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Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots and voice activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real time data capture of the interactions of users with the products and services. We can design what data are recorded, how and where it may be stored, and crucially, how it can be analyzed to reveal individual or collective usage patterns.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. Digital phenotyping was originally proposed to correlate a person’s mental state by using their metadata and even sensor data on their smartphone. In some cases, the data is physiological, for example pulse or movement-related, and it is collected automatically. In other cases, the data is actually metadata, for example, when a call is made and the call duration rather than the content of the call. Oftentimes, as would be expected from a personal device located on the body of the user, rich data pertaining to geo-location, social media use and interaction is gathered. Health and wellbeing-related, scientifically validated assessment scales may also generate digital phenotype data. Another form of digital phenotype data is Ecological Momentary Assessment (EMA), which originally made use of paper-diary techniques to enable people to record their observations or answers to specific questions and combined the ecological validity with the rigorous measurement techniques of psychometric research. EMA secures data about both behavioural and intrapsychic aspects of individuals&#39; daily activities, and it obtains reports about the experience as it occurs, thereby minimizing the effects of reliance on memory and reconstruction which can often be impaired by hindsight bias or recall bias.

The use of digital phenotyping data and its analysis using machine learning and artificial intelligence is important since many national public health organizations are exploring how to use digital technologies such as health apps and cloud-based services for the self-management of diseases and thus logging user interactions allows for greater insight into user needs and provides ideas for improving these digital interventions, for example through enhanced personalization. Public health services benefit since the data can be automatically and hence cost-effectively collected. Such data may facilitate new ways for digital epidemiological analyses and provide data to inform health policies. If the public health organizations promote health apps and digital phenotyping analysis using machine learning and artificial intelligence is taken up by these organizations, then there is clear need for guidelines on the ethical application of these ‘democratized’ algorithms and techniques.

My keynote talk begins by reviewing the evolution of the use of technology to support peoples’ health and wellbeing, from telecare and telehealth through to personalised healthcare, the growth of the idea of ‘quantified self’ and ultimately, self-managed care. I then discuss the growing use of commercially available digital devices and software for selfcare, and the explosion in the data arising from their use in society. The opportunities for the application of machine learning to the data, including EMA data are explored and the implications are discussed, across such areas as big data for research study design, ethics, the ‘servitization’ of machine learning, bias, surveillance, and health and wellbeing services. In order to illustrate my work, I will draw upon case studies from digital health and wellbeing, including maternal mental health, crisis helplines and apps for people living with dementia.

ICT4AWE 2020

Digital Phenotyping and Machine Learning in the Next Generation of Digital Health Technologies: Utilising Event Logging, Ecological Momentary Assessment &amp; Machine Learning

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots and voice activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real time data capture of the interactions of users with the products and services. We can design what data are recorded, how and where it may be stored, and crucially, how it can be analyzed to reveal individual or collective usage patterns.

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. Digital phenotyping was originally proposed to correlate a person’s mental state by using their metadata and even sensor data on their smartphone. In some cases, the data is physiological, for example pulse or movement-related, and it is collected automatically. In other cases, the data is actually metadata, for example, when a call is made and the call duration rather than the content of the call. Oftentimes, as would be expected from a personal device located on the body of the user, rich data pertaining to geo-location, social media use and interaction is gathered. Health and wellbeing-related, scientifically validated assessment scales may also generate digital phenotype data. Another form of digital phenotype data is Ecological Momentary Assessment (EMA), which originally made use of paper-diary techniques to enable people to record their observations or answers to specific questions and combined the ecological validity with the rigorous measurement techniques of psychometric research. EMA secures data about both behavioural and intrapsychic aspects of individuals' daily activities, and it obtains reports about the experience as it occurs, thereby minimizing the effects of reliance on memory and reconstruction which can often be impaired by hindsight bias or recall bias.

The use of digital phenotyping data and its analysis using machine learning and artificial intelligence is important since many national public health organizations are exploring how to use digital technologies such as health apps and cloud-based services for the self-management of diseases and thus logging user interactions allows for greater insight into user needs and provides ideas for improving these digital interventions, for example through enhanced personalization. Public health services benefit since the data can be automatically and hence cost-effectively collected. Such data may facilitate new ways for digital epidemiological analyses and provide data to inform health policies. If the public health organizations promote health apps and digital phenotyping analysis using machine learning and artificial intelligence is taken up by these organizations, then there is clear need for guidelines on the ethical application of these ‘democratized’ algorithms and techniques.

My keynote talk begins by reviewing the evolution of the use of technology to support peoples’ health and wellbeing, from telecare and telehealth through to personalised healthcare, the growth of the idea of ‘quantified self’ and ultimately, self-managed care. I then discuss the growing use of commercially available digital devices and software for selfcare, and the explosion in the data arising from their use in society. The opportunities for the application of machine learning to the data, including EMA data are explored and the implications are discussed, across such areas as big data for research study design, ethics, the ‘servitization’ of machine learning, bias, surveillance, and health and wellbeing services. In order to illustrate my work, I will draw upon case studies from digital health and wellbeing, including maternal mental health, crisis helplines and apps for people living with dementia.

Digital Phenotyping and Machine Learning in the Next Generation of Digital Health Technologies: Utilising Event Logging, Ecological Momentary Assessment & Machine Learning

keynote

The International Conference on Information and Communication Technologies for Ageing Well and e-Health aims to be a meeting point for those that study age and health-related quality of life and apply information and communication technologies for helping people stay healthier, more independent and active at work or in their community.

Digitalization is the societal change process in which new ICT-based solutions bring forward completely new ways of doing things, new businesses and new movements in the society. Digitalization also provides completely new ways of addressing issues related to life and work, health and care. But what does Digitalization mean and what effects may it have on our particular part of the society? How can we work with managing and leading digital transformation and how do we involve the people in this transformation process. This talk will provide interesting examples of digitalization, what effects it may have on work, on people on patients and relatives.

The Transforming Power of Digitalization

There is growing evidence that digital media usage can become problematic and ‘addictive’. Much research has focused on the role of user personal and social context in developing the problem, and little is known around the role of technology design in triggering and exacerbating the issue.
Digital media are equipped with powerful influence and persuasion techniques, which can increase users’ engagement and retention, but at the same time, can be questioned for hurting users’ wellness. Interestingly, technology offers an unprecedented opportunity for tools around assisting behavioural change and promoting a more regulated usage style. It can be designed to capture data around the (digital) behaviour and use them to derive interactive intervention techniques and issue them intelligently. The same approach can be thought for a range of human behaviours online and in real-life.
Challenges and risks in designing such tools are paramount, mainly due to the nature of subjects with problematic behaviour, e.g. denial, trivialisation of issue, the flight into health and relapse, and also due to the conflicting agendas and priorities in the production industry. This keynote will summarise the research around the topic and argue the case for Responsibility by Design concept in which tech companies are asked to empower users and their surrogate parties (social or technical) with data and tools to regulate their digital usage and be meaningfully informed about it and meet the emerging requirement of Digital Wellbeing. The speaker will present recent projects, conducted closely with charities and tech industry in UK and Europe, and the policy change achieved through them.

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