Mealtime prediction using wearable insulin pump data to support diabetes management.

Many patients with diabetes struggle with post-meal high blood glucose due to missed or untimely meal-related insulin doses. To address this challenge, our research aims to: (1) study mealtime patterns in patients with type 1 diabetes using wearable insulin pump data, and (2) develop personalized models for predicting future mealtimes to support timely insulin dose administration. Using two independent datasets with over 45,000 meal logs from 82 patients with diabetes, we find that the majority of people ( 60%) have irregular and inconsistent mealtime patterns that change notably through the course of each day and across months in their own historical data.

DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment.

A comprehensive assessment of cigarette smoking behavior and its effect on health requires a detailed examination of smoke exposure. We propose a CNN-LSTM-based deep learning architecture named DeepPuff to quantify Respiratory Smoke Exposure Metrics (RSEM). Smoke inhalations were detected from the breathing and hand gesture sensors of the Personal Automatic Cigarette Tracker v2 (PACT 2.

Understanding temporal changes and seasonal variations in glycemic trends using wearable data.

Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years.

An Examination of Objective and Self-Report Measures of Ad Libitum Electronic Cigarette Use: Identifying Patterns of Puffing Behavior and Evaluating Self-Report Items.

Introduction: There has been little research objectively examining use-patterns among individuals who use electronic cigarettes (e-cigarettes). The primary aim of this study was to identify patterns of e-cigarette use and categorize distinct use-groups by analyzing patterns of puff topography variables over time. The secondary aim was to identify the extent to which self-report questions about use accurately assess e-cigarette use-behavior.

Analyzing Impact of Mouthpiece-based Puff Topography Devices on Smoking Behavior using Wearable Sensors.

Detailed assessment of smoking topography (puffing and post-puffing metrics) can lead to a better understanding of factors that influence tobacco use. Research suggests that portable mouthpiece-based devices used for puff topography measurement may alter natural smoking behavior. This paper evaluated the impact of a portable puff topography device (CReSS Pocket) on puffing & post-puffing topography using a wearable system, the Personal Automatic Cigarette Tracker v2 (PACT 2.

Investigating Temporal Patterns of Glycemic Control around Holidays.

Maintaining good glycemic control is a central part of diabetes care. However, it can be a tedious task because many factors in daily living can affect glycemic control. To support management, a growing number of people living with diabetes are now being prescribed continuous glucose monitors (CGMs) for real-time tracking of their blood glucose levels.

Electromyogram in Cigarette Smoking Activity Recognition.

In this study, information from surface electromyogram (sEMG) signals was used to recognize cigarette smoking. The sEMG signals collected from lower arm were used in two different ways: (1) as an individual predictor of smoking activity and (2) as an additional sensor/modality along with the inertial measurement unit (IMU) to augment recognition performance. A convolutional and a recurrent neural network were utilized to recognize smoking-related hand gestures.

A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors.

A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors.

Objective Detection of Cigarette Smoking from Physiological Sensor Signals.

Cigarette smoking has severe health impacts on those who smoke and the people around them. Several wearable sensing modalities have recently been investigated to collect objective data on daily smoking, including detection of smoking episodes from breathing patterns, hand to mouth behavior, and characteristic hand gestures or cigarette lighting events. In order to provide new insight into ongoing research on the objective collection of smoking-related events, this paper proposes a novel method to identify smoking events from the associated changes in heart rate parameters specific to smoking.

A Comparison of SVM and CNN-LSTM Based Approach for Detecting Smoke Inhalations from Respiratory signal.

Wearable sensors have successfully been used in recent studies to monitor cigarette smoking events and analyze people's smoking behavior. Respiratory inductive plethysmography (RIP) has been employed to track breathing and to identify characteristic breathing pattern specific to smoking. Pattern recognition algorithms such as Support Vector Machine (SVM), Hidden Markov Model, Decision tree, or ensemble approaches have been used to identify smoke inhalations.

Computation of Cigarette Smoke Exposure Metrics From Breathing.

Traditional metrics of smoke exposure in cigarette smokers are derived either from self-report, biomarkers, or puff topography. Methods involving biomarkers measure concentrations of nicotine, nicotine metabolites, or carbon monoxide. Puff-topography methods employ portable instruments to measure puff count, puff volume, puff duration, and inter-puff interval.

Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study.

Introduction: Wearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior. The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool.

Smoking detection based on regularity analysis of hand to mouth gestures.

A number of studies have been introduced for the detection of smoking via a variety of features extracted from the wrist IMU data. However, none of the previous studies investigated gesture regularity as a way to detect smoking events. This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures.

Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter.

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.

Can Thrifty Gene(s) or Predictive Fetal Programming for Thriftiness Lead to Obesity?

Obesity and related disorders are thought to have their roots in metabolic "thriftiness" that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research.

The co-optimization of floral display and nectar reward.

In most insect-pollinated flowers, pollinators cannot detect the presence of nectar without entering the flower. Therefore, flowers may cheat by not producing nectar and may still get pollinated. Earlier studies supported this 'cheater flower' hypothesis and suggested that the cost saving by cheater flowers could be the most predominant selective force in the evolution of nectarless flowers.

Metabolic syndrome: aggression control mechanisms gone out of control.

An upcoming hypothesis about the evolutionary origins of metabolic syndrome is that of a 'soldier' to 'diplomat' transition in behaviour and the accompanying metabolic adaptations. Theoretical as well as empirical studies have shown that similar to the soldier and diplomat dichotomy, physically aggressive and non-aggressive strategists coexist in animal societies with negative frequency dependent selection. Although dominant individuals have a higher reproductive success obtained through means such as greater access to females, subordinate individuals have alternative means such as sneak-mating for gaining a substantial reproductive success.

Obesity as a perceived social signal.

Fat accumulation has been classically considered as a means of energy storage. Obese people are theorized as metabolically 'thrifty', saving energy during times of food abundance. However, recent research has highlighted many neuro-behavioral and social aspects of obesity, with a suggestion that obesity, abdominal obesity in particular, may have evolved as a social signal.