How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Design and implementation of an intelligent monitoring system for household added salt consumption in China based on a real-world study: a randomized controlled trial.

A high intake of salt is a major risk factor for cardiovascular diseases. Despite decades of effort to reduce salt consumption, the salt intake in China is still considerably above the recommended level. Thus, this study aims to design and implement an intelligent household added salt monitoring system (SALTCHECKER) to monitor and control added salt consumption in Chinese households. A randomized controlled trial will be conducted among households to test the effect of a SALTCHECKER in Chongqing, China. The test modalities are the SALTCHECKER (with a smart salt checker and a salt-limiting WeChat mini programme) compared to a salt checker (with only a weighing function). The effectiveness of the system will be investigated by assessing the daily added salt intake of each household member and the salt consumption-related knowledge, attitude and practice (KAP) of the household's main cook. Assessments will be performed at baseline and at 3 and 6 months. This study will be the first to explore the effect of the household added salt monitoring system on the reduction in salt intake in households. If the intelligent monitoring system is found to be effective in limiting household added salt consumption, it could provide scientific evidence on reducing salt consumption and preventing salt-related chronic diseases. Chinese clinical trial registry (Primary registry in the World Health Organization registry network): ChiCTR1800018586. Date of registration: September 25, 2018

“How Is My Child’s Asthma?” Digital Phenotype and Actionable Insights for Pediatric Asthma

Background: In the traditional asthma management protocol, a child meets with a clinician infrequently, once in 3 to 6 months, and is assessed using the Asthma Control Test questionnaire. This information is inadequate for timely determination of asthma control, compliance, precise diagnosis of the cause, and assessing the effectiveness of the treatment plan. The continuous monitoring and improved tracking of the child’s symptoms, activities, sleep, and treatment adherence can allow precise determination of asthma triggers and a reliable assessment of medication compliance and effectiveness. Digital phenotyping refers to moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices, in particular, mobile phones. The kHealth kit consists of a mobile app, provided on an Android tablet, that asks timely and contextually relevant questions related to asthma symptoms, medication intake, reduced activity because of symptoms, and nighttime awakenings; a Fitbit to monitor activity and sleep; a Microlife Peak Flow Meter to monitor the peak expiratory flow and forced exhaled volume in 1 second; and a Foobot to monitor indoor air quality. The kHealth cloud stores personal health data and environmental data collected using Web services. The kHealth Dashboard interactively visualizes the collected data. Objective: The objective of this study was to discuss the usability and feasibility of collecting clinically relevant data to help clinicians diagnose or intervene in a child’s care plan by using the kHealth system for continuous and comprehensive monitoring of child’s symptoms, activity, sleep pattern, environmental triggers, and compliance. The kHealth system helps in deriving actionable insights to help manage asthma at both the personal and cohort levels. The Digital Phenotype Score and Controller Compliance Score introduced in the study are the basis of ongoing work on addressing personalized asthma care and answer questions such as, “How can I help my child better adhere to care instructions and reduce future exacerbation?” Methods: The Digital Phenotype Score and Controller Compliance Score summarize the child’s condition from the data collected using the kHealth kit to provide actionable insights. The Digital Phenotype Score formalizes the asthma control level using data about symptoms, rescue medication usage, activity level, and sleep pattern. The Compliance Score captures how well the child is complying with the treatment protocol. We monitored and analyzed data for 95 children, each recruited for a 1- or 3-month-long study. The Asthma Control Test scores obtained from the medical records of 57 children were used to validate the asthma control levels calculated using the Digital Phenotype Scores. Results: At the cohort level, we found asthma was very poorly controlled in 37% (30/82) of the children, not well controlled in 26% (21/82), and well controlled in 38% (31/82). Among the very poorly controlled children (n=30), we found 30% (9/30) were highly compliant toward their controller medication intake—suggesting a re-evaluation for change in medication or dosage—whereas 50% (15/30) were poorly compliant and candidates for a more timely intervention to improve compliance to mitigate their situation. We observed a negative Kendall Tau correlation between Asthma Control Test scores and Digital Phenotype Score as −0.509 (P\u3c.01). Conclusions: kHealth kit is suitable for the collection of clinically relevant information from pediatric patients. Furthermore, Digital Phenotype Score and Controller Compliance Score, computed based on the continuous digital monitoring, provide the clinician with timely and detailed evidence of a child’s asthma-related condition when compared with the Asthma Control Test scores taken infrequently during clinic visits

Personalized Digital Phenotype Score, Healthcare Management and Intervention Strategies Using Knowledge Enabled Digital Health Framework for Pediatric Asthma

Asthma is a personalized, and multi-trigger respiratory condition which requires continuous monitoring and management of symptoms and medication adherence. We developed kHealth: Knowledge-enabled Digital Healthcare Framework to monitor and manage the asthma symptoms, medication adherence, lung function, daily activity, sleep quality, indoor, and outdoor environmental triggers of pediatric asthma patients. The kHealth framework collects up to 1852 data points per patient per day. It is practically impossible for the clinicians, parents, and the patient to analyze this vast amount of multimodal data collected from the kHealth framework. In this chapter, we describe the personalized scores, clinically relevant asthma categorization using digital phenotype score, actionable insights, and potential intervention strategies for better pediatric asthma management

Data imputation and body weight variability calculation using linear and non-linear methods in data collected from digital smart scales: a simulation and validation study

Background: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Results: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. Conclusions: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data

Wearable Structured Mental-Sensing-Graph Measurement

The emotional assessment under internet of things (IoT) architecture can support researchers to establish the relationships between human social and physiological signals and emotions. In this paper, a wearable emotion sensing system is developed under narrow band internet of things (NB-IoT) wireless communication technology. The wearable sensing device integrates social linked sensors including voice, activity, and heart rate. Using this system, a dating experiment is set up to investigate multimodal factors of male’s attractiveness perception. In particular, the multimodal data are fused in a graph structure, and this further leads to a graph convolutional neural networks model for emotion evaluation and a mental-sensing-graph intelligent interpreter. Different types of mental-sensing-graphs are fused during the training stage, and the model achieves a verification accuracy of 0.93. The intrinsic relationships among the multimodal data have been captured by the subgraphs which have star-shaped structures, and the center of the subgraphs are mostly audio node. The obtained results show that the attractiveness perception of the male participants in dating is more aligned to language communication. The results also reveal that when the male participants date highly attractive women during the experiments, a significant correlation is observed between the multimodal features and the attractiveness perception levels

Towards Stable Electrochemical Sensing for Wearable Wound Monitoring

Wearable biosensing has the tremendous advantage of providing point-of-care diagnosis and convenient therapy. In this research, methods to stabilize the electrochemical sensing response from detection of target biomolecules, Uric Acid (UA) and Xanthine, closely linked to wound healing, have been investigated. Different kinds of materials have been explored to address such detection from a wearable, healing platform. Electrochemical sensing modalities have been implemented in the detection of purine metabolites, UA and Xanthine, in the physiologically relevant ranges of the respective biomarkers. A correlation can be drawn between the concentrations of these bio-analytes and wound severity, thus offering probable quantitative insights on wound healing progression. These insights attempt to contribute in reducing some impacts of the financial structure on the healthcare economy associated with wound-care. An enzymatic electrochemical sensing system was designed to provide quick response at a cost-effective, miniaturized scale. Robust enzyme immobilization protocols have assisted in preserving enzyme activity to offer stable response under relevant variations of temperature and pH, from normal. Increased hydrophilicity of the sensor surface using corona plasma, has assisted in improving conductivity, thus allowing for increased electroactive functionalization and loading across the substrate’s surface. Superior sensor response was attained from higher loading of nanomaterials (MWCNT/AuNP) and enzymes (UOx/XO) employed in detection. Potentiometric analyses of the nanomaterial modified enzymatic biosensors were conducted using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) modalities. Under relevant physiological conditions, the biosensor was noted to offer a variation in response between 10 % and 30 % within a week. Stable, reproducible results were obtained from repeated use of the biosensor over multiple days, also offering promise for continuous monitoring. Shelf life of the biosensor was noted to be more than two days with response retained by about 80 % thereafter. Secondary analyses have been performed utilizing the enzymatic biosensor to explore the feasibility of target biomarker detection from clinical extracts of different biofluids for wound monitoring. Biosensor response evaluation from the extracts of human wound exudate, and those obtained from perilesional and healthy skin, provided an average recovery between 107 % and 110 % with a deviation within (+/-) 6 %. Gradual decrease in response (10-20 %) was noted in detection from extracts further away from injury site. Increased accumulation of biofluids on the sensor surface was studied to explore sensor response stability as a function of sample volume. With a broad linear range of detection (0.1 nM – 7.3 mM) and detection limits lower than the physiological concentrations, this study has assessed the viability of stable wound monitoring under physiologically relevant conditions on a wearable platform