Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio

TL;DR

This study develops a neural network model trained on large-scale sleep diary data to predict individual sleep quality and generate personalized sleep habit recommendations, outperforming standard methods and providing explainability for clinical use.

Contribution

The paper introduces a neural network approach trained on extensive sleep diary data to personalize sleep recommendations and improve prediction accuracy over traditional statistical methods.

Findings

Neural network outperforms standard statistical models in predicting sleep quality.

Personalized recommendations significantly improve sleep quality compared to standard methods.

Model provides explainability and confidence intervals for clinical adoption.

Abstract

SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.