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Hijma HJ, Zhuparris A, van Hoogdalem EJ, Cohen AF. Disproportional inflation of clinical trial costs: why we should care, and what we should do about it. Nat Rev Drug Discov 2024; 23:85-86. [PMID: 38195708 DOI: 10.1038/d41573-024-00002-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
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Rousel J, Nădăban A, Saghari M, Pagan L, Zhuparris A, Theelen B, Gambrah T, van der Wall HEC, Vreeken RJ, Feiss GL, Niemeyer-van der Kolk T, Burggraaf J, van Doorn MBA, Bouwstra JA, Rissmann R. Lesional skin of seborrheic dermatitis patients is characterized by skin barrier dysfunction and correlating alterations in the stratum corneum ceramide composition. Exp Dermatol 2024; 33:e14952. [PMID: 37974545 DOI: 10.1111/exd.14952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/21/2023] [Accepted: 10/01/2023] [Indexed: 11/19/2023]
Abstract
Seborrheic dermatitis (SD) is a chronic inflammatory skin disease characterized by erythematous papulosquamous lesions in sebum rich areas such as the face and scalp. Its pathogenesis appears multifactorial with a disbalanced immune system, Malassezia driven microbial involvement and skin barrier perturbations. Microbial involvement has been well described in SD, but skin barrier involvement remains to be properly elucidated. To determine whether barrier impairment is a critical factor of inflammation in SD alongside microbial dysbiosis, a cross-sectional study was performed in 37 patients with mild-to-moderate facial SD. Their lesional and non-lesional skin was comprehensively and non-invasively assessed with standardized 2D-photography, optical coherence tomography (OCT), microbial profiling including Malassezia species identification, functional skin barrier assessments and ceramide profiling. The presence of inflammation was established through significant increases in erythema, epidermal thickness, vascularization and superficial roughness in lesional skin compared to non-lesional skin. Lesional skin showed a perturbed skin barrier with an underlying skewed ceramide subclass composition, impaired chain elongation and increased chain unsaturation. Changes in ceramide composition correlated with barrier impairment indicating interdependency of the functional barrier and ceramide composition. Lesional skin showed significantly increased Staphylococcus and decreased Cutibacterium abundances but similar Malassezia abundances and mycobial composition compared to non-lesional skin. Principal component analysis highlighted barrier properties as main discriminating features. To conclude, SD is associated with skin barrier dysfunction and changes in the ceramide composition. No significant differences in the abundance of Malassezia were observed. Restoring the cutaneous barrier might be a valid therapeutic approach in the treatment of facial SD.
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Affiliation(s)
- Jannik Rousel
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Andreea Nădăban
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Mahdi Saghari
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
| | - Lisa Pagan
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
| | - Ahnjili Zhuparris
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands
| | - Bart Theelen
- Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands
| | - Tom Gambrah
- Centre for Human Drug Research, Leiden, The Netherlands
| | | | - Rob J Vreeken
- Maastricht Multimodal Molecular Imaging Institute, Maastricht University, Maastricht, The Netherlands
| | | | | | - Jacobus Burggraaf
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
| | - Martijn B A van Doorn
- Centre for Human Drug Research, Leiden, The Netherlands
- Department of Dermatology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Joke A Bouwstra
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Robert Rissmann
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Leiden University Medical Center, Leiden, The Netherlands
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Zhuparris A, Maleki G, van Londen L, Koopmans I, Aalten V, Yocarini IE, Exadaktylos V, van Hemert A, Cohen A, Gal P, Doll RJ, Groeneveld GJ, Jacobs G, Kraaij W. A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity. Sci Rep 2023; 13:18844. [PMID: 37914808 PMCID: PMC10620211 DOI: 10.1038/s41598-023-46075-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 10/27/2023] [Indexed: 11/03/2023] Open
Abstract
Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.
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Affiliation(s)
- Ahnjili Zhuparris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands.
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands.
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands.
| | - Ghobad Maleki
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | | | - Ingrid Koopmans
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | - Vincent Aalten
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands
| | - Vasileios Exadaktylos
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
| | - Albert van Hemert
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | - Adam Cohen
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | - Pim Gal
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | - Robert-Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Leiden University Medical Centre (LUMC), Leiden University, Leiden, The Netherlands
| | - Gabriël Jacobs
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333CL, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands
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Ten Voorde W, Saghari M, Boltjes J, de Kam ML, Zhuparris A, Feiss G, Buters TP, Prens EP, Damman J, Niemeyer-van der Kolk T, Moerland M, Burggraaf J, van Doorn MBA, Rissmann R. A multimodal, comprehensive characterization of a cutaneous wound model in healthy volunteers. Exp Dermatol 2023. [PMID: 37051698 DOI: 10.1111/exd.14808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/13/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023]
Abstract
Development of pharmacological interventions for wound treatment is challenging due to both poorly understood wound healing mechanisms and heterogeneous patient populations. A standardized and well-characterized wound healing model in healthy volunteers is needed to aid in-depth pharmacodynamic and efficacy assessments of novel compounds. The current study aims to objectively and comprehensively characterize skin punch biopsy-induced wounds in healthy volunteers with an integrated, multimodal test battery. Eighteen (18) healthy male and female volunteers received three biopsies on the lower back, which were left to heal without intervention. The wound healing process was characterized using a battery of multimodal, non-invasive methods as well as histology and qPCR analysis in re-excised skin punch biopsies. Biophysical and clinical imaging read-outs returned to baseline values in 28 days. Optical coherence tomography detected cutaneous differences throughout the wound healing progression. qPCR analysis showed involvement of proteins, quantified as mRNA fold increase, in one or more healing phases. All modalities used in the study were able to detect differences over time. Using multidimensional data visualization, we were able to create a distinction between wound healing phases. Clinical and histopathological scoring were concordant with non-invasive imaging read-outs. This well-characterized wound healing model in healthy volunteers will be a valuable tool for the standardized testing of novel wound healing treatments.
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Affiliation(s)
- Wouter Ten Voorde
- Centre for Human Drug Research, Leiden, the Netherlands
- Leiden University Medical Centre, Leiden, the Netherlands
| | - Mahdi Saghari
- Centre for Human Drug Research, Leiden, the Netherlands
- Leiden University Medical Centre, Leiden, the Netherlands
| | - Jiry Boltjes
- Centre for Human Drug Research, Leiden, the Netherlands
| | | | | | - Gary Feiss
- Cutanea Life Sciences, Wayne, Pennsylvania, USA
| | - Thomas P Buters
- Centre for Human Drug Research, Leiden, the Netherlands
- Leiden University Medical Centre, Leiden, the Netherlands
| | - Errol P Prens
- Department of Dermatology Erasmus Medical Centre, Rotterdam, the Netherlands
| | - Jeffrey Damman
- Department of Pathology Erasmus Medical Centre, Rotterdam, the Netherlands
| | | | | | - Jacobus Burggraaf
- Centre for Human Drug Research, Leiden, the Netherlands
- Leiden University Medical Centre, Leiden, the Netherlands
- Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
| | | | - Robert Rissmann
- Centre for Human Drug Research, Leiden, the Netherlands
- Leiden University Medical Centre, Leiden, the Netherlands
- Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
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Zhuparris A, Maleki G, Koopmans I, Doll RJ, Voet N, Kraaij W, Cohen A, van Brummelen E, De Maeyer JH, Groeneveld GJ. Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study. JMIR Form Res 2023; 7:e41178. [PMID: 36920465 PMCID: PMC10131943 DOI: 10.2196/41178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions. OBJECTIVE We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic. METHODS In total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data. RESULTS The single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively. CONCLUSIONS We demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period. TRIAL REGISTRATION ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735.
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Affiliation(s)
| | - Ghobad Maleki
- Centre for Human Drug Research (CHDR), Leiden, Netherlands
| | | | - Robert J Doll
- Centre for Human Drug Research (CHDR), Leiden, Netherlands
| | - Nicoline Voet
- Department of Rehabilitation, Rehabilitation Center Klimmendaal, Nijmegen, Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands
| | - Adam Cohen
- Centre for Human Drug Research (CHDR), Leiden, Netherlands
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Maleki G, Zhuparris A, Koopmans I, Doll RJ, Voet N, Cohen A, van Brummelen E, Groeneveld GJ, De Maeyer J. Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study. JMIR Form Res 2022; 6:e31775. [PMID: 36098990 PMCID: PMC9516375 DOI: 10.2196/31775] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients’ quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD. Objective This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls. Methods In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants’ FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants’ smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification. Results Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification. Conclusions Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity. Trial Registration ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735
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Affiliation(s)
- Ghobad Maleki
- Centre for Human Drug Research, Leiden, Netherlands
- Leiden University Medical Center, Leiden, Netherlands
| | - Ahnjili Zhuparris
- Centre for Human Drug Research, Leiden, Netherlands
- Leiden University Medical Center, Leiden, Netherlands
| | | | | | - Nicoline Voet
- Radboud University Medical Center, Nijmegen, Netherlands
- Klimmendaal, Arnhem, Netherlands
| | - Adam Cohen
- Centre for Human Drug Research, Leiden, Netherlands
| | | | - Geert Jan Groeneveld
- Centre for Human Drug Research, Leiden, Netherlands
- Leiden University Medical Center, Leiden, Netherlands
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Kruizinga MD, Zhuparris A, Dessing E, Krol FJ, Sprij AJ, Doll RJ, Stuurman FE, Exadaktylos V, Driessen GJA, Cohen AF. Development and technical validation of a smartphone-based pediatric cough detection algorithm. Pediatr Pulmonol 2022; 57:761-767. [PMID: 34964557 PMCID: PMC9306830 DOI: 10.1002/ppul.25801] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children. METHODS The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions. RESULTS The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source. CONCLUSION This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | | | - Eva Dessing
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Fas J Krol
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Arwen J Sprij
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | | | | | | | - Gertjan J A Driessen
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Department of pediatrics, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
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Kruizinga MD, Essers E, Stuurman FE, Yavuz Y, de Kam ML, Zhuparris A, Janssens HM, Groothuis I, Sprij AJ, Nuijsink M, Cohen AF, Driessen GJA. Clinical validation of digital biomarkers for pediatric patients with asthma and cystic fibrosis - Potential for clinical trials and clinical care. Eur Respir J 2021; 59:13993003.00208-2021. [PMID: 34887326 DOI: 10.1183/13993003.00208-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/10/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Digital biomarkers are a promising novel method to capture clinical data in a home-setting. However, clinical validation prior to implementation is of vital importance. The aim of this study was to clinically validate physical activity, heart rate, sleep and FEV1 as digital biomarkers measured by a smartwatch and portable spirometer in children with asthma and cystic fibrosis (CF). METHODS This was a prospective cohort study including 60 children with asthma and 30 children with CF (age 6-16). Participants wore a smartwatch, performed daily spirometry at home and completed a daily symptom questionnaire for 28-days. Physical activity, heart rate, sleep and FEV1 were considered candidate digital endpoints. Data from 128 healthy children was used for comparison. Reported outcomes were compliance, difference between patients and controls, correlation with disease-activity and potential to detect clinical events. Analysis was performed with linear mixed effect models. RESULTS Median compliance was 88%. On average, patients exhibited lower physical activity and FEV1 compared to healthy children, whereas the heart rate of children with asthma was higher compared to healthy children. Days with a higher symptom score were associated with lower physical activity for children with uncontrolled asthma and CF. Furthermore, FEV1 was lower and (nocturnal) heart rate was higher for both patient groups on days with more symptoms. Candidate biomarkers and showed a distinct pattern before- and after a pulmonary exacerbation. CONCLUSION Portable spirometer- and smartwatch-derived digital biomarkers show promise as candidate endpoints for use in clinical trials or clinical care in pediatric lung disease.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, the Netherlands .,Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Esmée Essers
- Centre for Human Drug Research, Leiden, the Netherlands.,Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Frederik E Stuurman
- Centre for Human Drug Research, Leiden, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Yalçin Yavuz
- Centre for Human Drug Research, Leiden, the Netherlands
| | | | | | - Hettie M Janssens
- Division of Respiratory Medicine and Allergology, Department of Pediatrics, Erasmus Medical Centre/Sophia Children's Hospital, University Hospital Rotterdam, Rotterdam, The Netherlands
| | - Iris Groothuis
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Arwen J Sprij
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Marianne Nuijsink
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Gertjan J A Driessen
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands.,Department of pediatrics, Maastricht University Medical Centre, Maastricht, the Netherlands
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Davies EL, Puljevic C, Gilchrist G, Potts L, Zhuparris A, Maier LJ, Barratt MJ, Winstock AR, Ferris JA. Impacts of changes in alcohol consumption patterns during the first 2020 COVID-19 restrictions for people with and without mental health and neurodevelopmental conditions: A cross sectional study in 13 countries. Int J Drug Policy 2021; 101:103563. [PMID: 34952280 PMCID: PMC8692164 DOI: 10.1016/j.drugpo.2021.103563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/01/2021] [Accepted: 12/05/2021] [Indexed: 02/07/2023]
Abstract
Background The initial period of COVID-19-related restrictions affected substance use in some population groups. We explored how changes in alcohol use at the beginning of the pandemic impacted the health and wellbeing of people with and without mental health and neurodevelopmental conditions (MHDCs). Methods Data came from the Global Drug Survey Special Edition on COVID-19 conducted in May-June 2020. Measured were; changes in drinking compared to February 2020 (pre-COVID-19 restrictions), reasons for changes, and impact on physical health, mental health, relationships, finances, work/study, and enjoyment. This study included 38,141 respondents (median age = 32 IQR 25-45; 51.9% cis man; 47.8% cis woman; 1.2% trans/non-binary; 30.2% with MHDCs e.g. depression 20.0%, anxiety 16.3%, ADHD 3.8%, PTSD 3.3%). Results A third (35.3%) of respondents with MHDCs and 17.8% without MHDCs indicated that increased drinking affected their mental health negatively (p<.001); 44.2% of respondents with MHDCS compared to 32.6% without MHDCs said it affected their physical health negatively (p<.001). Reduced drinking was associated with better mental health among a fifth (21.1%) of respondents with MHDCS and 14.4% without MHDCs (p<.001). Age, relationship status, living arrangements, employment, coping and distress were significant predictors of increases in drinking. Conclusion Among people with MHDCS, reduced alcohol consumption was associated with better mental health, while the negative effects of increased drinking were more pronounced when compared to people without MHDCS. When supporting people in reducing alcohol consumption during uncertain times, people with MHDCS may need additional support, alongside those experiencing greater levels of distress.
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Affiliation(s)
- Emma L Davies
- Centre for Psychological Research, Oxford Brookes University, UK.
| | - Cheneal Puljevic
- Centre for Health Services Research, The University of Queensland, Australia; School of Public Health, The University of Queensland, Australia
| | | | | | | | - Larissa J Maier
- Department of Clinical Pharmacy, University of California, San Francisco, USA
| | - Monica J Barratt
- Social and Global Studies Centre and Digital Ethnography Research Centre, RMIT University, Melbourne, Vic, Australia; National Drug and Alcohol Research Centre, UNSW Sydney, NSW, Australia
| | | | - Jason A Ferris
- Centre for Health Services Research, The University of Queensland, Australia
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10
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Prins S, Zhuparris A, Hart EP, Doll RJ, Groeneveld GJ. A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation. Alzheimers Res Ther 2021; 13:132. [PMID: 34274005 PMCID: PMC8286577 DOI: 10.1186/s13195-021-00874-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/04/2021] [Indexed: 11/10/2022]
Abstract
Background In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer’s disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs. Methods Healthy elderly subjects (n = 200; age 65–70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier. Results Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65–86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65–83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10). Conclusion This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs. Trial registration ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered). Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00874-9.
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Affiliation(s)
- Samantha Prins
- Centre for Human drug Research, Leiden, the Netherlands.,Leiden University Medical Center, Leiden, the Netherlands
| | | | - Ellen P Hart
- Centre for Human drug Research, Leiden, the Netherlands
| | | | - Geert Jan Groeneveld
- Centre for Human drug Research, Leiden, the Netherlands. .,Leiden University Medical Center, Leiden, the Netherlands.
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11
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Kruizinga MD, Moll A, Zhuparris A, Ziagkos D, Stuurman FE, Nuijsink M, Cohen AF, Driessen GJA. Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers. Respiration 2021; 100:979-988. [PMID: 34004601 DOI: 10.1159/000516328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/10/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device. METHODS In this study, patients admitted due to pneumonia (n = 30), preschool wheezing (n = 30), and asthma exacerbation (AE; n = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day "healthy" period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores. RESULTS Median compliance was 47% (interquartile range [IQR] 33-81%) during the entire study period, 68% (IQR 54-91%) during the recovery period, and 28% (IQR 0-74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia. CONCLUSIONS The digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Allison Moll
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | | | | | - Frederik E Stuurman
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Marianne Nuijsink
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Gertjan J A Driessen
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Maastricht University Medical Centre, Leiden, The Netherlands
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12
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Kruizinga MD, van der Heide N, Moll A, Zhuparris A, Yavuz Y, de Kam ML, Stuurman FE, Cohen AF, Driessen GJA. Towards remote monitoring in pediatric care and clinical trials-Tolerability, repeatability and reference values of candidate digital endpoints derived from physical activity, heart rate and sleep in healthy children. PLoS One 2021; 16:e0244877. [PMID: 33411722 PMCID: PMC7790377 DOI: 10.1371/journal.pone.0244877] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/17/2020] [Indexed: 12/26/2022] Open
Abstract
Background Digital devices and wearables allow for the measurement of a wide range of health-related parameters in a non-invasive manner, which may be particularly valuable in pediatrics. Incorporation of such parameters in clinical trials or care as digital endpoint could reduce the burden for children and their parents but requires clinical validation in the target population. This study aims to determine the tolerability, repeatability, and reference values of novel digital endpoints in healthy children. Methods Apparently healthy children (n = 175, 46% male) aged 2–16 were included. Subjects were monitored for 21 days using a home-monitoring platform with several devices (smartwatch, spirometer, thermometer, blood pressure monitor, scales). Endpoints were analyzed with a mixed effects model, assessing variables that explained within- and between-subject variability. Endpoints based on physical activity, heart rate, and sleep-related parameters were included in the analysis. For physical-activity-related endpoints, a sample size needed to detect a 15% increase was calculated. Findings Median compliance was 94%. Variability in each physical activity-related candidate endpoint was explained by age, sex, watch wear time, rain duration per day, average ambient temperature, and population density of the city of residence. Estimated sample sizes for candidate endpoints ranged from 33–110 per group. Daytime heart rate, nocturnal heart rate and sleep duration decreased as a function of age and were comparable to reference values published in the literature. Conclusions Wearable- and portable devices are tolerable for pediatric subjects. The raw data, models and reference values presented here can be used to guide further validation and, in the future, clinical trial designs involving the included measures.
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Affiliation(s)
- M. D. Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands
- Juliana Children’s Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
- Leiden University Medical Centre, Leiden, The Netherlands
- * E-mail:
| | - N. van der Heide
- Centre for Human Drug Research, Leiden, The Netherlands
- Juliana Children’s Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - A. Moll
- Centre for Human Drug Research, Leiden, The Netherlands
- Juliana Children’s Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - A. Zhuparris
- Centre for Human Drug Research, Leiden, The Netherlands
| | - Y. Yavuz
- Centre for Human Drug Research, Leiden, The Netherlands
| | - M. L. de Kam
- Centre for Human Drug Research, Leiden, The Netherlands
| | - F. E. Stuurman
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden University Medical Centre, Leiden, The Netherlands
| | - A. F. Cohen
- Centre for Human Drug Research, Leiden, The Netherlands
- Leiden University Medical Centre, Leiden, The Netherlands
| | - G. J. A. Driessen
- Juliana Children’s Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
- Maastricht University Medical Centre, Maastricht, The Netherlands
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13
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Sverdlov O, Curcic J, Hannesdottir K, Gou L, De Luca V, Ambrosetti F, Zhang B, Praestgaard J, Vallejo V, Dolman A, Gomez-Mancilla B, Biliouris K, Deurinck M, Cormack F, Anderson JJ, Bott NT, Peremen Z, Issachar G, Laufer O, Joachim D, Jagesar RR, Jongs N, Kas MJ, Zhuparris A, Zuiker R, Recourt K, Zuilhof Z, Cha JH, Jacobs GE. A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression. Front Psychiatry 2021; 12:640741. [PMID: 34025472 PMCID: PMC8136319 DOI: 10.3389/fpsyt.2021.640741] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/23/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.
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Affiliation(s)
| | - Jelena Curcic
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Liangke Gou
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Bingsong Zhang
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC, United States
| | - Jens Praestgaard
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | - Vanessa Vallejo
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Andrew Dolman
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | | | | | - Mark Deurinck
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - John J Anderson
- Neurotrack Technologies, Inc., Redwood City, CA, United States
| | - Nicholas T Bott
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States
| | | | | | | | | | - Raj R Jagesar
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | - Niels Jongs
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | - Martien J Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | | | - Rob Zuiker
- Centre for Human Drug Research, Leiden, Netherlands
| | | | - Zoë Zuilhof
- Centre for Human Drug Research, Leiden, Netherlands
| | - Jang-Ho Cha
- Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | - Gabriel E Jacobs
- Centre for Human Drug Research, Leiden, Netherlands.,Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands
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Kruizinga MD, Essers E, Stuurman FE, Zhuparris A, van Eik N, Janssens HM, Groothuis I, Sprij AJ, Nuijsink M, Cohen AF, Driessen GJA. Technical validity and usability of a novel smartphone-connected spirometry device for pediatric patients with asthma and cystic fibrosis. Pediatr Pulmonol 2020; 55:2463-2470. [PMID: 32592537 PMCID: PMC7496177 DOI: 10.1002/ppul.24932] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 06/25/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND Diagnosis and follow-up of respiratory diseases traditionally rely on pulmonary function tests (PFTs), which are currently performed in hospitals and require trained personnel. Smartphone-connected spirometers, like the Air Next spirometer, have been developed to aid in the home monitoring of patients with pulmonary disease. The aim of this study was to investigate the technical validity and usability of the Air Next spirometer in pediatric patients. METHODS Device variability was tested with a calibrated syringe. About 90 subjects, aged 6 to 16, were included in a prospective cohort study. Fifty-eight subjects performed conventional spirometry and subsequent Air Next spirometry. The bias and the limits of agreement between the measurements were calculated. Furthermore, subjects used the device for 28 days at home and completed a subject-satisfaction questionnaire at the end of the study period. RESULTS Interdevice variability was 2.8% and intradevice variability was 0.9%. The average difference between the Air Next and conventional spirometry was 40 mL for forced expiratory volume in 1 second (FEV1) and 3 mL for forced vital capacity (FVC). The limits of agreement were -270 mL and +352 mL for FEV1 and -403 mL and +397 mL for FVC. About 45% of FEV1 measurements and 41% of FVC measurements at home were acceptable and reproducible according to American Thoracic Society/European Respiratory Society criteria. Parents scored difficulty, usefulness, and reliability of the device 1.9, 3.5, and 3.8 out of 5, respectively. CONCLUSION The Air Next device shows validity for the measurement of FEV1 and FVC in a pediatric patient population.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Division of Respiratory Medicine and Allergology, Department of Pediatrics, Erasmus Medical Centre/Sophia Children's Hospital, University Hospital Rotterdam, Rotterdam, The Netherlands.,Department of Pediatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Esmée Essers
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - F E Stuurman
- Centre for Human Drug Research, Leiden, The Netherlands.,Department of Clinical Pharmacology and Toxicology, Leiden University Medical Centre, Leiden, The Netherlands
| | | | - Nellie van Eik
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Hettie M Janssens
- Division of Respiratory Medicine and Allergology, Department of Pediatrics, Erasmus Medical Centre/Sophia Children's Hospital, University Hospital Rotterdam, Rotterdam, The Netherlands
| | - Iris Groothuis
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Arwen J Sprij
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Marianne Nuijsink
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands.,Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands
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Prins S, Zhuparris A, Groeneveld GJ. Usefulness of Plasma Amyloid as a Prescreener for the Earliest Alzheimer Pathological Changes Depends on the Study Population. Ann Neurol 2019; 87:154-155. [PMID: 31675127 DOI: 10.1002/ana.25634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 09/27/2019] [Accepted: 10/07/2019] [Indexed: 11/11/2022]
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16
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Hupli A, Berning M, Zhuparris A, Fadiman J. Descriptive assemblage of psychedelic microdosing: Netnographic study of Youtube™ videos and on-going research projects. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.peh.2019.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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