Heart Failure (HF) is a major health and economic issue worldwide. HF-related expenses are largely driven by hospital admissions and re-admissions, many of which are potentially preventable. Current ...self-management programs, however, have failed to reduce hospital admissions. This may be explained by their low predictive power for decompensation and high adherence requirements. Slight alterations in the voice profile may allow to detect decompensation in HF patients at an earlier stage and reduce hospitalizations. This pilot study investigates the potential of voice as a digital biomarker to predict health status deterioration in HF patients. In a two-month longitudinal observational study, we collect voice samples and HF-related quality-of-life questionnaires from 35 stable HF patients. Patients use our developed study application installed on a tablet at home during the study period. From the collected data, we use signal processing to extract voice characteristics from the audio samples and associate them with the answers to the questionnaire data. The primary outcome will be the correlation between voice characteristics and HF-related quality-of-life health status.
Peer-to-peer (P2P) energy markets are gaining interest in the energy sector as a means to increase the share of decentralised energy resources (DER), thus fostering a clean, resilient and ...decentralised supply of energy. Various reports have touted P2P energy markets as ideal use case for blockchain-technology, as it offers advantages such as fault-tolerant operation, trust delegation, immutability, transparency, resilience, and automation. However, relatively little is known about the influence of hardware and communication infrastructure limitations on blockchain systems in real-life applications. In this article, we demonstrate the implementation of a real-world blockchain managed microgrid in Walenstadt, Switzerland. The 37 participating households are equipped with 75 special smart-metres that include single board computers (SBC) that run their own, application-specific private blockchain. Using the field-test setup, we provide an empirical evaluation of the feasibility of a Byzantine fault tolerant blockchain system. Furthermore, we artificially throttle bandwidth between nodes to simulate how the bandwidth of communication infrastructure impacts its performance. We find that communication networks with a bandwidth smaller than 1000 kbit/s – which includes WPAN, LoRa, narrowband IoT, and narrowband PLC – lead to insufficient throughput of the operation of a blockchain-managed microgrid. While larger numbers of validators may provide higher decentralisation and fault-tolerant operation, they considerably reduce throughput. The results from the field-test in the Walenstadt microgrid show that the blockchain running on the smart-metre SBCs can provide a maximum throughput of 10 transactions per second. The blockchain throughput halts almost entirely if the system is run by more than 40 validators. Based on the field test, we provide simplified guidelines for utilities or grid operators interested in implementing local P2P markets based on BFT systems.
User-generated app store reviews of mobile apps provide valuable information for developers. However, making sense of this large pool of data across many apps remains a challenge today due to the ...large data volumes and because app stores lack systematic, built-in analysis tools for user reviews.
Overview: The Internet of Things (IoT) offers product companies the opportunity to develop an IoT business. Existing performance measurement systems (PMS) are unsuitable for measuring and managing ...the business logic of IoT business. Based on research conducted with 31 product companies, we present three measurement traps, a key performance indicators (KPI) set suited for steering IoT business in product companies, and three recommendations for implementing the KPI set. Companies can use the KPI set to manage their IoT businesses more effectively and avoid the measurement traps.
Abstract
Background
The Assistant to Lift your Level of activitY (Ally) app is a smartphone application that combines financial incentives with chatbot-guided interventions to encourage users to ...reach personalized daily step goals.
Purpose
To evaluate the effects of incentives, weekly planning, and daily self-monitoring prompts that were used as intervention components as part of the Ally app.
Methods
We conducted an 8 week optimization trial with n = 274 insurees of a health insurance company in Switzerland. At baseline, participants were randomized to different incentive conditions (cash incentives vs. charity incentives vs. no incentives). Over the course of the study, participants were randomized weekly to different planning conditions (action planning vs. coping planning vs. no planning) and daily to receiving or not receiving a self-monitoring prompt. Primary outcome was the achievement of personalized daily step goals.
Results
Study participants were more active and healthier than the general Swiss population. Daily cash incentives increased step-goal achievement by 8.1%, 95% confidence interval (CI): 2.1, 14.1 and, only in the no-incentive control group, action planning increased step-goal achievement by 5.8%, 95% CI: 1.2, 10.4. Charity incentives, self-monitoring prompts, and coping planning did not affect physical activity. Engagement with planning interventions and self-monitoring prompts was low and 30% of participants stopped using the app over the course of the study.
Conclusions
Daily cash incentives increased physical activity in the short term. Planning interventions and self-monitoring prompts require revision before they can be included in future versions of the app. Selection effects and engagement can be important challenges for physical-activity apps.
Clinical Trial Information
This study was registered on ClinicalTrials.gov, NCT03384550.
Users of a physical activity app were more likely to increase their daily activity after receiving cash rewards but not in response to chatbot-guided reminders and planning interventions.
Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma ...exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved.
The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex.
We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex.
We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean -0.1 (95% CI -12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI -3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch-based sex classification performed best yielding an accuracy of 83%.
Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.
FLIRT: A feature generation toolkit for wearable data Föll, Simon; Maritsch, Martin; Spinola, Federica ...
Computer methods and programs in biomedicine,
November 2021, 2021-11-00, 20211101, Volume:
212
Journal Article
Peer reviewed
Open access
•Wearable data is increasingly utilized for biomedical research and clinical practice.•Standardized wearable data processing and feature generation is needed.•FLIRT is an open-source Python package ...tailored to wearable data processing.•FLIRT calculates more than 100 features ready for machine learning models.
Background and Objective: Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction.
Methods: FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions – a basis for a wide variety of ML algorithms.
Results: We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper.
Conclusion: FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.
Overview: The Internet of Things (IoT) offers product companies the opportunity to develop an IoT business. Existing performance measurement systems (PMS) are unsuitable for measuring and managing ...the business logic of IoT business. Based on research conducted with 31 product companies, we present three measurement traps, a key performance indicators (KPI) set suited for steering IoT business in product companies, and three recommendations for implementing the KPI set. Companies can use the KPI set to manage their IoT businesses more effectively and avoid the measurement traps.
There has been limited research investigating whether small financial incentives can promote participation, behavior change, and engagement in physical activity promotion programs. This study ...evaluates the effects of two types of small financial incentives within a physical activity promotion program of a Swiss health insurance company.
Three-arm cluster-randomized trial comparing small personal financial incentives and charity financial incentives (10 Swiss Francs, equal to US$10.40) for each month with an average step count of >10,000 steps per day to control. Insureds’ federal state of residence was the unit of randomization. Data were collected in 2015 and analyses were completed in 2018.
German-speaking insureds of a large health insurer in Switzerland were invited. Invited insureds were aged ≥18 years, enrolled in complementary insurance plans and registered on the insurer's online platform.
Primary outcome was the participation rate. Secondary outcomes were steps per day, the proportion of participant days in which >10,000 steps were achieved and non-usage attrition over the first 3 months of the program.
Participation rate was 5.94% in the personal financial incentive group (OR=1.96, 95% CI=1.55, 2.49) and 4.98% in the charity financial incentive group (OR=1.59, 95% CI=1.25, 2.01) compared with 3.23% in the control group. At the start of the program, the charity financial group had a 12% higher chance of walking 10,000 steps per day than the control group (OR=1.68, 95% CI=1.23, 2.30), but this effect dissipated after 3 months. Steps per day and non-usage attrition did not differ significantly between the groups.
Small personal and charity financial incentives can increase participation in physical activity promotion programs. Incentives may need to be modified in order to prevent attrition and promote behavior change over a longer period of time.
This study is registered at www.isrctn.com ISRCTN24436134.
Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have ...the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear.
This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs.
We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records.
Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants' previous experience with technology. Finally, risk bias varied markedly.
The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care.
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