Sedentary time, physical activity and cardiometabolic health:accelerometry-based study in the Northern Finland Birth Cohort 1966

Abstract The popularity of accelerometer-based activity monitors has been associated with several analytical challenges, including how to quantify accelerometer outputs in terms of sedentary behavior, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Recently, machine learning (ML) approaches have been coupled with raw accelerometry to classify activities by intensity, but the generalizability of ML models outside of the development datasets remains poorly understood. Currently, the health benefits of meeting the recommended amounts of sleep and MVPA in adults are well documented, but the cardiometabolic health implications of sedentary time and LPA are still unclear. The present study reviewed studies calibrating and validating wearable accelerometers using ML approaches and preformed cross-dataset tests to evaluate the generalization performance of ML models for classifying activity intensities from raw acceleration data. Additionally, the latest follow-up in the Northern Finland Birth Cohort 1966 study (n = 5,840) at age 46 years included measurement of daily activities for two weeks with two accelerometers. This data was used to examine how the levels and patterns of accelerometer-estimated activity intensities (sedentary behavior, LPA, and MVPA) are associated with cardiometabolic health in this large sample of middle-aged adults, and to create a data-driven hierarchy predicting their activity behaviors. Based on the study, ML techniques can classify activities in terms of type, category, or intensity with acceptable accuracy irrespective of accelerometer placement. However, ML models developed with raw acceleration data for classifying activity intensities (sedentary behavior, LPA, and MVPA) are not generalizable to other populations monitored with different accelerometers, suggesting that further strategies are needed to enhance their generalizability. The study suggests that adults, in addition to MVPA, may also gain cardiometabolic health benefits through LPA, particularly when it replaces sedentary time. Finally, the data-driven hierarchy of correlates created consisted of factors of relative importance, and can potentially be used to target and tailor interventions.Tiivistelmä Nykyään hyvin suosittujen kiihtyvyysanturiin perustuvien aktiivisuusmittareiden keräämän datan analysointiin liittyy monia haasteita, kuten paikallaanolon, kevyen liikunnan sekä keskiraskaan ja raskaan liikunnan tarkan määrän määrittäminen. Viime aikoina on otettu käyttöön koneoppimismenetelmiä kiihtyvyysanturin tuottaman raakasignaalin analysoinnissa luokittelemaan liikettä sen intensiteetin perusteella, mutta toistaiseksi näiden menetelmien yleistettävyys on huonosti tiedossa. Nykyisin tiedetään aika hyvin ne terveyshyödyt, joita saadaan, jos noudatetaan unen sekä keskiraskaan ja raskaan liikunnan suosituksia. Paikallaanolon ja kevyen liikunnan vaikutukset sydän- ja verisuoniterveyteen ovat kuitenkin heikommin tiedossa. Tässä tutkimuksessa tehtiin systemaattinen kirjallisuuskatsaus koneoppimismenetelmien käytöstä kannettavien kiihtyvyysanturien kalibroinnissa ja validoinnissa. Työssä testattiin koneoppimismenetelmien yleistettävyyttä fyysisen aktiivisuuden intensiteetin luokitteluun kiihtyvyysanturin antaman raakadatan perusteella yhdistäen useita toisistaan riippumattomia mittausaineistoja. Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotisaineistonkeruussa (n = 5,840) oli mitattu liikunta-aktiivisuutta kahdella kiihtyvyysanturilla. Tämän mittaustiedon avulla tutkittiin sitä, kuinka kiihtyvyysanturilla mitattu fyysisen aktiivisuuden intensiteetti (paikallaanolo, kevyt liikunta sekä keskiraskas ja raskas liikunta) ja eri intensiteetillä toteutetun aktiivisuuden jakautuminen vuorokauden sisällä ovat yhteydessä keski-ikäisten sydänterveyteen. Lisäksi luotiin aineiston perusteella hierarkinen malli ennustamaan liikuntakäyttäytymistä. Tutkimuksen perusteella koneoppimistekniikoiden avulla voidaan riittävällä tarkkuudella luokitella fyysistä aktiivisuutta liikuntamuodon, intensiteetin ja eri intensiteettien jakautumisen perusteella riippumatta kiihtyvyysanturin sijainnista. Kiihtyvyysanturin tuottamaan raakadataan perustuvat fyysisen aktiivisuuden intensiteetin luokitteluun kehitetyt koneoppimismallit eivät ole kuitenkaan yleistettävissä muihin väestöryhmiin, joissa on käytetty erilaisia kiihtyvyysantureita, vaan tarvitaan lisätutkimusta parantamaan mallien yleistettävyyttä. Tutkimuksen perusteella keskiraskaan ja raskaan liikunnan lisäksi kevytkin liikunta-aktiivisuus, erityisesti jos se korvaa paikallaan oloa, on yhteydessä aikuisten parempaan sydänterveyteen. Aineiston perusteella luotu hierarkinen malli antoi tietoa useista sydänterveyttä edistävistä tekijöistä ja sitä voidaan käyttää liikuntainterventioiden räätälöinnissä

A novel time-aware food recommender-system based on deep learning and graph clustering

Abstract Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user. We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG. Experiments using dataset extracted from “Allrecipes.com” demonstrated that the developed food recommender-system performed best

Community detection algorithms in healthcare applications:a systematic review

Abstract Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed

AccNet24:a deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

Abstract Objective: Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers. Methods: Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18–91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories. Results: Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%–30% on unseen data. Conclusion: AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction

Calibration and validation of accelerometer-based activity monitors:a systematic review of machine-learning approaches

Abstract Background: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions. Method: We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles. Results: A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure. Conclusions: It appears that various ML-based techniques together with raw acceleration data sampled at 20–30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals

Machine-learning models for activity class prediction:a comparative study of feature selection and classification algorithms

Abstract Purpose: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data. Methods: The hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set. Results: The appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods. Conclusions: A subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes

Healthy food recommendation using a time-aware community detection approach and reliability measurement

Abstract Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model

Accumulation patterns of sedentary time and breaks and their association with cardiometabolic health markers in adults

Abstract Breaking up sedentary time with physical activity (PA) could modify the detrimental cardiometabolic health effects of sedentary time. Our aim was to identify profiles according to distinct accumulation patterns of sedentary time and breaks in adults, and to investigate how these profiles are associated with cardiometabolic outcomes. Participants (n = 4439) of the Northern Finland Birth Cohort 1966 at age 46 years wore a hip-worn accelerometer for 7 consecutive days during waking hours. Uninterrupted ≥1-min sedentary bouts were identified, and non-sedentary bouts in between two consecutive sedentary bouts were considered as sedentary breaks. K-means clustering was performed with 65 variables characterizing how sedentary time was accumulated and interrupted. Linear regression was used to determine the association of accumulation patterns with cardiometabolic health markers. Four distinct groups were formed as follows: “Couch potatoes” (n = 1222), “Prolonged sitters” (n = 1179), “Shortened sitters” (n = 1529), and “Breakers” (n = 509). Couch potatoes had the highest level of sedentariness and the shortest sedentary breaks. Prolonged sitters, accumulating sedentary time in bouts of ≥15–30 min, had no differences in cardiometabolic outcomes compared with Couch potatoes. Shortened sitters accumulated sedentary time in bouts lasting <15 min and performed more light-intensity PA in their sedentary breaks, and Breakers performed more light-intensity and moderate-to-vigorous PA. These latter two profiles had lower levels of adiposity, blood lipids, and insulin sensitivity, compared with Couch potatoes (1.1–25.0% lower values depending on the cardiometabolic health outcome, group, and adjustments for potential confounders). Avoiding uninterrupted sedentary time with any active behavior from light-intensity upwards could be beneficial for cardiometabolic health in adults

Evaluating and enhancing the generalization performance of machine learning models for physical activity intensity prediction from raw acceleration data

Abstract Purpose: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors. Method: Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within-dataset (leave-one-subject-out) cross-validation, and then cross-tested to other datasets with different accelerometers. To enhance the models’ generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation). Results: The datasets showed high performance in within-dataset cross-validation (accuracy 71.9–95.4%, Kappa K=0.63–0.94). The performance of the within-dataset validated models decreased when applied to datasets with different accelerometers (41.2–59.9%, K=0.21–0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9–83.7%, K=0.61–0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4–90.7%, K=0.68–0.89). Conclusions: Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within-dataset validation is not sufficient to understand the models’ performance on other populations with different accelerometers

A novel healthy and time-aware food recommender system using attributed community detection

Abstract Food recommendation systems aim to provide recommendations according to a user’s diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, which restricts their ability to generate healthy recommendations. This paper develops a novel health-aware food recommendation system that explicitly accounts for food ingredients, food categories, and the factor of time, predicting the user’s preference through time-aware collaborative filtering and a food ingredient content-based model. Based on the user's predicted preferences and the health factor of each food, our model provides final recommendations to the target user. The performance of our model was compared to several state-of-the-art recommender systems in terms of five distinct metrics: Precision, Recall, F1, AUC, and NDCG. Experimental analysis of datasets extracted from the websites Allrecipes.com and Food.com demonstrated that our proposed food recommender system performs well compared to previous food recommendation models