. Integrating IAQ sensing, digital health-related interventions, citizen science, and the TAM model offers opportunities to address IAQ challenges, enhance public health, and foster sustainable indoor environments. The analytical results show that factors such as human behaviour, indoor activities, and awareness play crucial roles in shaping IAQ.

Pandemics, notably the recent COVID-19 outbreak, have impacted both public health and the global economy. A profound understanding of disease progression and efficient response strategies is thus needed to prepare for potential future outbreaks. In this paper, we emphasize the potential of Agent-Based Models (ABM) in capturing complex infection dynamics and understanding the impact of interventions. We simulate realistic pharmaceutical, behavioral, and digital interventions that mirror challenges in real-world policy adoption and suggest a holistic combination of these interventions for pandemic response. Using these simulations, we study the trends of emergent behavior on a large-scale population based on real-world socio-demographic and geo-census data from Kings County in Washington. Our analysis reveals the pivotal role of the initial 100 days in dictating a pandemic's course, emphasizing the importance of quick decision-making and efficient policy development. Further, we highlight that investing in behavioral and digital interventions can reduce the burden on pharmaceutical interventions by reducing the total number of infections and hospitalizations, and by delaying the pandemic's peak. We also infer that allocating the same amount of dollars towards extensive testing with contact tracing and self-quarantine offers greater cost efficiency compared to spending the entire budget on vaccinations.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

This edition of Digital Health News industry round-up includes projects focusing on artificial intelligence (AI), an app to tackle health inequalities and a project to use primary care data to help eradicate hepatitis C. The Royal Marsden NHS Foundation Trust and the Francis Crick Institute are working with AI start-up Owkin to gain a better understanding of how kidney cancers evolve to help improve treatment for the disease. By studying the evolutionary features of a tumour and understanding how it has evolved through a series of genetic changes over time, scientists hope to help doctors predict a patient's outcome, meaning they can tailor treatment to individuals to improve health outcomes. Dr Samra Turajlic, group leader at the Francis Crick Institute and consultant medical oncologist at The Royal Marsden NHS Foundation Trust, said: "We know that the outcomes of any individual patient with kidney cancer are determined by the distinct way their tumour evolves. We want to be able to predict the next step in a tumour's evolutionary trajectory and better tailor treatments that can effectively tackle a patient's cancer. "New technologies and tools are critical in helping us achieve this at a scale and speed that is required in clinical practice, and at a cost that will make these measurements implementable in most healthcare systems." It hopes that it will be able to draw links between the histological characteristics of a tumour with patient outcomes. This will also support the move to precision medicine. The project will use rapid and low-cost AI on digital pathology, helping the day-to-day management of patients in a cost-effective way. Thomas Clozel, co-founder and CEO of Owkin, said: "By using AI to improve our fundamental understanding of cancer tumours, we aim to enable doctors to move towards a precision medicine approach to treatment.

The COVID-19 pandemic has had a major impact on mental health across the globe. Depression is predicted to be the leading cause of lost life years due to illness by 2030. At the same time, less than 1 in 5 people receive appropriate treatment. Digital interventions – which package up psychotherapeutic components into a computer program or mobile app – have been proposed as a way of meeting the unmet demand for psychological treatment. As digital interventions are becoming increasingly adopted within both private and public healthcare systems, researchers asked if digital interventions are as effective as traditional face-to-face therapy, whether the benefits are also found in public healthcare settings and what is the role of human support.

India's busiest railway station has an approximate footfall of five lakh passengers/day. Every day, over a million transactions are handled across 13,000 Aadhaar centres. In a nation, where queues are omnipresent at public service buildings, a burning need for intelligent digital interventions is obvious. India is expected to reach 627 million Internet users by 2019 end. This digital adoption is propelled by rural India, which registers 35 per cent annual growth. Why then is the common Indian not lapping up information from the many websites and apps launched by the Government of India?

Private one-to-one therapy can be prohibitively expensive; public waiting lists can be six months long and overprescription of antidepressants can become an unfortunate yet inevitable consequence of a mental healthcare system in crisis. It's a serious problem and, according to Minister of State for Mental Health Jim Daly, it's a problem that requires a "radical" solution. Last year Daly announced that the HSE are rolling out an eMental health strategy to address the mental health services staff shortage. The support project will include an instant messaging active listening service and the piloting of online therapy initiatives - and it can all be accessed from the comfort of your sitting room. Ireland isn't the first country to embrace eMental health.