1
|
Hama Y, Yamada S, Nishimura R, Yoshida M, Tsuga K, Morita E, Tamada Y, Kato Y, Kubo Y, Okada R, Nagayoshi M, Tamura T, Hishida A, Wakai K, Naito M. Association between dysphagia risk and sleep quality in community-dwelling older adults: A cross-sectional study. Heliyon 2024; 10:e32028. [PMID: 38882350 PMCID: PMC11180309 DOI: 10.1016/j.heliyon.2024.e32028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/26/2024] [Accepted: 05/27/2024] [Indexed: 06/18/2024] Open
Abstract
Objectives Exploring the effects of swallowing function on sleep quality could provide valuable insights into the potential impact of reduced swallowing function on sleep. However, pertinent studies are limited. Therefore, this study aimed to investigate the relationship between dysphagia risk and sleep health in community-dwelling older adults. Methods Data for this cross-sectional study were obtained from the Shizuoka and Daiko studies conducted as part of the Japan Multi-Institutional Collaborative Cohort Study. Information on demographics, overall lifestyle, dysphagia risk, as well as sleep quality, duration, satisfaction, and regularity, was obtained using a self-administered questionnaire. Dysphagia risk and sleep quality were assessed using the Dysphagia Risk Assessment Questionnaire for the Community-dwelling Elderly and the Japanese version of the Pittsburgh Sleep Questionnaire Index, respectively. Multivariate logistic regression, adjusted for covariates, was employed to assess the association between dysphagia risk and sleep health. Results Among the 3058 participants (1633 males, 1425 females) aged ≥60 years, 28.0 % exhibited dysphagia risk, and 19.1 % reported poor sleep quality. Those with dysphagia risk were more likely to experience poor sleep quality than those without dysphagia risk. In male participants, dysphagia was significantly associated with poor sleep quality, unsatisfactory sleep, and sleep irregularity, but was not significantly associated with unsatisfactory or irregular sleep in female participants. The Japanese version of the Pittsburgh Sleep Questionnaire Index components-subjective sleep quality, sleep latency, sleep disturbances, and daytime dysfunction-were associated with dysphagia risk in both sexes. Conclusions Dysphagia risk is associated with sleep quality in older individuals in Japan. Thus, preserving swallowing function may contribute to enhancing sleep quality.
Collapse
Affiliation(s)
- Yohko Hama
- Department of Advanced Prosthodontics, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
- Hiroshima Oral Health Center, Hiroshima, Japan
| | - Sachiko Yamada
- Department of Oral Epidemiology, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Rumi Nishimura
- Department of Oral Epidemiology, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Mitsuyoshi Yoshida
- Department of Dentistry and Oral-Maxillofacial Surgery, Fujita Health University School of Medicine, Toyoake, Japan
| | - Kazuhiro Tsuga
- Department of Advanced Prosthodontics, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| | - Emi Morita
- International Institute for Integrative Sleep Medicine, University of Tsukuba, Japan
- Forestry and Forest Products Research Institute, Forest Research and Management Organization, Japan
| | - Yudai Tamada
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Miyagi, Japan
| | - Yasufumi Kato
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yoko Kubo
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Rieko Okada
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Mako Nagayoshi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Mariko Naito
- Department of Oral Epidemiology, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan
| |
Collapse
|
2
|
Chan AHY, Te Ao B, Baggott C, Cavadino A, Eikholt AA, Harwood M, Hikaka J, Gibbs D, Hudson M, Mirza F, Naeem MA, Semprini R, Chang CL, Tsang KCH, Shah SA, Jeremiah A, Abeysinghe BN, Roy R, Wall C, Wood L, Dalziel S, Pinnock H, van Boven JFM, Roop P, Harrison J. DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol. BMJ Open Respir Res 2024; 11:e002275. [PMID: 38777583 PMCID: PMC11116853 DOI: 10.1136/bmjresp-2023-002275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.
Collapse
Affiliation(s)
- Amy Hai Yan Chan
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Braden Te Ao
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Christina Baggott
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Alana Cavadino
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Amber A Eikholt
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Matire Harwood
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Joanna Hikaka
- Te Kupenga Hauora Māori, University of Auckland, Auckland, New Zealand
| | - Dianna Gibbs
- Pinnacle Midlands Health Network, Hamilton, New Zealand
| | - Mariana Hudson
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Farhaan Mirza
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
| | - Muhammed Asif Naeem
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
- National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Ruth Semprini
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Catherina L Chang
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Kevin C H Tsang
- University College London, London, UK
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Syed Ahmar Shah
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Aron Jeremiah
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Binu Nisal Abeysinghe
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Rajshri Roy
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Clare Wall
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Lisa Wood
- Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia
| | - Stuart Dalziel
- Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand
| | - Hilary Pinnock
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Job F M van Boven
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Partha Roop
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Jeff Harrison
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| |
Collapse
|
3
|
Wieczorek K, Ananth S, Valazquez-Pimentel D. Acoustic biomarkers in asthma: a systematic review. J Asthma 2024:1-16. [PMID: 38634718 DOI: 10.1080/02770903.2024.2344156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring. DATA SOURCES A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023. STUDY SELECTION 27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound. RESULTS Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified. CONCLUSIONS This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.
Collapse
Affiliation(s)
| | - Sachin Ananth
- London North West University Healthcare Trust, London, UK
| | | |
Collapse
|
4
|
Lee SE, Rudd M, Kim TH, Oh JY, Lee JH, Jover L, Small PM, Chung KF, Song WJ. Feasibility and Utility of a Smartphone Application-Based Longitudinal Cough Monitoring in Chronic Cough Patients in a Real-World Setting. Lung 2023; 201:555-564. [PMID: 37831232 DOI: 10.1007/s00408-023-00647-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE This study evaluated the feasibility and utility of longitudinal cough frequency monitoring with the Hyfe Cough Tracker, a mobile application equipped with cough-counting artificial intelligence algorithms, in real-world patients with chronic cough. METHODS Patients with chronic cough (> 8-week duration) were monitored continuously for cough frequency with the Hyfe app for at least one week. Cough was also evaluated using the Leicester Cough Questionnaire (LCQ) and daily cough severity scoring (0-10). The study analyzed adherence rate, the correlation between objective cough frequency and subjective scores, day-to-day variability, and patient experience. RESULTS Of 65 subjects consecutively recruited, 43 completed the study. The median cough monitoring duration was 13.9 days, with a median adherence of 91%. Study completion was associated with baseline cough severity, and the adherence rate was higher in younger subjects. Cross-sectional correlation analyses showed modest correlations between objective and subjective cough measures at the group level. However, in time series correlation analyses, correlations between objective and subjective measures widely varied across individuals. Cough frequency had greater day-to-day variability than daily cough severity scores in most subjects. A patient experience survey found that 70% of participants found the cough monitoring helpful, 86% considered it acceptable, and 84% felt it was easy to use. CONCLUSION Monitoring cough frequency longitudinally for at least one week may be feasible. The substantial day-to-day variability in objective cough frequency highlights the need for continuous monitoring. Grasping the implications of daily cough variability is crucial in both clinical practice and clinical trials.
Collapse
Affiliation(s)
- Seung-Eun Lee
- Department of Internal Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Matthew Rudd
- Department of Mathematics and Computer Science, The University of the South, Sewanee, TN, USA
- Hyfe Inc, Wilmington, DE, USA
| | - Tae-Hwa Kim
- Department of Internal Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Ji-Yoon Oh
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji-Hyang Lee
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | | | - Peter M Small
- Hyfe Inc, Wilmington, DE, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Kian Fan Chung
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Woo-Jung Song
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| |
Collapse
|
5
|
Castro O, Mair JL, Salamanca-Sanabria A, Alattas A, Keller R, Zheng S, Jabir A, Lin X, Frese BF, Lim CS, Santhanam P, van Dam RM, Car J, Lee J, Tai ES, Fleisch E, von Wangenheim F, Tudor Car L, Müller-Riemenschneider F, Kowatsch T. Development of "LvL UP 1.0": a smartphone-based, conversational agent-delivered holistic lifestyle intervention for the prevention of non-communicable diseases and common mental disorders. Front Digit Health 2023; 5:1039171. [PMID: 37234382 PMCID: PMC10207359 DOI: 10.3389/fdgth.2023.1039171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 04/06/2023] [Indexed: 05/28/2023] Open
Abstract
Background Non-communicable diseases (NCDs) and common mental disorders (CMDs) are the leading causes of death and disability worldwide. Lifestyle interventions via mobile apps and conversational agents present themselves as low-cost, scalable solutions to prevent these conditions. This paper describes the rationale for, and development of, "LvL UP 1.0″, a smartphone-based lifestyle intervention aimed at preventing NCDs and CMDs. Materials and Methods A multidisciplinary team led the intervention design process of LvL UP 1.0, involving four phases: (i) preliminary research (stakeholder consultations, systematic market reviews), (ii) selecting intervention components and developing the conceptual model, (iii) whiteboarding and prototype design, and (iv) testing and refinement. The Multiphase Optimization Strategy and the UK Medical Research Council framework for developing and evaluating complex interventions were used to guide the intervention development. Results Preliminary research highlighted the importance of targeting holistic wellbeing (i.e., both physical and mental health). Accordingly, the first version of LvL UP features a scalable, smartphone-based, and conversational agent-delivered holistic lifestyle intervention built around three pillars: Move More (physical activity), Eat Well (nutrition and healthy eating), and Stress Less (emotional regulation and wellbeing). Intervention components include health literacy and psychoeducational coaching sessions, daily "Life Hacks" (healthy activity suggestions), breathing exercises, and journaling. In addition to the intervention components, formative research also stressed the need to introduce engagement-specific components to maximise uptake and long-term use. LvL UP includes a motivational interviewing and storytelling approach to deliver the coaching sessions, as well as progress feedback and gamification. Offline materials are also offered to allow users access to essential intervention content without needing a mobile device. Conclusions The development process of LvL UP 1.0 led to an evidence-based and user-informed smartphone-based intervention aimed at preventing NCDs and CMDs. LvL UP is designed to be a scalable, engaging, prevention-oriented, holistic intervention for adults at risk of NCDs and CMDs. A feasibility study, and subsequent optimisation and randomised-controlled trials are planned to further refine the intervention and establish effectiveness. The development process described here may prove helpful to other intervention developers.
Collapse
Affiliation(s)
- Oscar Castro
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Jacqueline Louise Mair
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Alicia Salamanca-Sanabria
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Aishah Alattas
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Roman Keller
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Shenglin Zheng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Ahmad Jabir
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Xiaowen Lin
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Bea Franziska Frese
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Chang Siang Lim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington DC, DC, United States
| | - Josip Car
- Centre for Population Health Sciences, LKCMedicine, Nanyang Technological University, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jimmy Lee
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Research Division, Institute of Mental Health, Singapore, Singapore
- North Region & Department of Psychosis, Institute of Mental Health, Singapore, Singapore
| | - E Shyong Tai
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Elgar Fleisch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions,Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Florian von Wangenheim
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Lorainne Tudor Car
- Neuroscience and Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Falk Müller-Riemenschneider
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Digital Health Center, Berlin Institute of Health, Charite University Medical Centre Berlin, Berlin, Germany
| | - Tobias Kowatsch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
| |
Collapse
|
6
|
Kuhn M, Nalbant E, Kohlbrenner D, Alge M, Kuett L, Arvaji A, Sievi NA, Russi EW, Clarenbach CF. Validation of a small cough detector. ERJ Open Res 2023; 9:00279-2022. [PMID: 36699651 PMCID: PMC9868968 DOI: 10.1183/23120541.00279-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 09/23/2022] [Indexed: 01/28/2023] Open
Abstract
Research question The assessment of cough frequency in clinical practice relies predominantly on the patient's history. Currently, objective evaluation of cough is feasible with bulky equipment during a brief time (i.e. hours up to 1 day). Thus, monitoring of cough has been rarely performed outside clinical studies. We developed a small wearable cough detector (SIVA-P3) that uses deep neural networks for the automatic counting of coughs. This study examined the performance of the SIVA-P3 in an outpatient setting. Methods We recorded cough epochs with SIVA-P3 over eight consecutive days in patients suffering from chronic cough. During the first 24 h, the detector was validated against cough events counted by trained human listeners. The wearing comfort and the device usage were assessed using a questionnaire. Results In total, 27 participants (mean±sd age 50±14 years) with either chronic unexplained cough (n=12), COPD (n=4), asthma (n=5) or interstitial lung disease (n=6) were studied. During the daytime, the sensitivity of SIVA-P3 cough detection was 88.5±2.49% and the specificity was 99.97±0.01%. During the night-time, the sensitivity was 84.15±5.04% and the specificity was 99.97±0.02%. The wearing comfort and usage of the device was rated as very high by most participants. Conclusion SIVA-P3 enables automatic continuous cough monitoring in an outpatient setting for objective assessment of cough over days and weeks. It shows comparable sensitivity or higher sensitivity than other devices with fully automatic cough counting. Thanks to its wearing comfort and the high performance for cough detection, it has the potential for being used in routine clinical practice.
Collapse
Affiliation(s)
- Manuel Kuhn
- Faculty of Medicine, University of Zurich, Zurich, Switzerland,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland,Corresponding author: Manuel Kuhn ()
| | | | - Dario Kohlbrenner
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | | | | | - Alexandra Arvaji
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | - Noriane A. Sievi
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | - Erich W. Russi
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Christian F. Clarenbach
- Faculty of Medicine, University of Zurich, Zurich, Switzerland,Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| |
Collapse
|
7
|
Langholm C, Kowatsch T, Bucci S, Cipriani A, Torous J. Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research. Digit Biomark 2023; 7:104-114. [PMID: 37901364 PMCID: PMC10601905 DOI: 10.1159/000530698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/27/2023] [Indexed: 10/31/2023] Open
Abstract
The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.
Collapse
Affiliation(s)
- Carsten Langholm
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Tobias Kowatsch
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
- Center for Digital Health Interventions, Department of Management, Technology, and Economics at ETH Zurich, Zurich, Switzerland
| | - Sandra Bucci
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Andrea Cipriani
- Department of Psychiatry, University of Manchester, Manchester, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
8
|
Dhinagaran DA, Martinengo L, Ho MHR, Joty S, Kowatsch T, Atun R, Tudor Car L. Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework. JMIR Mhealth Uhealth 2022; 10:e38740. [PMID: 36194462 PMCID: PMC9579935 DOI: 10.2196/38740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/02/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation. OBJECTIVE The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care. METHODS We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study. RESULTS The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes. CONCLUSIONS Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.
Collapse
Affiliation(s)
| | - Laura Martinengo
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
| | - Moon-Ho Ringo Ho
- School of Social Sciences, Nanyang Technological University Singapore, Singapore, Singapore
| | - Shafiq Joty
- School of Computer Sciences and Engineering, Nanyang Technological University Singapore, Singapore, Singapore
| | - Tobias Kowatsch
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St Gallen, St Gallen, Switzerland
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Future Health Technologies Programme, Campus for Research Excellence and Technological Enterprise, Singapore-ETH Centre, Singapore, Singapore
| | - Rifat Atun
- Department of Global Health & Population, Department of Health Policy & Management, Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States
- Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Cambridge, MA, United States
- Health Systems Innovation Lab, Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, United States
| | - Lorainne Tudor Car
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore
- Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| |
Collapse
|
9
|
Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA. Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open 2022; 12:e064166. [PMID: 36192103 PMCID: PMC9535155 DOI: 10.1136/bmjopen-2022-064166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.
Collapse
Affiliation(s)
- Kevin Cheuk Him Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
- Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK
| | - Dario Salvi
- Internet of Things and People Research Centre, Malmo University, Malmo, Sweden
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
10
|
Tsang KCH, Pinnock H, Wilson AM, Shah SA. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. J Asthma Allergy 2022; 15:855-873. [PMID: 35791395 PMCID: PMC9250768 DOI: 10.2147/jaa.s285742] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/16/2022] [Indexed: 12/21/2022] Open
Abstract
Background Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.
Collapse
Affiliation(s)
- Kevin C H Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, and Norwich Medical School, University of East Anglia, Norwich, UK
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
11
|
Holmes J, Heaney LG, McGarvey LPA. Objective and Subjective Measurement of Cough in Asthma: A Systematic Review of the Literature. Lung 2022; 200:169-178. [PMID: 35416544 PMCID: PMC9038879 DOI: 10.1007/s00408-022-00527-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/09/2022] [Indexed: 12/04/2022]
Abstract
Background The extent to which objective and subjective tools has been used to measure the characteristics and burden of cough in patients with asthma has not been reported. Objective To review the large and extensive body of literature in asthma with the specific hypothesis that the characteristics of cough and clinical impact in this disease has only occasionally been studied. Methods For this systematic review, we searched EMBASE and MEDLINE databases using a combination of MeSH terms for “cough” and “asthma” for studies published up to and including end of August 2021. Studies included for analysis were confined to those undertaken in adult patients (≥ 18 years) with asthma of any severity where any tool or method to specifically measure cough was employed. Results Of 12,090 citations identified after our initial search, 112 full-text articles met criteria for inclusion in our analysis. We found that a broad range of objective and subjective measures have been used albeit with a lack of consistency between studies. Clinically important levels of cough associated with impaired health status were identified in patients with asthma. Conclusion Although cough is a common symptom in asthma, the clinical features and accompanying healthcare burden have been studied infrequently. In studies where cough was measured, the methods employed varied considerably. A more consistent use of cough-specific measurement tools is required to better determine the nature and burden of cough in asthma.
Collapse
Affiliation(s)
- Joshua Holmes
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK
| | - Liam G Heaney
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK
| | - Lorcan P A McGarvey
- Wellcome-Wolfson Institute for Experimental Medicine, Belfast, UK. .,Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK.
| |
Collapse
|
12
|
Abdelkhalek M, Qiu J, Hernandez M, Bozkurt A, Lobaton E. Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:7103-7107. [PMID: 34892738 DOI: 10.1109/embc46164.2021.9630082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients' privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance-This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].
Collapse
|