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Article ; Online: Home monitoring to detect progression of interstitial lung disease: A prospective cohort study.

Althobiani, Malik A / Ranjan, Yatharth / Russell, Anne-Marie / Jacob, Joseph / Orini, Michele / Sankesara, Heet / Conde, Pauline / Rashid, Zulqarnain / Dobson, Richard J B / Hurst, John R / Porter, Joanna C / Folarin, Amos A

Respirology (Carlton, Vic.)

2024

Language	English
Publishing date	2024-04-08
Publishing country	Australia
Document type	Journal Article
ZDB-ID	1435849-9
ISSN	1440-1843 ; 1323-7799
ISSN (online)	1440-1843
ISSN	1323-7799
DOI	10.1111/resp.14708
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Zs.A 4957: Show issues

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Article ; Online: Remote Administration of ADHD-Sensitive Cognitive Tasks: A Pilot Study.

Sun, Shaoxiong / Denyer, Hayley / Sankesara, Heet / Deng, Qigang / Ranjan, Yatharth / Conde, Pauline / Rashid, Zulqarnain / Bendayan, Rebecca / Asherson, Philip / Bilbow, Andrea / Groom, Madeleine / Hollis, Chris / Folarin, Amos A / Dobson, Richard J B / Kuntsi, Jonna

Journal of attention disorders

2023 Volume 27, Issue 9, Page(s) 1040–1050

Abstract: Objective: We assessed the feasibility and validity of remote researcher-led administration and self-administration of modified versions of two cognitive tasks sensitive to ADHD, a four-choice reaction time task (Fast task) and a combined Continuous ... ...

Abstract	Objective: We assessed the feasibility and validity of remote researcher-led administration and self-administration of modified versions of two cognitive tasks sensitive to ADHD, a four-choice reaction time task (Fast task) and a combined Continuous Performance Test/Go No-Go task (CPT/GNG), through a new remote measurement technology system. Method: We compared the cognitive performance measures (mean and variability of reaction times (MRT, RTV), omission errors (OE) and commission errors (CE)) at a remote baseline researcher-led administration and three remote self-administration sessions between participants with and without ADHD ( Results: The most consistent group differences were found for RTV, MRT and CE at the baseline researcher-led administration and the first self-administration, with 8 of the 10 comparisons statistically significant and all comparisons indicating medium to large effect sizes. Conclusion: Remote administration of cognitive tasks successfully captured the difficulties with response inhibition and regulation of attention, supporting the feasibility and validity of remote assessments.
MeSH term(s)	Humans ; Attention Deficit Disorder with Hyperactivity/diagnosis ; Attention Deficit Disorder with Hyperactivity/psychology ; Pilot Projects ; Reaction Time/physiology ; Attention/physiology ; Neuropsychological Tests ; Cognition/physiology
Language	English
Publishing date	2023-06-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2004350-8
ISSN	1557-1246 ; 1087-0547
ISSN (online)	1557-1246
ISSN	1087-0547
DOI	10.1177/10870547231172763
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Article ; Online: An independent shopping experience for wheelchair users through augmented reality and RFID.

Rashid, Zulqarnain / Pous, Rafael / Norrie, Christopher S

Assistive technology : the official journal of RESNA

2017 Volume 31, Issue 1, Page(s) 9–18

Abstract: People with physical and mobility impairments continue to struggle to attain independence in the performance of routine activities and tasks. For example, browsing in a store and interacting with products located beyond an arm's length may be impossible ... ...

Abstract	People with physical and mobility impairments continue to struggle to attain independence in the performance of routine activities and tasks. For example, browsing in a store and interacting with products located beyond an arm's length may be impossible without the enabling intervention of a human assistant. This research article describes a study undertaken to design, develop, and evaluate potential interaction methods for motor-impaired individuals, specifically those who use wheelchairs. Our study includes a user-centered approach, and a categorization of wheelchair users based upon the severity of their disability and their individual needs. We designed and developed access solutions that utilize radio frequency identification (RFID), augmented reality (AR), and touchscreen technologies in order to help people who use wheelchairs to carry out certain tasks autonomously. In this way, they have been empowered to go shopping independently, free from reliance upon the assistance of others. A total of 18 wheelchair users participated in the completed study.
MeSH term(s)	Activities of Daily Living ; Adult ; Aged ; Augmented Reality ; Female ; Humans ; Male ; Middle Aged ; Radio Frequency Identification Device ; Self-Help Devices ; Wheelchairs
Language	English
Publishing date	2017-06-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1014913-2
ISSN	1949-3614 ; 1040-0435
ISSN (online)	1949-3614
ISSN	1040-0435
DOI	10.1080/10400435.2017.1329240
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Article: Investigating the Use of Digital Health Technology to Monitor COVID-19 and Its Effects: Protocol for an Observational Study (Covid Collab Study).

Stewart, Callum / Ranjan, Yatharth / Conde, Pauline / Rashid, Zulqarnain / Sankesara, Heet / Bai, Xi / Dobson, Richard J B / Folarin, Amos A

JMIR research protocols

2021 Volume 10, Issue 12, Page(s) e32587

Abstract: Background: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people's health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, ...

Abstract	Background: The ubiquity of mobile phones and increasing use of wearable fitness trackers offer a wide-ranging window into people's health and well-being. There are clear advantages in using remote monitoring technologies to gain an insight into health, particularly under the shadow of the COVID-19 pandemic. Objective: Covid Collab is a crowdsourced study that was set up to investigate the feasibility of identifying, monitoring, and understanding the stratification of SARS-CoV-2 infection and recovery through remote monitoring technologies. Additionally, we will assess the impacts of the COVID-19 pandemic and associated social measures on people's behavior, physical health, and mental well-being. Methods: Participants will remotely enroll in the study through the Mass Science app to donate historic and prospective mobile phone data, fitness tracking wearable data, and regular COVID-19-related and mental health-related survey data. The data collection period will cover a continuous period (ie, both before and after any reported infections), so that comparisons to a participant's own baseline can be made. We plan to carry out analyses in several areas, which will cover symptomatology; risk factors; the machine learning-based classification of illness; and trajectories of recovery, mental well-being, and activity. Results: As of June 2021, there are over 17,000 participants-largely from the United Kingdom-and enrollment is ongoing. Conclusions: This paper introduces a crowdsourced study that will include remotely enrolled participants to record mobile health data throughout the COVID-19 pandemic. The data collected may help researchers investigate a variety of areas, including COVID-19 progression; mental well-being during the pandemic; and the adherence of remote, digitally enrolled participants. International registered report identifier (irrid): DERR1-10.2196/32587.
Language	English
Publishing date	2021-12-08
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719222-2
ISSN	1929-0748
ISSN	1929-0748
DOI	10.2196/32587
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Article: The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement.

de Angel, Valeria / Adeleye, Fadekemi / Zhang, Yuezhou / Cummins, Nicholas / Munir, Sara / Lewis, Serena / Laporta Puyal, Estela / Matcham, Faith / Sun, Shaoxiong / Folarin, Amos A / Ranjan, Yatharth / Conde, Pauline / Rashid, Zulqarnain / Dobson, Richard / Hotopf, Matthew

JMIR mental health

2023 Volume 10, Page(s) e42866

Abstract: Background: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and ...

Abstract	Background: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. Objective: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. Methods: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. Results: The overall retention rate was 60%. Higher-intensity treatment (χ Conclusions: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
Language	English
Publishing date	2023-01-24
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2798262-2
ISSN	2368-7959
ISSN	2368-7959
DOI	10.2196/42866
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Article ; Online: RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.

Ranjan, Yatharth / Rashid, Zulqarnain / Stewart, Callum / Conde, Pauline / Begale, Mark / Verbeeck, Denny / Boettcher, Sebastian / Dobson, Richard / Folarin, Amos

JMIR mHealth and uHealth

2019 Volume 7, Issue 8, Page(s) e11734

Abstract: Background: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open ... ...

Abstract	Background: With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective: Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods: RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results: General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions: RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.
MeSH term(s)	Data Analysis ; Humans ; Monitoring, Physiologic/instrumentation ; Monitoring, Physiologic/methods ; Monitoring, Physiologic/trends ; Software Design ; Telemedicine/instrumentation ; Telemedicine/methods ; Telemedicine/trends ; Wearable Electronic Devices/trends
Language	English
Publishing date	2019-08-01
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2719220-9
ISSN	2291-5222 ; 2291-5222
ISSN (online)	2291-5222
ISSN	2291-5222
DOI	10.2196/11734
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Article: Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence.

Laiou, Petroula / Biondi, Andrea / Bruno, Elisa / Viana, Pedro F / Winston, Joel S / Rashid, Zulqarnain / Ranjan, Yatharth / Conde, Pauline / Stewart, Callum / Sun, Shaoxiong / Zhang, Yuezhou / Folarin, Amos / Dobson, Richard J B / Schulze-Bonhage, Andreas / Dümpelmann, Matthias / Richardson, Mark P / Radar-Cns Consortium

Biomedicines

2022 Volume 10, Issue 10

Abstract: Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the ...

Abstract	Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
Language	English
Publishing date	2022-10-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines10102662
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Article ; Online: Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model.

Zhang, Yuezhou / Folarin, Amos A / Dineley, Judith / Conde, Pauline / de Angel, Valeria / Sun, Shaoxiong / Ranjan, Yatharth / Rashid, Zulqarnain / Stewart, Callum / Laiou, Petroula / Sankesara, Heet / Qian, Linglong / Matcham, Faith / White, Katie / Oetzmann, Carolin / Lamers, Femke / Siddi, Sara / Simblett, Sara / Schuller, Björn W /

Vairavan, Srinivasan / Wykes, Til / Haro, Josep Maria / Penninx, Brenda W J H / Narayan, Vaibhav A / Hotopf, Matthew / Dobson, Richard J B / Cummins, Nicholas

Journal of affective disorders

2024 Volume 355, Page(s) 40–49

Abstract: Background: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression- ... ...

Abstract	Background: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. Methods: The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. Results: From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. Limitations: Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. Conclusion: This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.
MeSH term(s)	Humans ; Speech ; Smartphone ; Depression/diagnosis ; Speech Recognition Software ; Deep Learning
Language	English
Publishing date	2024-03-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2024.03.106
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Article: Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis.

Kontaxis, Spyridon / Laporta, Estela / Garcia, Esther / Guerrero, Ana Isabel / Zabalza, Ana / Matteo, Martinis / Lucia, Roselli / Simblett, Sara / Weyer, Janice / Hotopf, Matthew / Narayan, Vaibhav A / Rashid, Zulqarnain / Folarin, Amos A / Dobson, Richard J B / Buron, Mathias Due / Leocani, Letizia / Cummins, Nicholas / Vairavan, Srinivasan / Costa, Gloria Dalla /

Magyari, Melinda / Sørensen, Per Soelberg / Nos, Carlos / Bailón, Raquel / Comi, Giancarlo / The Radar-Cns Consortium

Frontiers in physiology

2023 Volume 14, Page(s) 1145818

Abstract: Objective: ...

Abstract	Objective:
Language	English
Publishing date	2023-04-06
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2023.1145818
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Article ; Online: Presentation of long COVID and associated risk factors in a mobile health study

Stewart, Callum / Ranjan, Yatharth / Conde, Pauline / Sun, Shaoxiong / Rashid, Zulqarnain / Sankesara, Heet / Cummins, Nicholas / Laiou, Petroula / Bai, Xi / Dobson, Richard / Folarin, Amos

medRxiv

Abstract: Background The Covid Collab study was a citizen science mobile health research project set up in June 2020 to monitor COVID-19 symptoms and mental health through questionnaire self-reports and passive wearable device data. Methods Using mobile health ... ...

Abstract	Background The Covid Collab study was a citizen science mobile health research project set up in June 2020 to monitor COVID-19 symptoms and mental health through questionnaire self-reports and passive wearable device data. Methods Using mobile health data, we consider whether a participant is suffering from long COVID in two ways. Firstly, by whether the participant has a persistent change in a physiological signal commencing at a diagnosis of COVID-19 that last for at least twelve weeks. Secondly, by whether a participant has self-reported persistent symptoms for at least twelve weeks. We assess sociodemographic and wearable-based risk factors for the development of long COVID according to the above two categorisations. Findings Persistent changes to physiological signals measured by com- mercial fitness wearables, including heart rate, sleep, and activity, are visible following a COVID-19 infection and may help differentiate people who develop long COVID. Anxiety and depression are significantly and persistently affected at a group level following a COVID-19 infection. We found the level of activity undertaken in the year prior to illness was protective against long COVID and that symptoms of depression before and during the acute illness may be a risk factor. Interpretation Mobile health and wearable devices may prove to be a useful resource for tracking recovery and presence of long-term sequelae to COVID-19. Mental wellbeing is significantly negatively effected on average for an extended period of time following a COVID-19 infection.
Keywords	covid19
Language	English
Publishing date	2022-09-27
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2022.09.27.22280404
Database	COVID19

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