Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement

BACKGROUND Chronic Lung disorders like COPD and IPF are characterised by exacerbations which are a significant problem: unpleasant for patients, and sometimes severe enough to cause hospital admission (and therefore NHS pressures) and death. Reducing the impact of exacerbations is very important. Moreover, due to the COVID-19 pandemic, the vulnerable populations with these disorders are at high risk and hence their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution of gaining visibility into the health of people in their daily life. Thus, remote monitoring of patients in their daily lives using mobile and wearable devices could be useful especially in high vulnerability groups. A scenario we consider here is to monitor patients and detect disease exacerbation and progression and investigate the opportunity of detecting exacerbations in real-time with a future goal of real-time intervention. OBJECTIVE The primary objective is to assess the feasibility and acceptability of remote monitoring using wearable and mobile phones in patients with pulmonary diseases. The aims will be evaluated over these areas: Participant acceptability, drop-out rates and interpretation of data, Detection of clinically important events such as exacerbations and disease progression, Quantification of symptoms (physical and mental health), Impact of disease on mood and wellbeing/QoL and The trajectory-tracking of main outcome variables, symptom fluctuations and order. The secondary objective of this study is to provide power calculations for a larger longitudinal follow-up study. METHODS Participants will be recruited from 2 NHS sites in 3 different cohorts - COPD, IPF and Post hospitalised Covid. A total of 60 participants will be recruited, 20 in each cohort. Data collection will be done remotely using the RADAR-Base mHealth platform for different devices - Garmin wearable devices, smart spirometers, mobile app questionnaires, surveys and finger pulse oximeters. Passive data collected includes wearable derived continuous heart rate, SpO2, respiration rate, activity, and sleep. Active data collected includes disease-specific PROMs, mental health questionnaires and symptoms tracking to track disease trajectory in addition to speech sampling, spirometry and finger Pulse Oximetry. Analyses are intended to assess the feasibility of RADAR-Base for lung disorder remote monitoring (include quality of data, a cross-section of passive and active data, data completeness, the usability of the system, acceptability of the system). Where adequate data is collected, we will attempt to explore disease trajectory, patient stratification and identification of acute clinically interesting events such as exacerbations. A key part of this study is understanding the potential of real-time data collection, here we will simulate an intervention using the Exacerbation Rating Scale (ERS) to acquire responses at-time-of-event to assess the performance of a model for exacerbation identification from passive data collected. RESULTS RALPMH study provides a unique opportunity to assess the use of remote monitoring in the study of lung disorders. The study is set to be started in mid-May 2021. The data collection apparatus, questionnaires and wearable integrations have been set up and tested by clinical teams. While waiting for ethics approval, real-time detection models are currently being constructed. CONCLUSIONS RALPMH will provide a reference infrastructure for the use of wearable data for monitoring lung diseases. Specifically information regarding the feasibility and acceptability of remote monitoring and the potential of real-time remote data collection and analysis in the context of chronic lung disorders. Moreover, it provides a unique standpoint to look into the specifics of novel coronavirus without burdensome interventions. It will help plan and inform decisions in any future studies that make use of remote monitoring in the area of Respiratory health. CLINICALTRIAL https://www.isrctn.com/ISRCTN16275601

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Digitization of Measurement Based Care Pathways in Mental Health through REDCap and Electronic Health Record Integration (Preprint)

10.2196/preprints.25656 ◽

2020 ◽

Author(s):

Steve Hawley ◽

David Rotenberg ◽

Joanna Yu ◽

Nikola Bogetic ◽

Natalia Potapova ◽

...

Keyword(s):

Mental Health ◽

Data Collection ◽

Data Quality ◽

Real Time ◽

Fold Increase ◽

Self Report ◽

Care Pathways ◽

Health Record ◽

Measurement Based Care ◽

Clinician Attitudes

BACKGROUND The delivery of standardized self-report assessments is essential for measurement-based care (MBC) in mental health. Paper-based methods of MBC data collection may result in transcription errors, missing data, and other data quality issues when entered into the patient electronic health record (EHR). OBJECTIVE To address these issues, a dedicated instance of REDCap, a free, widely used electronic data capture platform, was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. METHODS REDCap was integrated with the primary clinical information system to facilitate real-time transfer of discrete data and descriptive reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physician and clinician attitudes towards the new system. RESULTS Integration of REDCap with the EHR transitioned clinical workflows from paper-based to electronic data collection. This resulted in significant time-savings, improved data quality and valuable real-time information delivery. Digitization of self-report assessments at each appointment contributed to clinic-wide implementation of the Major Depressive Disorder Integrated Care Pathway (MDD-ICP). This digital transformation facilitated a 4-fold increase in physician adoption of this ICP workflow and a 3-fold increase in patient enrollment resulting in an overall significant increase in MDD-ICP capacity. Physician and Clinician attitudes were overall positive with almost all respondents agreeing that the system was useful to their work. CONCLUSIONS REDCap provided an intuitive patient interface for collection of self-report measures, real-time access to results to inform clinical decisions, and an extensible backend for systems integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for broad deployment of complex workflows and standardized assessments leading to accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate automatic transfer of self-report data to the EHR. However, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

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Conversations that Count: Lessons From Evaluating a Men’s Digital Mental Health Response During COVID-19

Evaluation Journal of Australasia ◽

10.1177/1035719x211053072 ◽

2021 ◽

pp. 1035719X2110530

Author(s):

Kathryn Erskine ◽

Matt Healey

Keyword(s):

Mental Health ◽

Data Collection ◽

Real Time ◽

Digital Health ◽

Evaluation Design ◽

Evaluation Practice ◽

Health Response ◽

Digital Evaluation ◽

Support Evaluation ◽

Evaluation Space

This paper details disruption and innovation in digital evaluation practice at Movember, as a result of the COVID-19 pandemic. The paper examines a men’s digital health intervention (DHI) – Movember Conversations – and the product pivot that was necessary to ensure it could respond to the pandemic. The paper focuses on the implications of the pivot for the evaluation and how the evaluation was adapted to the COVID-19 exigencies. It details the redesign of the evaluation to ensure methods wrapped around the modified product and could deliver real-time, practical insights. The paper seeks to fill knowledge gaps in the DHI evaluation space and outlines four key principles that support evaluation re-design in an agile setting. These include a user-centred approach to evaluation design, proportionate data collection, mixed (and flexible) methodologies, and agile evaluation reporting. The paper concludes with key lessons and reflections from the evaluators about what worked at Movember, to support other evaluators planning digital evaluations.

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Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

Perspectives of the ASHA Special Interest Groups ◽

10.1044/2019_pers-sig11-2018-0020 ◽

2019 ◽

Vol 4 (2) ◽

pp. 356-362

Author(s):

Jennifer W. Means ◽

Casey McCaffrey

Keyword(s):

Graduate Students ◽

Data Collection ◽

Real Time ◽

Clinical Supervision ◽

Video Recording ◽

Advanced Technology ◽

High Definition ◽

Recording Technology ◽

Self Confidence ◽

Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

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DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Financial Innovation ◽

10.1186/s40854-021-00269-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Suppawong Tuarob ◽

Poom Wettayakorn ◽

Ponpat Phetchai ◽

Siripong Traivijitkhun ◽

Sunghoon Lim ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Data Collection ◽

Stock Market ◽

Real Time ◽

Stock Prices ◽

Contextual Information ◽

Online News ◽

Stock Prediction ◽

Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

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Data mining-based air pollution characteristics and real-time monitoring of college students’ physical and mental health

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07926-2 ◽

2021 ◽

Vol 14 (15) ◽

Author(s):

Xiaomei Wu ◽

Xuejun Ma

Keyword(s):

Mental Health ◽

College Students ◽

Data Mining ◽

Air Pollution ◽

Real Time ◽

Physical And Mental Health ◽

Real Time Monitoring ◽

Pollution Characteristics

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Is empowerment a route to improving mental health and wellbeing in an urban regeneration (UR) context?

Urban Studies ◽

10.1177/0042098016632435 ◽

2016 ◽

Vol 54 (7) ◽

pp. 1619-1637 ◽

Cited By ~ 14

Author(s):

Camilla Baba ◽

Ade Kearns ◽

Emma McIntosh ◽

Carol Tannahill ◽

James Lewsey

Keyword(s):

Mental Health ◽

Linear Regression Analysis ◽

Urban Regeneration ◽

Personal Characteristics ◽

Well Being ◽

Community Empowerment ◽

Health Improvement ◽

Cross Sectional ◽

Health And Wellbeing ◽

Health Gains

Urban regeneration (UR) programmes are recognised as a type of Population Health Intervention (PHI), addressing social and health inequalities. Policy recommends programmes involve communities through engagement and empowerment. Whilst the literature has started to link empowerment with health improvement, this has not been within an UR context. As part of broader research on the economic evaluation of community empowerment activities, this paper examines how health gains can be generated through promoting empowerment as well as identifying whether feelings of empowerment are associated with residents personal characteristics or perceptions of their neighbourhood. Using 2011 Community Health and Wellbeing Survey (GoWell) cross-sectional data, ordinal logistic regression and simple linear regression analysis of 15 Glasgow neighbourhoods undergoing regeneration with 4302 adult householders (≥16 years old) was completed. Analyses identified strong associations ( P≥ 0.05) between empowerment and the mental health subscale of the SF12v2 and with several items of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) scale. Furthermore, residents’ who felt more empowered reported more positive attitudes towards their surroundings and housing providers. This concurs with recent evidence of the importance of residents’ psychological investments in their neighbourhood influencing their sense of place attachment. Such analyses present initial evidence of the value of investing resources within UR programmes to activities geared towards increasing residents’ empowerment as a means of producing those health gains often sought by more costly aspects of the programmes.

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