Welcome to the 6th International Conference on Digital Health (www.acm-digitalhealth.org), held in conjunction with the 25th International World Wide Web Conference (WWW 2016) and incooperation with ACM Special Interest Group on Management of Data (SIGMOD) and Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) in Montreal, Canada from 11th April 2016 until 13th April 2016. Following a great success of eHealth 2008 in London, 2009 in Istanbul, 2010 in Casablanca and ehealth 2011 in Malaga with high profile presence from WHO and ECDC, and following on International Workshop on Public Health in the Digital Age (1st PHDA 20113 and 2nd PHDA 2014) building a community of public health informatics professionals, the 5th DH 2015 provided a major re-launch of this prime international interdisciplinary event for the first time co-located with WWW 2015 in Florence, Italy bringing together frontline public health professionals and computer science researches in data mining, crowdsourcing and Big Data analysis for public health surveillance. Following the successful model from 2015 we organized the programme into more independent Tracks and arranged the proceedings to be printed by ACM Digital Library. Building on the successful collocation with WWW 2015, this year DH 2016 promises to attract computer scientists attending WWW 2016 to public health data management and analytics challenges, and we are also inviting a wider industry, start-ups and medical audience. We have a great academic programme including 8 full research papers, 15 short papers, 4 extended abstracts, 23 posters and a line-up of industry and healthcare speakers confirmed. The DH 2016 conference is following its predecessors on social media. You can follow our Twitter account (@eHealthconf) for the latest updates. We welcome online discussion and feedback - the official hashtag for the conference is #DH2016. We also have a Facebook page at http://www.facebook.com/eHealthConf. And please take a look at our Flickr page for the poster presentations at https://www.flickr.com/groups/digitalhealth2016/. This year we are repeating a very popular start-up event to bring together the academic, industry, start up and medical audiences in an effective and enjoyable way. We are also including a special PhD Track for the first time to provide feedback and mentoring advice to PhD students as well as students-aimed "Health challenge" to get hands-on experience with health data and intervention design in interdisciplinary groups.
{"title":"Proceedings of the 6th International Conference on Digital Health Conference","authors":"P. Kostkova, F. Grasso, Carlos Castillo","doi":"10.1145/2896338","DOIUrl":"https://doi.org/10.1145/2896338","url":null,"abstract":"Welcome to the 6th International Conference on Digital Health (www.acm-digitalhealth.org), held in conjunction with the 25th International World Wide Web Conference (WWW 2016) and incooperation with ACM Special Interest Group on Management of Data (SIGMOD) and Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) in Montreal, Canada from 11th April 2016 until 13th April 2016. \u0000 \u0000Following a great success of eHealth 2008 in London, 2009 in Istanbul, 2010 in Casablanca and ehealth 2011 in Malaga with high profile presence from WHO and ECDC, and following on International Workshop on Public Health in the Digital Age (1st PHDA 20113 and 2nd PHDA 2014) building a community of public health informatics professionals, the 5th DH 2015 provided a major re-launch of this prime international interdisciplinary event for the first time co-located with WWW 2015 in Florence, Italy bringing together frontline public health professionals and computer science researches in data mining, crowdsourcing and Big Data analysis for public health surveillance. Following the successful model from 2015 we organized the programme into more independent Tracks and arranged the proceedings to be printed by ACM Digital Library. \u0000 \u0000Building on the successful collocation with WWW 2015, this year DH 2016 promises to attract computer scientists attending WWW 2016 to public health data management and analytics challenges, and we are also inviting a wider industry, start-ups and medical audience. We have a great academic programme including 8 full research papers, 15 short papers, 4 extended abstracts, 23 posters and a line-up of industry and healthcare speakers confirmed. \u0000 \u0000The DH 2016 conference is following its predecessors on social media. You can follow our Twitter account (@eHealthconf) for the latest updates. We welcome online discussion and feedback - the official hashtag for the conference is #DH2016. We also have a Facebook page at http://www.facebook.com/eHealthConf. And please take a look at our Flickr page for the poster presentations at https://www.flickr.com/groups/digitalhealth2016/. \u0000 \u0000This year we are repeating a very popular start-up event to bring together the academic, industry, start up and medical audiences in an effective and enjoyable way. We are also including a special PhD Track for the first time to provide feedback and mentoring advice to PhD students as well as students-aimed \"Health challenge\" to get hands-on experience with health data and intervention design in interdisciplinary groups.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127049423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: e-Learning, Edutaiment and Serious Games for Health","authors":"B. Manjón","doi":"10.1145/3257760","DOIUrl":"https://doi.org/10.1145/3257760","url":null,"abstract":"","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128900199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patrick D. Howell, Layla D. Martin, Hesamoddin Salehian, Chul Lee, Kyler M. Eastman, Joohyun Kim
It is well known that in a balanced diet, eating the right amount of calories and nutrients to maintain a healthy weight is important for one's physical wellness and health. Thus, understanding demographic and behavior patterns of food consumption is a topic that several researchers in public health have long pursued. In this paper, we study how perceived food tastes, which are known to affect palatability of foods, are related to the dynamics and nature of population-wide dietary preferences and patterns over demographics, time, and location. In contrast to previous studies that have been clinical in nature based on small samples of participants via treatment data, our study offers a more ``big data''-style approach by leveraging a massive collection of food items and entries from the MyFitnessPal user base. Despite its differences from traditional research, our findings actually validate some previous studies that correlate food taste with certain population groups or public health patterns. In addition, we are able to extend research into previously unexploited directions.
{"title":"Analyzing Taste Preferences From Crowdsourced Food Entries","authors":"Patrick D. Howell, Layla D. Martin, Hesamoddin Salehian, Chul Lee, Kyler M. Eastman, Joohyun Kim","doi":"10.1145/2896338.2896358","DOIUrl":"https://doi.org/10.1145/2896338.2896358","url":null,"abstract":"It is well known that in a balanced diet, eating the right amount of calories and nutrients to maintain a healthy weight is important for one's physical wellness and health. Thus, understanding demographic and behavior patterns of food consumption is a topic that several researchers in public health have long pursued. In this paper, we study how perceived food tastes, which are known to affect palatability of foods, are related to the dynamics and nature of population-wide dietary preferences and patterns over demographics, time, and location. In contrast to previous studies that have been clinical in nature based on small samples of participants via treatment data, our study offers a more ``big data''-style approach by leveraging a massive collection of food items and entries from the MyFitnessPal user base. Despite its differences from traditional research, our findings actually validate some previous studies that correlate food taste with certain population groups or public health patterns. In addition, we are able to extend research into previously unexploited directions.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128650572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advances in mobile and computer technology are combining to create massive changes in the way data about human health and well-being are gathered and used. As the trend toward wearable and ubiquitous health tracking devices moves forward, the sheer quantity of new data from a wide variety of devices presents challenges for analysts. In the coming years, this data will inevitably be used in the criminal and civil justice systems. However, the tools to make full use of it are currently lacking. This paper discusses scenarios where data collected from health and fitness related devices may intersect with legal requirements such as investigations into insurance fraud or even murder. The conclusion is that there is much work to be done to enable reliable investigations. This should include at least the establishment of an organization to promote development of the field, development of cross-disciplinary education materials, and the creation of an open data bank for information sharing.
{"title":"Medical Device Data Goes to Court","authors":"D. Vandervort","doi":"10.1145/2896338.2896341","DOIUrl":"https://doi.org/10.1145/2896338.2896341","url":null,"abstract":"Advances in mobile and computer technology are combining to create massive changes in the way data about human health and well-being are gathered and used. As the trend toward wearable and ubiquitous health tracking devices moves forward, the sheer quantity of new data from a wide variety of devices presents challenges for analysts. In the coming years, this data will inevitably be used in the criminal and civil justice systems. However, the tools to make full use of it are currently lacking. This paper discusses scenarios where data collected from health and fitness related devices may intersect with legal requirements such as investigations into insurance fraud or even murder. The conclusion is that there is much work to be done to enable reliable investigations. This should include at least the establishment of an organization to promote development of the field, development of cross-disciplinary education materials, and the creation of an open data bank for information sharing.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123239831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tauhidur Rahman, M. Czerwinski, Ran Gilad-Bachrach, Paul Johns
Various wearable sensors capturing body vibration, jaw movement, hand gesture, etc., have shown promise in detecting when one is currently eating. However, based on existing literature and user surveys conducted in this study, we argue that a Just-in-Time eating intervention, triggered upon detecting a current eating event, is sub-optimal. An eating intervention triggered at "About-to-Eat" moments could provide users with a further opportunity to adopt a better and healthier eating behavior. In this work, we present a wearable sensing framework that predicts "About-to-Eat" moments and the "Time until the Next Eating Event". The wearable sensing framework consists of an array of sensors that capture physical activity, location, heart rate, electrodermal activity, skin temperature and caloric expenditure. Using signal processing and machine learning on this raw multimodal sensor stream, we train an "About-to-Eat" moment classifier that reaches an average recall of 77%. The "Time until the Next Eating Event" regression model attains a correlation coefficient of 0.49. Personalization further increases the performance of both of the models to an average recall of 85% and correlation coefficient of 0.65. The contributions of this paper include user surveys related to this problem, the design of a system to predict about to eat moments and a regression model used to train multimodal sensory data in real time for potential eating interventions for the user.
{"title":"Predicting \"About-to-Eat\" Moments for Just-in-Time Eating Intervention","authors":"Tauhidur Rahman, M. Czerwinski, Ran Gilad-Bachrach, Paul Johns","doi":"10.1145/2896338.2896359","DOIUrl":"https://doi.org/10.1145/2896338.2896359","url":null,"abstract":"Various wearable sensors capturing body vibration, jaw movement, hand gesture, etc., have shown promise in detecting when one is currently eating. However, based on existing literature and user surveys conducted in this study, we argue that a Just-in-Time eating intervention, triggered upon detecting a current eating event, is sub-optimal. An eating intervention triggered at \"About-to-Eat\" moments could provide users with a further opportunity to adopt a better and healthier eating behavior. In this work, we present a wearable sensing framework that predicts \"About-to-Eat\" moments and the \"Time until the Next Eating Event\". The wearable sensing framework consists of an array of sensors that capture physical activity, location, heart rate, electrodermal activity, skin temperature and caloric expenditure. Using signal processing and machine learning on this raw multimodal sensor stream, we train an \"About-to-Eat\" moment classifier that reaches an average recall of 77%. The \"Time until the Next Eating Event\" regression model attains a correlation coefficient of 0.49. Personalization further increases the performance of both of the models to an average recall of 85% and correlation coefficient of 0.65. The contributions of this paper include user surveys related to this problem, the design of a system to predict about to eat moments and a regression model used to train multimodal sensory data in real time for potential eating interventions for the user.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122482960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiongqian Liang, David Fuhry, David Maung, Alexandra L Borstad, R. Crawfis, Lynne V. Gauthier, Arnab Nandi, S. Parthasarathy
Stroke is a major cause of hemiparesis in United States. Constraint--Induced Movement therapy (CI therapy) is an effective treatment for upper extremity hemiparesis; however it is inaccessible to most patients. To make it more accessible, we developed a game-based rehabilitation system incorporating the major rehabilitation principles from CI therapy. We introduce a data analytics framework for our rehabilitation system in this paper that can provide objective measures of motor performance during gameplay. We design techniques of preprocessing collected data and propose a series of kinematic measurements, which are used to assess the motor performance and supplement in-clinic measures of therapeutic effect. We also present contextual filtering techniques to enable comparing movement production under different conditions, e.g., self-paced versus game-paced movement. We apply our data analytics framework on data collected from several participants. Our analysis shows that participants' motor movement improves over the period of treatment, with different participants showing different patterns of improvement, e.g., speed versus range of motion. Results of kinematic measurements during gameplay are highly consistent with in-clinic performance based on the Wolf Motor Function Test. Moreover, our fine-grained trend analysis reveals potential to detect fatigue, which is related to the duration of gameplay.
{"title":"Data Analytics Framework for A Game-based Rehabilitation System","authors":"Jiongqian Liang, David Fuhry, David Maung, Alexandra L Borstad, R. Crawfis, Lynne V. Gauthier, Arnab Nandi, S. Parthasarathy","doi":"10.1145/2896338.2896356","DOIUrl":"https://doi.org/10.1145/2896338.2896356","url":null,"abstract":"Stroke is a major cause of hemiparesis in United States. Constraint--Induced Movement therapy (CI therapy) is an effective treatment for upper extremity hemiparesis; however it is inaccessible to most patients. To make it more accessible, we developed a game-based rehabilitation system incorporating the major rehabilitation principles from CI therapy. We introduce a data analytics framework for our rehabilitation system in this paper that can provide objective measures of motor performance during gameplay. We design techniques of preprocessing collected data and propose a series of kinematic measurements, which are used to assess the motor performance and supplement in-clinic measures of therapeutic effect. We also present contextual filtering techniques to enable comparing movement production under different conditions, e.g., self-paced versus game-paced movement. We apply our data analytics framework on data collected from several participants. Our analysis shows that participants' motor movement improves over the period of treatment, with different participants showing different patterns of improvement, e.g., speed versus range of motion. Results of kinematic measurements during gameplay are highly consistent with in-clinic performance based on the Wolf Motor Function Test. Moreover, our fine-grained trend analysis reveals potential to detect fatigue, which is related to the duration of gameplay.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131189108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates whether infectious intestinal diseases (IIDs) can be detected and quantified using social media content. Experiments are conducted on user-generated data from the microblogging service, Twitter. Evaluation is based on the comparison with the number of IID cases reported by traditional health surveillance methods. We employ a deep learning approach for creating a topical vocabulary, and then apply a regularised linear (Elastic Net) as well as a nonlinear (Gaussian Process) regression function for inference. We show that like previous text regression tasks, the nonlinear approach performs better. In general, our experimental results, both in terms of predictive performance and semantic interpretation, indicate that Twitter data contain a signal that could be strong enough to complement conventional methods for IID surveillance.
{"title":"On Infectious Intestinal Disease Surveillance using Social Media Content","authors":"Bin Zou, Vasileios Lampos, R. Gorton, I. Cox","doi":"10.1145/2896338.2896372","DOIUrl":"https://doi.org/10.1145/2896338.2896372","url":null,"abstract":"This paper investigates whether infectious intestinal diseases (IIDs) can be detected and quantified using social media content. Experiments are conducted on user-generated data from the microblogging service, Twitter. Evaluation is based on the comparison with the number of IID cases reported by traditional health surveillance methods. We employ a deep learning approach for creating a topical vocabulary, and then apply a regularised linear (Elastic Net) as well as a nonlinear (Gaussian Process) regression function for inference. We show that like previous text regression tasks, the nonlinear approach performs better. In general, our experimental results, both in terms of predictive performance and semantic interpretation, indicate that Twitter data contain a signal that could be strong enough to complement conventional methods for IID surveillance.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114469977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karthik Srinivasan, Faiz Currim, S. Ram, Casey Lindberg, Esther Sternberg, Perry Skeath, B. Najafi, J. Razjouyan, Hyo-Ki Lee, Colin Foe-Parker, Nicole Goebel, Reuben Herzl, M. Mehl, Brian Gilligan, J. Heerwagen, Kevin Kampschroer, Kelli Canada
With rapid development of sensor technologies and the internet of things, research in the area of connected health is increasing in importance and complexity with wide-reaching impacts for public health. As data sources such as mobile (wearable) sensors get cheaper, smaller, and smarter, important research questions can be answered by combining information from multiple data sources. However, integration of multiple heterogeneous data streams often results in a dataset with several empty cells or missing values. The challenge is to use such sparsely populated integrated datasets without compromising model performance. Naïve approaches for dataset modification such as discarding observations or ad-hoc replacement of missing values often lead to misleading results. In this paper, we discuss and evaluate current best-practices for modeling such data with missing values and then propose an ensemble-learning based sparse-data modeling framework. We develop a predictive model using this framework and compare it with existing models using a study in a healthcare setting. Instead of generating a single score on variable/feature importance, our framework enables the user to understand the importance of a variable based on the existing data values and their localized impact on the outcome.
{"title":"Feature Importance and Predictive Modeling for Multi-source Healthcare Data with Missing Values","authors":"Karthik Srinivasan, Faiz Currim, S. Ram, Casey Lindberg, Esther Sternberg, Perry Skeath, B. Najafi, J. Razjouyan, Hyo-Ki Lee, Colin Foe-Parker, Nicole Goebel, Reuben Herzl, M. Mehl, Brian Gilligan, J. Heerwagen, Kevin Kampschroer, Kelli Canada","doi":"10.1145/2896338.2896347","DOIUrl":"https://doi.org/10.1145/2896338.2896347","url":null,"abstract":"With rapid development of sensor technologies and the internet of things, research in the area of connected health is increasing in importance and complexity with wide-reaching impacts for public health. As data sources such as mobile (wearable) sensors get cheaper, smaller, and smarter, important research questions can be answered by combining information from multiple data sources. However, integration of multiple heterogeneous data streams often results in a dataset with several empty cells or missing values. The challenge is to use such sparsely populated integrated datasets without compromising model performance. Naïve approaches for dataset modification such as discarding observations or ad-hoc replacement of missing values often lead to misleading results. In this paper, we discuss and evaluate current best-practices for modeling such data with missing values and then propose an ensemble-learning based sparse-data modeling framework. We develop a predictive model using this framework and compare it with existing models using a study in a healthcare setting. Instead of generating a single score on variable/feature importance, our framework enables the user to understand the importance of a variable based on the existing data values and their localized impact on the outcome.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126440167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Mark, M. Czerwinski, Shamsi T. Iqbal, Paul Johns
Positive wellbeing in the workplace is tied to better health. However, lack of wellbeing in the workplace is a serious problem in the U.S, is rising continually, and can lead to poor health conditions. In this study we investigate factors that might be associated with workplace wellbeing. We report on an in situ study in the workplace of 40 information workers whose mood was tracked for 12 days. We used a mixed-methods study using Fitbit actigraphs to measure sleep and physical activity, computer logging, and repeated daily surveys. We found that sleep and perceived productivity are positively correlated with affect balance (the balance of positive and negative affect), whereas concentration difficulty, and amount of time on workplace email, are negatively correlated with affect balance. Our model explains 48% of the variance of workplace mood. We discuss the value and challenges of multi-faceted measures of health as we move towards designing interdisciplinary digital health research.
{"title":"Workplace Indicators of Mood: Behavioral and Cognitive Correlates of Mood Among Information Workers","authors":"G. Mark, M. Czerwinski, Shamsi T. Iqbal, Paul Johns","doi":"10.1145/2896338.2896360","DOIUrl":"https://doi.org/10.1145/2896338.2896360","url":null,"abstract":"Positive wellbeing in the workplace is tied to better health. However, lack of wellbeing in the workplace is a serious problem in the U.S, is rising continually, and can lead to poor health conditions. In this study we investigate factors that might be associated with workplace wellbeing. We report on an in situ study in the workplace of 40 information workers whose mood was tracked for 12 days. We used a mixed-methods study using Fitbit actigraphs to measure sleep and physical activity, computer logging, and repeated daily surveys. We found that sleep and perceived productivity are positively correlated with affect balance (the balance of positive and negative affect), whereas concentration difficulty, and amount of time on workplace email, are negatively correlated with affect balance. Our model explains 48% of the variance of workplace mood. We discuss the value and challenges of multi-faceted measures of health as we move towards designing interdisciplinary digital health research.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129499731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenli Zhang, S. Ram, Mark Burkart, Yolande Pengetnze
Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.
{"title":"Extracting Signals from Social Media for Chronic Disease Surveillance","authors":"Wenli Zhang, S. Ram, Mark Burkart, Yolande Pengetnze","doi":"10.1145/2896338.2897728","DOIUrl":"https://doi.org/10.1145/2896338.2897728","url":null,"abstract":"Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114838274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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