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Cohen Rodrigues TR, de Buisonjé DR, Reijnders T, Santhanam P, Kowatsch T, Breeman LD, Janssen VR, Kraaijenhagen RA, Atsma DE, Evers AW. Human cues in eHealth to promote lifestyle change: An experimental field study to examine adherence to self-help interventions. Internet Interv 2024; 35:100726. [PMID: 38370288 PMCID: PMC10869898 DOI: 10.1016/j.invent.2024.100726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 02/20/2024] Open
Abstract
eHealth lifestyle interventions without human support (self-help interventions) are generally less effective, as they suffer from lower adherence levels. To solve this, we investigated whether (1) using a text-based conversational agent (TCA) and applying human cues contribute to a working alliance with the TCA, and whether (2) adding human cues and establishing a positive working alliance increase intervention adherence. Participants (N = 121) followed a TCA-supported app-based physical activity intervention. We manipulated two types of human cues: visual (ie, message appearance) and relational (ie, message content). We employed a 2 (visual cues: yes, no) x 2 (relational cues: yes, no) between-subjects design, resulting in four experimental groups: (1) visual and relational cues, (2) visual cues only, (3) relational cues only, or (4) no human cues. We measured the working alliance with the Working Alliance Inventory Short Revised form and intervention adherence as the number of days participants responded to the TCA's messages. Contrary to expectations, the working alliance was unaffected by using human cues. Working alliance was positively related to adherence (t(78) = 3.606, p = .001). Furthermore, groups who received visual cues showed lower adherence levels compared to those who received relational cues only or no cues (U = 1140.5, z = -3.520, p < .001). We replicated the finding that establishing a working alliance contributes to intervention adherence, independently of the use of human cues in a TCA. However, we were unable to show that adding human cues impacted the working alliance and increased adherence. The results indicate that adding visual cues to a TCA may even negatively affect adherence, possibly because it may create confusion concerning the true nature of the coach, which may prompt unrealistic expectations.
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Affiliation(s)
| | | | - Thomas Reijnders
- Health, Medical, and Neuropsychology Unit, Leiden University, the Netherlands
| | - Prabhakaran Santhanam
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Instiute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St.Gallen, St. Gallen, Switzerland
| | - Linda D. Breeman
- Health, Medical, and Neuropsychology Unit, Leiden University, the Netherlands
| | - Veronica R. Janssen
- Health, Medical, and Neuropsychology Unit, Leiden University, the Netherlands
- Department of Cardiology, Leiden University Medical Center, the Netherlands
| | - Roderik A. Kraaijenhagen
- NDDO Institute for Prevention and Early Diagnostics (NIPED), Amsterdam, the Netherlands
- Vital10, Amsterdam, the Netherlands
| | - Douwe E. Atsma
- Department of Cardiology, Leiden University Medical Center, the Netherlands
| | - Andrea W.M. Evers
- Health, Medical, and Neuropsychology Unit, Leiden University, the Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Medical Delta, Leiden University, Technical University of Delft, Erasmus University Rotterdam, the Netherlands
| | - the BENEFIT consortium
- Health, Medical, and Neuropsychology Unit, Leiden University, the Netherlands
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Instiute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St.Gallen, St. Gallen, Switzerland
- Department of Cardiology, Leiden University Medical Center, the Netherlands
- NDDO Institute for Prevention and Early Diagnostics (NIPED), Amsterdam, the Netherlands
- Vital10, Amsterdam, the Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Medical Delta, Leiden University, Technical University of Delft, Erasmus University Rotterdam, the Netherlands
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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.
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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
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Barata F, Cleres D, Tinschert P, Iris Shih CH, Rassouli F, Boesch M, Brutsche M, Fleisch E. Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study. JMIR Form Res 2023; 7:e38439. [PMID: 36655551 PMCID: PMC9989914 DOI: 10.2196/38439] [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: 04/14/2022] [Revised: 09/17/2022] [Accepted: 01/17/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. OBJECTIVE This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. METHODS Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. RESULTS In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. CONCLUSIONS The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.
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Affiliation(s)
- Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - David Cleres
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Peter Tinschert
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Resmonics AG, Zurich, Switzerland
| | - Chen-Hsuan Iris Shih
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Resmonics AG, Zurich, Switzerland
| | - Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | | | - Martin Brutsche
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
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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.
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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
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Makieieva NI, Andrushchenko VV, Malakhova VM, Tkachenko AS, Onishchenko AI, Polyakov VV, Vygivska LA. THE LEVEL OF REACTIVE OXYGEN SPECIES AS A MARKER OF ASTHMA SEVERITY IN CHILDREN. WIADOMOSCI LEKARSKIE (WARSAW, POLAND : 1960) 2023; 76:205-212. [PMID: 36883511 DOI: 10.36740/wlek202301128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
OBJECTIVE The aim of the research was to assess the reactive oxygen species (ROS) levels in granulocytes of patients with asthma. PATIENTS AND METHODS Materials and methods: The study involved 35 children aged 5 to 17 years. 26 children with persistent asthma, partially controlled course in the period of exacerbation were divided into groups: 1 group - mild asthma (n = 12), group 2 - moderate asthma (n = 7) group 3 - severe asthma (n = 7) and control group included almost healthy children (n = 9). ROS levels in granulocytes were evaluated using BD FACSDiva™. The spirographic complex was used to assess the function of external respiration. RESULTS Results: The level of ROS in granulocytes of patients with severe asthma was significantly reduced compared with children in the control group and patients with mild and moderate asthma (p₁-₃ = 0.0003, p₂-₃ = 0.0017, p c-₃ = 0.0150). The concentration of ROS in granulocytes ≤ 285 a.u. was prognostically significant with high specificity and sensitivity with severe asthma. CONCLUSION Conclusions: The concentration of ROS levels in neutrophils in patients with severe asthma probably reflected the suppression of their products, which suggests the depletion of the reserve capacity of neutrophils. Decreased concentrations of reactive oxygen species in children with asthma can be considered as a possible marker of asthma severity.
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Zhang M, Sykes DL, Brindle K, Sadofsky LR, Morice AH. Chronic cough-the limitation and advances in assessment techniques. J Thorac Dis 2022; 14:5097-5119. [PMID: 36647459 PMCID: PMC9840016 DOI: 10.21037/jtd-22-874] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022]
Abstract
Accurate and consistent assessments of cough are essential to advance the understanding of the mechanisms of cough and individualised the management of patients. Considerable progress has been made in this work. Here we reviewed the currently available tools for subjectively and objectively measuring both cough sensitivity and severity. We also provided some opinions on the new techniques and future directions. The simple and practical Visual Analogue Scale (VAS), the Leicester Cough Questionnaire (LCQ), and the Cough Specific Quality of Life Questionnaire (CQLQ) are the most widely used self-reported questionnaires for evaluating and quantifying cough severity. The Hull Airway Reflux Questionnaire (HARQ) is a tool to elucidate the constellation of symptoms underlying the diagnosis of chronic cough. Chemical excitation tests are widely used to explore the pathophysiological mechanisms of the cough reflex, such as capsaicin, citric acid and adenosine triphosphate (ATP) challenge test. Cough frequency is an ideal primary endpoint for clinical research, but the application of cough counters has been limited in clinical practice by the high cost and reliance on aural validation. The ongoing development of cough detection technology for smartphone apps and wearable devices will hopefully simplify cough counting, thus transitioning it from niche research to a widely available clinical application.
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Affiliation(s)
- Mengru Zhang
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK;,Department of Pulmonary and Critical Care Medicine, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dominic L. Sykes
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Kayleigh Brindle
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Laura R. Sadofsky
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
| | - Alyn H. Morice
- Centre for Clinical Science, Respiratory Medicine, Hull York Medical School, University of Hull, Castle Hill Hospital, Cottingham, East Yorkshire, UK
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7
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de Buisonjé DR, Reijnders T, Cohen Rodrigues TR, Prabhakaran S, Kowatsch T, Lipman SA, Bijmolt THA, Breeman LD, Janssen VR, Kraaijenhagen RA, Kemps HMC, Evers AWM. Investigating Rewards and Deposit Contract Financial Incentives for Physical Activity Behavior Change Using a Smartphone App: Randomized Controlled Trial. J Med Internet Res 2022; 24:e38339. [PMID: 36201384 DOI: 10.2196/38339] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/04/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Financial incentive interventions for improving physical activity have proven to be effective but costly. Deposit contracts (in which participants pledge their own money) could be an affordable alternative. In addition, deposit contracts may have superior effects by exploiting the power of loss aversion. Previous research has often operationalized deposit contracts through loss framing a financial reward (without requiring a deposit) to mimic the feelings of loss involved in a deposit contract. OBJECTIVE This study aimed to disentangle the effects of incurring actual losses (through self-funding a deposit contract) and loss framing. We investigated whether incentive conditions are more effective than a no-incentive control condition, whether deposit contracts have a lower uptake than financial rewards, whether deposit contracts are more effective than financial rewards, and whether loss frames are more effective than gain frames. METHODS Healthy participants (N=126) with an average age of 22.7 (SD 2.84) years participated in a 20-day physical activity intervention. They downloaded a smartphone app that provided them with a personalized physical activity goal and either required a €10 (at the time of writing: €1=US $0.98) deposit up front (which could be lost) or provided €10 as a reward, contingent on performance. Daily feedback on incentive earnings was provided and framed as either a loss or gain. We used a 2 (incentive type: deposit or reward) × 2 (feedback frame: gain or loss) between-subjects factorial design with a no-incentive control condition. Our primary outcome was the number of days participants achieved their goals. The uptake of the intervention was a secondary outcome. RESULTS Overall, financial incentive conditions (mean 13.10, SD 6.33 days goal achieved) had higher effectiveness than the control condition (mean 8.00, SD 5.65 days goal achieved; P=.002; ηp2=0.147). Deposit contracts had lower uptake (29/47, 62%) than rewards (50/50, 100%; P<.001; Cramer V=0.492). Furthermore, 2-way analysis of covariance showed that deposit contracts (mean 14.88, SD 6.40 days goal achieved) were not significantly more effective than rewards (mean 12.13, SD 6.17 days goal achieved; P=.17). Unexpectedly, loss frames (mean 10.50, SD 6.22 days goal achieved) were significantly less effective than gain frames (mean 14.67, SD 5.95 days goal achieved; P=.007; ηp2=0.155). CONCLUSIONS Financial incentives help increase physical activity, but deposit contracts were not more effective than rewards. Although self-funded deposit contracts can be offered at low cost, low uptake is an important obstacle to large-scale implementation. Unexpectedly, loss framing was less effective than gain framing. Therefore, we urge further research on their boundary conditions before using loss-framed incentives in practice. Because of limited statistical power regarding some research questions, the results of this study should be interpreted with caution, and future work should be done to confirm these findings. TRIAL REGISTRATION Open Science Framework Registries osf.io/34ygt; https://osf.io/34ygt.
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Affiliation(s)
- David R de Buisonjé
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Thomas Reijnders
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
- Department of Human-Centered Design, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Talia R Cohen Rodrigues
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Santhanam Prabhakaran
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- 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
| | - Stefan A Lipman
- Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Tammo H A Bijmolt
- Faculty of Economics and Business, University of Groningen, Groningen, Netherlands
| | - Linda D Breeman
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Veronica R Janssen
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Hareld M C Kemps
- Department of Cardiology, Máxima Medical Center, Veldhoven, Netherlands
| | - Andrea W M Evers
- Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, Netherlands
- Medical Delta, Leiden University, Technical University Delft, Erasmus University, Delft, Netherlands
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Jokic S, Cleres D, Rassouli F, Steurer-Stey C, Puhan MA, Brutsche M, Fleisch E, Barata F. TripletCough: Cougher Identification and Verification from Contact-Free Smartphone-Based Audio Recordings Using Metric Learning. IEEE J Biomed Health Inform 2022; 26:2746-2757. [PMID: 35196248 DOI: 10.1109/jbhi.2022.3152944] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.
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Vertigan AE, Kapela SL, Birring SS, Gibson PG. Feasibility and clinical utility of ambulatory cough monitoring in an outpatient clinical setting: a real-world retrospective evaluation. ERJ Open Res 2021; 7:00319-2021. [PMID: 34616839 PMCID: PMC8488350 DOI: 10.1183/23120541.00319-2021] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/09/2021] [Indexed: 12/03/2022] Open
Abstract
RESEARCH QUESTION Objective quantification of cough is rarely utilised outside of research settings and the role of cough frequency monitoring in clinical practice has not been established. This study examined the clinical utility of cough frequency monitoring in an outpatient clinical setting. METHODS The study involved a retrospective review of cough monitor data. Participants included 174 patients referred for treatment of cough and upper airway symptoms (103 chronic cough; 50 inducible laryngeal obstruction; 21 severe asthma) and 15 controls. Measures, taken prior to treatment, included 24-h ambulatory cough frequency using the Leicester Cough Monitor, the Leicester Cough Questionnaire and Laryngeal Hypersensitivity Questionnaire. Post-treatment data were available for 50 participants. Feasibility and clinical utility were also reported. RESULTS Analysis time per recording was up to 10 min. 75% of participants could use the monitors correctly, and most (93%) recordings were interpretable. The geometric mean cough frequency in patients was 10.1±2.9 (mean±sd) compared to 2.4±2.0 for healthy controls (p=0.003). There was no significant difference in cough frequency between clinical groups (p=0.080). Cough frequency decreased significantly following treatment (p<0.001). There was a moderate correlation between cough frequency and both cough quality of life and laryngeal hypersensitivity. Cough frequency monitoring was responsive to therapy and able to discriminate differences in cough frequency between diseases. CONCLUSION While ambulatory cough frequency monitoring remains a research tool, it provides useful clinical data that can assist in patient management. Logistical issues may preclude use in some clinical settings, and additional time needs to be allocated to the process.
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Affiliation(s)
- Anne E. Vertigan
- Speech Pathology, John Hunter Hospital, New Lambton Heights, NSW, Australia
- Priority Centre for Healthy Lungs, The University of Newcastle Hunter Medical Research Institute, New Lambton, NSW, Australia
| | - Sarah L. Kapela
- Speech Pathology, John Hunter Hospital, New Lambton Heights, NSW, Australia
| | - Surinder S. Birring
- Respiratory Medicine, King's College Hospital, London, UK
- Dept of Respiratory Sciences, King's College London, London, UK
| | - Peter G. Gibson
- Priority Centre for Healthy Lungs, The University of Newcastle Hunter Medical Research Institute, New Lambton, NSW, Australia
- Centre of Excellence in Severe Asthma, The University of Newcastle Faculty of Health and Medicine, Callaghan, NSW, Australia
- Dept of Respiratory and Sleep Medicine, John Hunter Hospital, New Lambton Heights, NSW, Australia
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10
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Digital Health Technology and Telemedicine-Based Hospital and Home Programs in Pulmonary Medicine During the COVID-19 Pandemic. Am J Ther 2021; 28:e217-e223. [PMID: 33590991 DOI: 10.1097/mjt.0000000000001342] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND The current coronavirus disease 2019 (COVID-19) pandemic has caused a significant strain on medical resources throughout the world. A major shift to telemedicine and mobile health technologies has now taken on an immediate urgency. Newly developed devices designed for home use have facilitated remote monitoring of various physiologic parameters relevant to pulmonary diseases. These devices have also enabled home-based pulmonary rehabilitation programs. In addition, telemedicine and home care services have been leveraged to rapidly develop acute care hospital-at-home programs for the treatment of mild-to-moderate COVID-19 illness. AREAS OF UNCERTAINTY The benefit of remote monitoring technologies on patient outcomes has not been established in robust trials. Furthermore, the use of these devices, which can increase the burden of care, has not been integrated into current clinical workflows and electronic medical records. Finally, reimbursement for these telemedicine and remote monitoring services is variable. DATA SOURCES Literature review. THERAPEUTIC ADVANCES Advances in digital technology have improved remote monitoring of physiologic parameters relevant to pulmonary medicine. In addition, telemedicine services for the provision of pulmonary rehabilitation and novel hospital-at-home programs have been developed. These new home-based programs have been adapted for COVID-19 and may also be relevant for the management of acute and chronic pulmonary diseases after the pandemic. CONCLUSION Digital remote monitoring of physiologic parameters relevant to pulmonary medicine and novel hospital-at-home programs are feasible and may improve care for patients with acute and chronic respiratory-related disorders.
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11
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Tinschert P, Rassouli F, Barata F, Steurer-Stey C, Fleisch E, Puhan MA, Kowatsch T, Brutsche MH. Nocturnal Cough and Sleep Quality to Assess Asthma Control and Predict Attacks. J Asthma Allergy 2020; 13:669-678. [PMID: 33363391 PMCID: PMC7754262 DOI: 10.2147/jaa.s278155] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/04/2020] [Indexed: 01/11/2023] Open
Abstract
Introduction Objective markers for asthma, that can be measured without extra patient effort, could mitigate current shortcomings in asthma monitoring. We investigated whether smartphone-recorded nocturnal cough and sleep quality can be utilized for the detection of periods with uncontrolled asthma or meaningful changes in asthma control and for the prediction of asthma attacks. Methods We analyzed questionnaire and sensor data of 79 adults with asthma. Data were collected in situ for 29 days by means of a smartphone. Sleep quality and nocturnal cough frequencies were measured every night with the Pittsburgh Sleep Quality Index and by manually annotating coughs from smartphone audio recordings. Primary endpoint was asthma control assessed with a weekly version of the Asthma Control Test. Secondary endpoint was self-reported asthma attacks. Results Mixed-effects regression analyses showed that nocturnal cough and sleep quality were statistically significantly associated with asthma control on a between- and within-patient level (p < 0.05). Decision trees indicated that sleep quality was more useful for detecting weeks with uncontrolled asthma (balanced accuracy (BAC) 68% vs 61%; Δ sensitivity −12%; Δ specificity −2%), while nocturnal cough better detected weeks with asthma control deteriorations (BAC 71% vs 56%; Δ sensitivity 3%; Δ specificity −34%). Cut-offs using both markers predicted asthma attacks up to five days ahead with BACs between 70% and 75% (sensitivities 75 - 88% and specificities 57 - 72%). Conclusion Nocturnal cough and sleep quality have useful properties as markers for asthma control and seem to have prognostic value for the early detection of asthma attacks. Due to the limited study duration per patient and the pragmatic nature of the study, future research is needed to comprehensively evaluate and externally validate the performance of both biomarkers and their utility for asthma self-management.
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Affiliation(s)
- Peter Tinschert
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Claudia Steurer-Stey
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.,mediX Group Practice Zurich, Zurich, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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12
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Rassouli F, Tinschert P, Barata F, Steurer-Stey C, Fleisch E, Puhan MA, Baty F, Kowatsch T, Brutsche MH. Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker. J Asthma Allergy 2020; 13:649-657. [PMID: 33299332 PMCID: PMC7721277 DOI: 10.2147/jaa.s278119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/31/2020] [Indexed: 11/23/2022] Open
Abstract
Introduction The nature of nocturnal cough is largely unknown. It might be a valid marker for asthma control but very few studies characterized it as a basis for better defining its role and its use as clinical marker. This study investigated prevalence and characteristics of nocturnal cough in asthmatics over the course of four weeks. Methods In two centers, 94 adult patients with physician-diagnosed asthma were recruited. Patient-reported outcomes and nocturnal sensor data were collected by a smartphone with a chat-based study app. Results Patients coughed in 53% of 2212 nights (range: 0–345 coughs/night). Median coughs per hour were 0 (IQR 0–1). Nocturnal cough rates showed considerable inter-individual variance. The highest counts were measured in the first 30 min in bed (4.5-fold higher than rest of night). Eighty-six percent of coughs were part of a cough cluster. Clusters consisted of a median of two coughs (IQR 2–4). Nocturnal cough was persistent within patient. Conclusion To the best of the authors’ knowledge, this study is the first to describe prevalence and characteristics of nocturnal cough in asthma over a period of one month, demonstrating that it was a prevalent symptom with large variance between patients and high persistence within patients. Cough events in asthmatics were 4.5 times more frequent within the first 30 min in bed indicating a potential role of positional change, and not more frequent during the early morning hours. An important next step will investigate the association between nocturnal cough and asthma control.
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Affiliation(s)
- Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Peter Tinschert
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Claudia Steurer-Stey
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.,mediX Group Practice Zurich, Zurich, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Florent Baty
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland.,Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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13
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Barata F, Tinschert P, Rassouli F, Steurer-Stey C, Fleisch E, Puhan MA, Brutsche M, Kotz D, Kowatsch T. Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study. J Med Internet Res 2020; 22:e18082. [PMID: 32459641 PMCID: PMC7388043 DOI: 10.2196/18082] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/27/2020] [Accepted: 04/30/2020] [Indexed: 01/22/2023] Open
Abstract
Background 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. Objective 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. Methods 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. Results 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%. Conclusions 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.
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Affiliation(s)
- Filipe Barata
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
| | - Peter Tinschert
- Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Frank Rassouli
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Claudia Steurer-Stey
- Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland.,mediX Group Practice, Zurich, Switzerland
| | - Elgar Fleisch
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Milo Alan Puhan
- Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland
| | - Martin Brutsche
- Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - David Kotz
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Department of Computer Science, Dartmouth College, Hanover, NH, United States.,Center for Technology and Digital Health, Dartmouth College, Hanover, NH, United States
| | - Tobias Kowatsch
- Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.,Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
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14
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Bousquet J, Ansotegui IJ, Anto JM, Arnavielhe S, Bachert C, Basagaña X, Bédard A, Bedbrook A, Bonini M, Bosnic-Anticevich S, Braido F, Cardona V, Czarlewski W, Cruz AA, Demoly P, De Vries G, Dramburg S, Mathieu-Dupas E, Erhola M, Fokkens WJ, Fonseca JA, Haahtela T, Hellings PW, Illario M, Ivancevich JC, Jormanainen V, Klimek L, Kuna P, Kvedariene V, Laune D, Larenas-Linnemann D, Lourenço O, Onorato GL, Matricardi PM, Melén E, Mullol J, Papadopoulos NG, Pfaar O, Pham-Thi N, Sheikh A, Tan R, To T, Tomazic PV, Toppila-Salmi S, Tripodi S, Wallace D, Valiulis A, van Eerd M, Ventura MT, Yorgancioglu A, Zuberbier T. Mobile Technology in Allergic Rhinitis: Evolution in Management or Revolution in Health and Care? THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2019; 7:2511-2523. [PMID: 31445223 DOI: 10.1016/j.jaip.2019.07.044] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/02/2019] [Accepted: 07/25/2019] [Indexed: 01/08/2023]
Abstract
Smart devices and Internet-based applications (apps) are largely used in allergic rhinitis and may help to address some unmet needs. However, these new tools need to first of all be tested for privacy rules, acceptability, usability, and cost-effectiveness. Second, they should be evaluated in the frame of the digital transformation of health, their impact on health care delivery, and health outcomes. This review (1) summarizes some existing mobile health apps for allergic rhinitis and reviews those in which testing has been published, (2) discusses apps that include risk factors of allergic rhinitis, (3) examines the impact of mobile health apps in phenotype discovery, (4) provides real-world evidence for care pathways, and finally (5) discusses mobile health tools enabling the digital transformation of health and care, empowering citizens, and building a healthier society.
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Affiliation(s)
- Jean Bousquet
- University Hospital, Montpellier, France; MACVIA-France, Fondation partenariale FMC VIA-LR, Montpellier, France; VIMA, INSERM U 1168, VIMA: Ageing and chronic diseases Epidemiological and public health approaches, Villejuif, Université Versailles St-Quentin-en-Yvelines, Montigny le Bretonneux, France; Euforea, Brussels, Belgium; Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Dermatology and Allergy, Berlin Institute of Health, Comprehensive Allergy Center, Berlin, Germany.
| | - Ignacio J Ansotegui
- Department of Allergy and Immunology, Hospital Quirónsalud Bizkaia, Erandio, Spain
| | - Josep M Anto
- ISGlobAL, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Hospital del Mar Research Institute, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | | | - Claus Bachert
- Upper Airways Research Laboratory, ENT Department, Ghent University Hospital, Ghent, Belgium
| | - Xavier Basagaña
- ISGlobAL, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Annabelle Bédard
- ISGlobAL, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Anna Bedbrook
- MACVIA-France, Fondation partenariale FMC VIA-LR, Montpellier, France
| | - Matteo Bonini
- UOC Pneumologia, Istituto di Medicina Interna, F Policlinico Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; National Heart and Lung Institute, Royal Brompton Hospital & Imperial College London, London, United Kingdom
| | - Sinthia Bosnic-Anticevich
- Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW, Australia; Woolcock Emphysema Centre and Sydney Local Health District, Glebe, NSW, Australia
| | - Fulvio Braido
- Department of Internal Medicine (DiMI), University of Genoa, Genova, Italy; IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Vicky Cardona
- Allergy Section, Department of Internal Medicine, Hospital Vall d'Hebron & ARADyAL Research Network, Barcelona, Spain
| | | | - Alvaro A Cruz
- ProAR-Nucleo de Excelencia em Asma, Federal University of Bahia, Salvador, Brazil; WHO GARD Planning Group, Salvador, Brazil
| | - Pascal Demoly
- Department of Pulmonology, Division of Allergy, Hôpital Arnaud de Villeneuve, University Hospital of Montpellier, Montpellier, France; Equipe EPAR-IPLESP, Sorbonne Université, Paris, France
| | | | - Stephanie Dramburg
- Department of Pediatric Pulmonology, Immunology and Intensive Care Medicine, Charité-University Medicine Berlin, Berlin, Germany
| | | | - Marina Erhola
- National Institute for Health and Welfare, Helsinki, Finland
| | - Wytske J Fokkens
- Euforea, Brussels, Belgium; Department of Otorhinolaryngology, Amsterdam University Medical Centres, AMC, Amsterdam, the Netherlands
| | - Joao A Fonseca
- CINTESIS, Center for Research in Health Technology and Information Systems, Faculdade de Medicina da Universidade do Porto, Porto, Portugal; Medida, Lda, Porto, Portugal
| | - Tari Haahtela
- Skin and Allergy Hospital, Helsinki University Hospital, and University of Helsinki, Helsinki, Finland
| | - Peter W Hellings
- Euforea, Brussels, Belgium; Department of Otorhinolaryngology, University Hospitals Leuven, Leuven, Belgium; Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Maddalena Illario
- Division for Health Innovation, Campania Region and Federico II University Hospital Naples (R&D and DISMET), Naples, Italy
| | | | | | - Ludger Klimek
- Center for Rhinology and Allergology, Wiesbaden, Germany
| | - Piotr Kuna
- Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Lodz, Poland
| | - Violeta Kvedariene
- Institute of Biomedical Sciences, Department of Pathology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania; Institute of Clinical Medicine, Clinic of Chest Diseases and Allergology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Désirée Larenas-Linnemann
- Center of Excellence in Asthma and Allergy, Médica Sur Clinical Foundation and Hospital, México City, Mexico
| | - Olga Lourenço
- Faculty of Health Sciences and CICS-UBI, Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal
| | | | - Paolo M Matricardi
- AG Molecular Allergology and Immunomodulation, Department of Pediatric Pneumology and Immunology, Charité Medical University, Berlin, Germany
| | - Erik Melén
- E. Sachs' Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joaquim Mullol
- Rhinology Unit & Smell Clinic, ENT Department, Hospital Clínic, Barcelona, Spain; Clinical & Experimental Respiratory Immunoallergy, IDIBAPS, CIBERES, University of Barcelona, Barcelona, Spain
| | - Nikos G Papadopoulos
- Division of Infection, Immunity & Respiratory Medicine, Royal Manchester Children's Hospital, University of Manchester, Manchester, United Kingdom; Allergy Department, 2nd Pediatric Clinic, Athens General Children's Hospital "P&A Kyriakou", University of Athens, Athens, Greece
| | - Oliver Pfaar
- Department of Otorhinolaryngology, Head and Neck Surgery, Section of Rhinology and Allergy, University Hospital Marburg, Phillipps-Universität Marburg, Marburg, Germany
| | - Nhân Pham-Thi
- Allergy Department, Pasteur Institute, Paris, France
| | - Aziz Sheikh
- The Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Rachel Tan
- Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW, Australia; Woolcock Emphysema Centre and Sydney Local Health District, Glebe, NSW, Australia
| | - Teresa To
- Sidkkids Hospital and Institute of Health Policy, Management and Evaluation, Toronto, Ontario, Canada
| | | | - Sanna Toppila-Salmi
- Skin and Allergy Hospital, Helsinki University Hospital, and University of Helsinki, Helsinki, Finland
| | | | - Dana Wallace
- Nova Southeastern University, Fort Lauderdale, Fla
| | - Arunas Valiulis
- Institute of Clinical Medicine, Clinic of Children's Diseases, Vilnius University, Vilnius, Lithuania; Institute of Health Sciences, Department of Public Health, Vilnius University, Vilnius, Lithuania; European Academy of Paediatrics (EAP/UEMS-SP), Brussels, Belgium
| | | | - Maria Teresa Ventura
- University of Bari Medical School, Unit of Geriatric Immunoallergology, Bari, Italy
| | - Arzu Yorgancioglu
- Department of Pulmonary Diseases, Faculty of Medicine, Celal Bayar University, Manisa, Turkey
| | - Torsten Zuberbier
- Charité-Universitätsmedizin Berlin, Berlin, Germany; Corporate member of Freie Universität Berlin, Humboldt-Uniersität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy-Centre, Department of Dermatology and Allergy, Berlin, Germany; Member of GA(2)LEN, Berlin, Germany
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