Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data

We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task.Comment: To appear in NIPS ML4H 2017 and NIPS TSW 201

Cross-Sectional and longitudinal associations of objectively-measured physical activity on blood pressure: evaluation in 37 countries

Background: We examined the cross-sectional and longitudinal associations of objectively measured physical activity (step counts) and blood pressure (BP) among adults spanning 37countries.Methods: Across 37 countries, we used data from a pool of 9238 adult owners of Withings’ Pulse activity trackers, which measures steps taken each day, and Wireless Blood Pressure Monitor, which measures BP. Analyses were adjusted on age, sex, number of days where the tracker was worn, and number of BP measurements. Data was collected from 2009 to 2013.Results: Subjects had a mean ± standard deviation (SD) age of 51.6 ± 11.3 years and a body mass index (BMI) of 28.7±5.5 kg/m2. A 1-month increase of more than 3000 steps per day was associated with a decrease of systolic BP (SBP) and diastolic BP (DBP) among the obese (1.57mm Hg and 1.29 mm Hg respectively, both P<0.001) and the overweight population (0.79mm Hg and 0.84 mm Hg respectively, both P≤0.001), but not in the normal weight population(P=0.60 and P=0.36 respectively).Conclusion: One-month increases in daily step counts was associated with a decrease of SBP and DBP in a large obese and overweight free living population

An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices

International audienceBackground: Sleep is a modifiable lifestyle factor that can be a target for efficient intervention studies to improve the quality of life and decrease the risk or burden of some chronic conditions. Knowing the profiles of individuals with poor sleep patterns is therefore a prerequisite. Wearable devices have recently opened new areas in medical research as potential efficient tools to measure lifestyle factors such as sleep quantity and quality.Objectives: The goal of our research is to identify the determinants of poor sleep based on data from a large population of users of connected devices.Methods: We analyzed data from 15,839 individuals (13,658 males and 2181 females) considered highly connected customers having purchased and used at least 3 connected devices from the consumer electronics company Withings (now Nokia). Total and deep sleep durations as well as the ratio of deep/total sleep as a proxy of sleep quality were analyzed in association with available data on age, sex, weight, heart rate, steps, and diastolic and systolic blood pressures.Results: With respect to the deep/total sleep duration ratio used as a proxy of sleep quality, we have observed that those at risk of having a poor ratio (≤0.40) were more frequently males (odds ratio [OR]female vs male=0.45, 95% CI 0.38-0.54), younger individuals (OR>60 years vs 18-30 years=0.47, 95% CI 0.35-0.63), and those with elevated heart rate (OR>78 bpm vs ≤61 bpm=1.18, 95% CI 1.04-1.34) and high systolic blood pressure (OR>133 mm Hg vs ≤116 mm Hg=1.22, 95% CI 1.04-1.43). A direct association with weight was observed for total sleep duration exclusively.Conclusions: Wearables can provide useful information to target individuals at risk of poor sleep. Future alert or mobile phone notification systems based on poor sleep determinants measured with wearables could be tested in intervention studies to evaluate the benefits