Persuasive Technology for Digital Behavior Change Intervention Digital Behaviour Intervention, Wearable, Machine Learning, m-health, Personalisation

[Remi-Gau]

Added as an issue for book keeping

Source: https://www.brainhack-krakow.org/projects

Team leader

Aneta Lisowska
[email protected]
github anli66

Abstract:

Behavior change strategies aim to modify health risk behaviors such as physical inactivity, unhealthy eating and substance abuse to prevent the development of chronic diseases [1] and improve individuals physical and mental well-being [2]. Ubiquitous availability of mobile phones paired with wearable devices provides an opportunity for provision of digital behavior change interventions (DBCI). The effectiveness of DBCI depends on individuals adherence to the intervention. To facilitate adherence, it is important to provide the right support at the right time. The adequate support may include tailored content of notifications and personalised activity suggestions. The right time not only reflects the literal time of the day, but also takes into account the context of the individual (both internal, e.g. emotional state, and external, e.g. location). Machine learning methods can be used both to understand the patient’s internal context (e.g., classify emotions based on a signal captured by the wearable device [3]) and to personalize an intervention (e.g., tailor notification timing [4]). Brain hack participants will have the opportunity to develop methods that facilitate behaviour change. This may include predicting adherence to the activity suggestions, classifying emotional state from consumer-grade wearable1 and finding the conditions under which individuals are responsive to ’nudges’. The solutions to these problems may rely on simple statistical models or on more advance deep learning approaches. A successfully developed solution can be published and potentially even applied in real-life study!

[1] Dietz, W.H., Brownson, R.C., Douglas, C.E., Dreyzehner, J.J., Goetzel,R.Z., Gortmaker, S.L., Marks, J.S., Merrigan, K.A., Pate, R.R., Powell,L.M., et al.: Chronic disease prevention: Tobacco, physical activity, andnutrition for a healthy start: A vital direction for health and health care.NAM Perspectives (2016)
[2] Dale, H., Brassington, L., King, K.: The impact of healthy lifestyle inter-ventions on mental health and wellbeing: a systematic review. MentalHealth Review Journal (2014)
[3] Lisowska, A., Wilk, S., Peleg, M.: Catching patient’s attention at the righttime to help them undergo behavioural change: Stress classification exper-iment from blood volume pulse. In: International Conference on ArtificialIntelligence in Medicine, pp. 72–82 (2021). Springer
[4] Lisowska, A., Wilk, S., Peleg, M.: From personalized timely notificationto healthy habit formation: A feasibility study of reinforcement learn-ing approaches on synthetic data. In: Proceedings of the AIxIA 2021SMARTERCARE Workshop, CEUR-WS, pp. 7–18 (2021)
[5] Pinder, C., Vermeulen, J., Cowan, B.R., Beale, R.: Digital behaviourchange interventions to break and form habits. ACM Transactions onComputer-Human Interaction (TOCHI)25(3), 1–66 (2018)
[6] Eyal, N.: Hooked: How to Build Habit-forming Products. Penguin, ???(2014)
[7] Kahneman, D., Sibony, O., Sunstein, C.R.: Noise: a Flaw in HumanJudgment. Little, Brown, ??? (2021)
[8] Sapolsky, R.M.: Behave: The Biology of Humans at Our Best and Worst.Penguin, ??? (2017)
[9] Fogg, B.J.: Tiny Habits: the Small Changes that Change Everything.Eamon Dolan Books, ??? (2019)
[10] Shah, R.V., Grennan, G., Zafar-Khan, M., Alim, F., Dey, S., Ramanathan,D., Mishra, J.: Personalized machine learning of depressed mood usingwearables. Translational Psychiatry11(1), 1–18 (2021)
[11] Saganowski, S., Kazienko, P., Dziezyc, M., Dutkowiak, A., Polak, A., Dzi-adek, A., Ujma, M.: Review of consumer wearables in emotion, stress,meditation, sleep, and activity detection and analysis. arXiv preprintarXiv:2005.00093 (2020)
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List of materials:

The reading is aimed only as an inspiration (a bit of context for a project) and of course is not obligatory pre-requisite for participation.
• People after biology or psychology degree might like: Paper: [5], Books: [6–9] , Podcast: HubermanLab Podcast (episode ”Build strong habits”)
• People after electrical engineering or computer science degree could have a look at: Papers [10, 11], Presentation: Reinforcement Learning in Production
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List of requirements for taking part in the project:

1-3 people with bio-med background (including psychology, cognitive science) and knowledge of statistics (Regression, Tests of Significance, ANOVA etc.), 1-3 people with math or computer science background and knowledge of machine learning (SVM, Random Forest, Convolutions Neural Networks, Q-Learning), 1-3 people with physics or engineering background and knowledge of signal denoising approaches. Programming language of choice: Python.
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Maximal allowed number of team members: 9