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Tuominen RK, Renko JM. Biomarkers of Parkinson's disease in perspective of early diagnosis and translation of neurotrophic therapies. Basic Clin Pharmacol Toxicol 2024. [PMID: 38973499 DOI: 10.1111/bcpt.14042] [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: 10/27/2023] [Revised: 04/15/2024] [Accepted: 05/28/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder characterized by progressive loss of dopamine neurons and aberrant deposits of alpha-synuclein (α-syn) in the brain. The symptomatic treatment is started after the onset of motor manifestations in a late stage of the disease. Preclinical studies with neurotrophic factors (NTFs) show promising results of disease-modifying neuroprotective or even neurorestorative effects. Four NTFs have entered phase I-II clinical trials with inconclusive outcomes. This is not surprising because the preclinical evidence is from acute early-stage disease models, but the clinical trials included advanced PD patients. To conclude the value of NTF therapies, clinical studies should be performed in early-stage patients with prodromal symptoms, that is, before motor manifestations. In this review, we summarize currently available diagnostic and prognostic biomarkers that could help identify at-risk patients benefiting from NTF therapies. Focus is on biochemical and imaging biomarkers, but also other modalities are discussed. Neuroimaging is the most important diagnostic tool today, but α-syn imaging is not yet viable. Modern techniques allow measuring various forms of α-syn in cerebrospinal fluid, blood, saliva, and skin. Digital biomarkers and artificial intelligence offer new means for early diagnosis and longitudinal follow-up of degenerative brain diseases.
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Affiliation(s)
- Raimo K Tuominen
- Drug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Juho-Matti Renko
- Drug Research Program, Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
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2
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Arteaga-Bracho E, Cosne G, Kanzler C, Karatsidis A, Mazzà C, Penalver-Andres J, Zhu C, Shen C, Erb M K, Freigang M, Lapp HS, Thiele S, Wenninger S, Jung E, Petri S, Weiler M, Kleinschnitz C, Walter MC, Günther R, Campbell N, Belachew S, Hagenacker T. Smartphone-Based Assessment of Mobility and Manual Dexterity in Adult People with Spinal Muscular Atrophy. J Neuromuscul Dis 2024:JND240004. [PMID: 38995798 DOI: 10.3233/jnd-240004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
Background More responsive, reliable, and clinically valid endpoints of disability are essential to reduce size, duration, and burden of clinical trials in adult persons with spinal muscular atrophy (aPwSMA). Objective The aim is to investigate the feasibility of smartphone-based assessments in aPwSMA and provide evidence on the reliability and construct validity of sensor-derived measures (SDMs) of mobility and manual dexterity collected remotely in aPwSMA. Methods Data were collected from 59 aPwSMA (23 walkers, 20 sitters and 16 non-sitters) and 30 age-matched healthy controls (HC). SDMs were extracted from five smartphone-based tests capturing mobility and manual dexterity, which were administered in-clinic and remotely in daily life for four weeks. Reliability (Intraclass Correlation Coefficients, ICC) and construct validity (ability to discriminate between HC and aPwSMA and correlations with Revised Upper Limb Module, RULM and Hammersmith Functional Scale - Expanded HFMSE) were quantified for all SDMs. Results The smartphone-based assessments proved feasible, with 92.1% average adherence in aPwSMA. The SDMs allowed to reliably assess both mobility and dexterity (ICC > 0.75 for 15/22 SDMs). Twenty-one out of 22 SDMs significantly discriminated between HC and aPwSMA. The highest correlations with the RULM were observed for SDMs from the manual dexterity tests in both non-sitters (Typing, ρ= 0.78) and sitters (Pinching, ρ= 0.75). In walkers, the highest correlation was between mobility tests and HFMSE (5 U-Turns, ρ= 0.79). Conclusions This exploratory study provides preliminary evidence for the usability of smartphone-based assessments of mobility and manual dexterity in aPwSMA when deployed remotely in participants' daily life. Reliability and construct validity of SDMs remotely collected in real-life was demonstrated, which is a pre-requisite for their use in longitudinal trials. Additionally, three novel smartphone-based performance outcome assessments were successfully established for aPwSMA. Upon further validation of responsiveness to interventions, this technology holds potential to increase the efficiency of clinical trials in aPwSMA.
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Affiliation(s)
| | | | | | | | | | | | - Cong Zhu
- Biogen Digital Health, Biogen, Cambridge, MA, USA
| | - Changyu Shen
- Biogen Digital Health, Biogen, Cambridge, MA, USA
| | - Kelley Erb M
- Biogen Digital Health, Biogen, Cambridge, MA, USA
| | - Maren Freigang
- Department of Neurology, Dresden University Hospital, Dresden, Germany
| | - Hanna-Sophie Lapp
- Department of Neurology, Dresden University Hospital, Dresden, Germany
| | - Simone Thiele
- Friedrich-Baur-Institute at the Department of Neurology, LMU University Hospital, LMU Munich
| | - Stephan Wenninger
- Friedrich-Baur-Institute at the Department of Neurology, LMU University Hospital, LMU Munich
| | - Erik Jung
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Susanne Petri
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Markus Weiler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christoph Kleinschnitz
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, Essen, Germany
| | - Maggie C Walter
- Friedrich-Baur-Institute at the Department of Neurology, LMU University Hospital, LMU Munich
| | - René Günther
- Department of Neurology, Dresden University Hospital, Dresden, Germany
| | | | | | - Tim Hagenacker
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences (C-TNBS), University Hospital Essen, Essen, Germany
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Adams JL, Kangarloo T, Gong Y, Khachadourian V, Tracey B, Volfson D, Latzman RD, Cosman J, Edgerton J, Anderson D, Best A, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Jensen-Roberts S, Müller MLTM, Stephenson D, Dorsey ER. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months. NPJ Parkinsons Dis 2024; 10:112. [PMID: 38866793 PMCID: PMC11169239 DOI: 10.1038/s41531-024-00721-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/10/2024] [Indexed: 06/14/2024] Open
Abstract
Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.
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Affiliation(s)
- Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
| | | | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | | | | | | | | | | | | | | | | | - Melissa A Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peter Wilmot
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yvonne Pohlson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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Choo M, Park D, Cho M, Bae S, Kim J, Han DH. Exploring a multimodal approach for utilizing digital biomarkers for childhood mental health screening. Front Psychiatry 2024; 15:1348319. [PMID: 38666089 PMCID: PMC11043569 DOI: 10.3389/fpsyt.2024.1348319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Background Depression and anxiety are prevalent mental health concerns among children and adolescents. The application of conventional assessment methods, such as survey questionnaires to children, may lead to self-reporting issues. Digital biomarkers provide extensive data, reducing bias in mental health self-reporting, and significantly influence patient screening. Our primary objectives were to accurately assess children's mental health and to investigate the feasibility of using various digital biomarkers. Methods This study included a total of 54 boys and girls aged between 7 to 11 years. Each participant's mental state was assessed using the Depression, Anxiety, and Stress Scale. Subsequently, the subjects participated in digital biomarker collection tasks. Heart rate variability (HRV) data were collected using a camera sensor. Eye-tracking data were collected through tasks displaying emotion-face stimuli. Voice data were obtained by recording the participants' voices while they engaged in free speech and description tasks. Results Depressive symptoms were positively correlated with low frequency (LF, 0.04-0.15 Hz of HRV) in HRV and negatively associated with eye-tracking variables. Anxiety symptoms had a negative correlation with high frequency (HF, 0.15-0.40 Hz of HRV) in HRV and a positive association with LF/HF. Regarding stress, eye-tracking variables indicated a positive correlation, while pNN50, which represents the proportion of NN50 (the number of pairs of successive R-R intervals differing by more than 50 milliseconds) divided by the total number of NN (R-R) intervals, exhibited a negative association. Variables identified for childhood depression included LF and the total time spent looking at a sad face. Those variables recognized for anxiety were LF/HF, heart rate (HR), and pNN50. For childhood stress, HF, LF, and Jitter showed different correlation patterns between the two grade groups. Discussion We examined the potential of multimodal biomarkers in children, identifying features linked to childhood depression, particularly LF and the Sad.TF:time. Anxiety was most effectively explained by HRV features. To explore reasons for non-replication of previous studies, we categorized participants by elementary school grades into lower grades (1st, 2nd, 3rd) and upper grades (4th, 5th, 6th). Conclusion This study confirmed the potential use of multimodal digital biomarkers for children's mental health screening, serving as foundational research.
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Affiliation(s)
| | - Doeun Park
- HCI Lab, Yonsei University, Seoul, Republic of Korea
| | - Minseo Cho
- HCI Lab, Yonsei University, Seoul, Republic of Korea
| | - Sujin Bae
- Department of Psychiatry, College of Medicine, Chung-Ang University, Seoul, Republic of Korea
| | - Jinwoo Kim
- HCI Lab, Yonsei University, Seoul, Republic of Korea
| | - Doug Hyun Han
- Department of Psychiatry, College of Medicine, Chung-Ang University, Seoul, Republic of Korea
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Macias Alonso AK, Hirt J, Woelfle T, Janiaud P, Hemkens LG. Definitions of digital biomarkers: a systematic mapping of the biomedical literature. BMJ Health Care Inform 2024; 31:e100914. [PMID: 38589213 PMCID: PMC11015196 DOI: 10.1136/bmjhci-2023-100914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/06/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research. METHODS We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses. RESULTS We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions. CONCLUSIONS The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.
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Affiliation(s)
- Ana Karen Macias Alonso
- Department of Applied Natural Sciences, Technische Hochschule Lübeck, Lübeck, Germany
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Julian Hirt
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Health, Eastern Switzerland University of Applied Sciences, St.Gallen, Switzerland
| | - Tim Woelfle
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology and MS Center, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Perrine Janiaud
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lars G Hemkens
- Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
- Meta-Research Innovation Center Berlin (METRIC-B), Berlin Institute of Health, Berlin, Germany
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Chudzik A, Śledzianowski A, Przybyszewski AW. Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases. SENSORS (BASEL, SWITZERLAND) 2024; 24:1572. [PMID: 38475108 DOI: 10.3390/s24051572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.
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Affiliation(s)
- Artur Chudzik
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Albert Śledzianowski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
| | - Andrzej W Przybyszewski
- Polish-Japanese Academy of Information Technology, Faculty of Computer Science, 86 Koszykowa Street, 02-008 Warsaw, Poland
- UMass Chan Medical School, Department of Neurology, 65 Lake Avenue, Worcester, MA 01655, USA
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Lebleu J, Daniels K, Pauwels A, Dekimpe L, Mapinduzi J, Poilvache H, Bonnechère B. Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement. SENSORS (BASEL, SWITZERLAND) 2024; 24:1163. [PMID: 38400321 PMCID: PMC10892564 DOI: 10.3390/s24041163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/20/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.
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Affiliation(s)
- Julien Lebleu
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Kim Daniels
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
| | | | - Lucie Dekimpe
- moveUp, 1000 Brussels, Belgium; (J.L.); (A.P.); (L.D.)
| | - Jean Mapinduzi
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Filière de Kinésithérapie et Réadaptation, Département des Sciences Clinique, Institut National de la Santé Publique, 6807 Bujumbura, Burundi
| | - Hervé Poilvache
- Orthopedic Surgery Department, CHIREC, 1420 Braine-l’Alleud, Belgium
| | - Bruno Bonnechère
- Department of PXL—Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium;
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, 3590 Diepenbeek, Belgium
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Nunes AS, Pawlik M, Mishra RK, Waddell E, Coffey M, Tarolli CG, Schneider RB, Dorsey ER, Vaziri A, Adams JL. Digital assessment of speech in Huntington disease. Front Neurol 2024; 15:1310548. [PMID: 38322583 PMCID: PMC10844459 DOI: 10.3389/fneur.2024.1310548] [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: 10/09/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024] Open
Abstract
Background Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.
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Affiliation(s)
| | - Meghan Pawlik
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Emma Waddell
- Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Madeleine Coffey
- Donald and Barbara Zucker School of Medicine, Uniondale, NY, United States
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
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Virmani T, Kemp AS, Pillai L, Glover A, Spencer H, Larson-Prior L. Development and implementation of the frog-in-maze game to study upper limb movement in people with Parkinson's disease. Sci Rep 2023; 13:22784. [PMID: 38123606 PMCID: PMC10733393 DOI: 10.1038/s41598-023-49382-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Upper-limb bradykinesia occurs early in Parkinson's disease (PD) and bradykinesia is required for diagnosis. Our goal was to develop, implement and validate a game "walking" a frog through a maze using bimanual, alternating finger-tapping movements to provide a salient, objective, and remotely monitorable method of tracking disease progression and response to therapy in PD. Twenty-five people with PD and 16 people without PD participated. Responses on 5 different mazes were quantified and compared to spatiotemporal gait parameters and standard disease metrics in these participants. Intertap interval (ITI) on maze 2 & 3, which included turns, was strongly inversely related to stride-length and stride-velocity and directly related to motor UPDRS scores. Levodopa decreased ITI, except in maze 4. PD participants with freezing of gait had longer ITI on all mazes. The responses quantified on maze 2 & 3 were related to disease severity and gait stride-length, were levodopa responsive, and were worse in people with freezing of gait, suggesting that these mazes could be used to quantify motor dysfunction in PD. Programming our frog-in-maze game onto a remotely distributable platform could provide a tool to monitor disease progression and therapeutic response in people with PD, including during clinical trials.
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Affiliation(s)
- Tuhin Virmani
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA.
- Department of Neurology, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA.
| | - Aaron S Kemp
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
| | - Lakshmi Pillai
- Department of Neurology, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
| | - Aliyah Glover
- Department of Neurology, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
| | - Horace Spencer
- Department of Biostatistics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
| | - Linda Larson-Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
- Department of Neurology, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
- Department of Neurobiology, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR, 72205, USA
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Hazan J, Liu KY, Fox NC, Howard R. Online clinical tools to support the use of new plasma biomarker diagnostic technology in the assessment of Alzheimer's disease: a narrative review. Brain Commun 2023; 5:fcad322. [PMID: 38090277 PMCID: PMC10715781 DOI: 10.1093/braincomms/fcad322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/11/2023] [Accepted: 11/23/2023] [Indexed: 02/15/2024] Open
Abstract
Recent advances in new diagnostic technologies for Alzheimer's disease have improved the speed and precision of diagnosis. However, accessing the potential benefits of this technology poses challenges for clinicians, such as deciding whether it is clinically appropriate to order a diagnostic test, which specific test or tests to order and how to interpret test results and communicate these to the patient and their caregiver. Tools to support decision-making could provide additional structure and information to the clinical assessment process. These tools could be accessed online, and such 'e-tools' can provide an interactive interface to support patients and clinicians in the use of new diagnostic technologies for Alzheimer's disease. We performed a narrative review of the literature to synthesize information available on this research topic. Relevant studies that provide an understanding of how these online tools could be used to optimize the clinical utility of diagnostic technology were identified. Based on these, we discuss the ways in which e-tools have been used to assist in the diagnosis of Alzheimer's disease and propose recommendations for future research to aid further development.
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Affiliation(s)
- Jemma Hazan
- Division of Psychiatry, University College London, London W1T 7BN, UK
| | - Kathy Y Liu
- Division of Psychiatry, University College London, London W1T 7BN, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, W1T 7NF, UK
| | - Robert Howard
- Division of Psychiatry, University College London, London W1T 7BN, UK
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11
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Topaloğlu H, Coşkun AN. Smartphone measures motor and respiratory function in spinal muscular atrophy. Neuromuscul Disord 2023; 33:823. [PMID: 37919207 DOI: 10.1016/j.nmd.2023.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Investigating and following the motor function in children with SMA is challenging. In this issue of Neuromuscular Disorders, Perumal et al. (2023) describes how the smartphone sensor can be used to assess the respiratory and upper limb function in comparison to physical outcome measures currently in use. Eight out of nine items were closely in accord.
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Affiliation(s)
- Haluk Topaloğlu
- Department of Pediatrics, Yeditepe School of Medicine, Istanbul, Turkey.
| | - Ayşe Nur Coşkun
- Department of Child Neurology, Atatürk Sanatory Education and Research Hospital, Ankara, Turkey
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Perumal TM, Wolf D, Berchtold D, Pointeau G, Zhang YP, Cheng WY, Lipsmeier F, Sprengel J, Czech C, Chiriboga CA, Lindemann M. Digital measures of respiratory and upper limb function in spinal muscular atrophy: design, feasibility, reliability, and preliminary validity of a smartphone sensor-based assessment suite. Neuromuscul Disord 2023; 33:845-855. [PMID: 37722988 DOI: 10.1016/j.nmd.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/14/2023] [Accepted: 07/26/2023] [Indexed: 09/20/2023]
Abstract
Spinal muscular atrophy (SMA) is characterized by progressive muscle weakness and paralysis. Motor function is monitored in the clinical setting using assessments including the 32-item Motor Function Measure (MFM-32), but changes in disease severity between clinical visits may be missed. Digital health technologies may assist evaluation of disease severity by bridging gaps between clinical visits. We developed a smartphone sensor-based assessment suite, comprising nine tasks, to assess motor and muscle function in people with SMA. We used data from the risdiplam phase 2 JEWELFISH trial to assess the test-retest reliability and convergent validity of each task. In the first 6 weeks, 116 eligible participants completed assessments on a median of 6.3 days per week. Eight of the nine tasks demonstrated good or excellent test-retest reliability (intraclass correlation coefficients >0.75 and >0.9, respectively). Seven tasks showed a significant association (P < 0.05) with related clinical measures of motor function (individual items from the MFM-32 or Revised Upper Limb Module scales) and seven showed significant association (P < 0.05) with disease severity measured using the MFM-32 total score. This cross-sectional study supports the feasibility, reliability, and validity of using smartphone-based digital assessments to measure function in people living with SMA.
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Affiliation(s)
- Thanneer Malai Perumal
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland.
| | - Detlef Wolf
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Doris Berchtold
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Grégoire Pointeau
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Yan-Ping Zhang
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Wei-Yi Cheng
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Florian Lipsmeier
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Jörg Sprengel
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | - Christian Czech
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
| | | | - Michael Lindemann
- F. Hoffmann-La Roche Ltd, Roche Innovation Center Basel, Grenzacherstrasse 124, Basel 4070, Switzerland
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13
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Pyper E, McKeown S, Hartmann-Boyce J, Powell J. Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review. J Med Internet Res 2023; 25:e46992. [PMID: 37819698 PMCID: PMC10600647 DOI: 10.2196/46992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Digital health technologies (DHTs) play an ever-expanding role in health care management and delivery. Beyond their use as interventions, DHTs also serve as a vehicle for real-world data collection to characterize patients, their care journeys, and their responses to other clinical interventions. There is a need to comprehensively map the evidence-across all conditions and technology types-on DHT measurement of patient outcomes in the real world. OBJECTIVE We aimed to investigate the use of DHTs to measure real-world clinical outcomes using patient-generated data. METHODS We conducted this systematic scoping review in accordance with the Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a preregistered protocol informed a search strategy for the following databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies published between 2000 and 2022 wherein digital health data were collected, passively or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytical applications, were established where needed. Following screening and full-text review, data were extracted and analyzed using predefined fields, and findings were reported in accordance with established guidelines. RESULTS The search strategy identified 11,015 publications, with 7308 records after duplicates and reviews were removed. After screening and full-text review, 510 studies were included for extraction. These studies encompassed 169 different conditions in over 20 therapeutic areas and 44 countries. The DHTs used for mental health and addictions research (111/510, 21.8%) were the most prevalent. The most common type of DHT, mobile apps, was observed in approximately half of the studies (250/510, 49%). Most studies used only 1 DHT (346/510, 67.8%); however, the majority of technologies used were able to collect more than 1 type of data, with the most common being physiological data (189/510, 37.1%), clinical symptoms data (188/510, 36.9%), and behavioral data (171/510, 33.5%). Overall, there has been real growth in the depth and breadth of evidence, number of DHT types, and use of artificial intelligence and advanced analytics over time. CONCLUSIONS This scoping review offers a comprehensive view of the variety of types of technology, data, collection methods, analytical approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing digital biomarkers, there is a need for more rigorous research that goes beyond technology validation to demonstrate whether robust real-world data can be reliably captured from patients in their daily life and whether its capture improves patient outcomes. This study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policy makers, and the life sciences industry. TRIAL REGISTRATION Open Science Framework 5TMKY; https://osf.io/5tmky/.
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Affiliation(s)
- Evelyn Pyper
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Sarah McKeown
- Department for Continuing Education, University of Oxford, Oxford, United Kingdom
| | - Jamie Hartmann-Boyce
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Department of Health Promotion and Policy, University of Massachusetts Amherst, Amherst, MA, United States
| | - John Powell
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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14
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Holmes AA, Matarazzo M, Mondesire‐Crump I, Katz E, Mahajan R, Arroyo‐Gallego T. Exploring Asymmetric Fine Motor Impairment Trends in Early Parkinson's Disease via Keystroke Typing. Mov Disord Clin Pract 2023; 10:1530-1535. [PMID: 37868929 PMCID: PMC10585965 DOI: 10.1002/mdc3.13864] [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: 04/24/2023] [Revised: 07/10/2023] [Accepted: 08/06/2023] [Indexed: 10/24/2023] Open
Abstract
Background The nQiMechPD algorithm transforms natural typing data into a numerical index that characterizes motor impairment in people with Parkinson's Disease (PwPD). Objectives Use nQiMechPD to compare asymmetrical progression of PD-related impairment in dominant (D-PD) versus non-dominant side onset (ND-PD) de-novo patients. Methods Keystroke data were collected from 53 right-handed participants (15 D-PD, 13 ND-PD, 25 controls). We apply linear mixed effects modeling to evaluate participants' right, left, and both hands nQiMechPD relative change by group. Results The 6-month nQiMechPD trajectories of right (**P = 0.002) and both (*P = 0.043) hands showed a significant difference in nQiMechPD trends between D-PD and ND-PD participants. Left side trends were not significantly different between these two groups (P = 0.328). Conclusions Significant differences between D-PD and ND-PD groups were observed, likely driven by contrasting dominant hand trends. Our findings suggest disease onset side may influence motor impairment progression, medication response, and functional outcomes in PwPD.
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Affiliation(s)
| | - Michele Matarazzo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de MadridHospital Universitario HM Puerta del Sur, HM HospitalesMadridSpain
| | | | | | - Rahul Mahajan
- nQ MedicalCambridgeMassachusettsUSA
- Division of Neurocritical Care, Department of NeurologyBrigham & Women's HospitalBostonMassachusettsUSA
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15
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Idrisoglu A, Dallora AL, Anderberg P, Berglund JS. Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. J Med Internet Res 2023; 25:e46105. [PMID: 37467031 PMCID: PMC10398366 DOI: 10.2196/46105] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.
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Affiliation(s)
- Alper Idrisoglu
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Ana Luiza Dallora
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
| | - Peter Anderberg
- Department of Health, Blekinge Institute of Technology, Karslkrona, Sweden
- School of Health Sciences, University of Skövde, Skövde, Sweden
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16
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Galatzer-Levy IR, Onnela JP. Machine Learning and the Digital Measurement of Psychological Health. Annu Rev Clin Psychol 2023; 19:133-154. [PMID: 37159287 DOI: 10.1146/annurev-clinpsy-080921-073212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Since its inception, the discipline of psychology has utilized empirical epistemology and mathematical methodologies to infer psychological functioning from direct observation. As new challenges and technological opportunities emerge, scientists are once again challenged to define measurement paradigms for psychological health and illness that solve novel problems and capitalize on new technological opportunities. In this review, we discuss the theoretical foundations of and scientific advances in remote sensor technology and machine learning models as they are applied to quantify psychological functioning, draw clinical inferences, and chart new directions in treatment.
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Affiliation(s)
- Isaac R Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA;
- Current affiliation: Google LLC, Mountain View, California, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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17
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Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
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18
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Adams JL, Kangarloo T, Tracey B, O'Donnell P, Volfson D, Latzman RD, Zach N, Alexander R, Bergethon P, Cosman J, Anderson D, Best A, Severson J, Kostrzebski MA, Auinger P, Wilmot P, Pohlson Y, Waddell E, Jensen-Roberts S, Gong Y, Kilambi KP, Herrero TR, Ray Dorsey E. Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study. NPJ Parkinsons Dis 2023; 9:64. [PMID: 37069193 PMCID: PMC10108794 DOI: 10.1038/s41531-023-00497-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/27/2023] [Indexed: 04/19/2023] Open
Abstract
Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.
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Affiliation(s)
- Jamie L Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
| | | | | | - Patricio O'Donnell
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Sage Therapeutics, Seattle, WA, USA
| | | | | | - Neta Zach
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Robert Alexander
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Banner Health, Phoenix, AZ, USA
| | | | | | | | | | | | - Melissa A Kostrzebski
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Peter Wilmot
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yvonne Pohlson
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Emma Waddell
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Stella Jensen-Roberts
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Yishu Gong
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Krishna Praneeth Kilambi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, USA
| | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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19
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Lunven M, Hernandez Dominguez K, Youssov K, Hamet Bagnou J, Fliss R, Vandendriessche H, Bapst B, Morgado G, Remy P, Schubert R, Reilmann R, Busse M, Craufurd D, Massart R, Rosser A, Bachoud-Lévi AC. A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease. Brain Commun 2023; 5:fcad043. [PMID: 36938527 PMCID: PMC10018460 DOI: 10.1093/braincomms/fcad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 11/30/2022] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Cognitive deficits represent a hallmark of neurodegenerative diseases, but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries' language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 min. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability and sensitivity to detect decline in early-stage Huntington's disease in a prospective and international multilingual study (France, the UK and Germany). Fifty-one out of 85 participants with Huntington's disease and 40 of 52 healthy controls included at baseline were followed up for 1 year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman's correlation and proneness to retest effects and sensitivity to decline through linear mixed models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and tract-based spatial statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion-weighted imaging in a subgroup that underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington's disease in a 1-year follow-up period with satisfactory psychometric properties. Huntington's disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over 1 year. Given its good performance in longitudinal analyses of the Huntington's disease cohort, it should likely become a very useful tool for measuring cognition in Huntington's disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases.
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Affiliation(s)
- Marine Lunven
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Karen Hernandez Dominguez
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Katia Youssov
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Jennifer Hamet Bagnou
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Rafika Fliss
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Henri Vandendriessche
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Blanche Bapst
- Department of Neuroradiology, AP-HP, Henri Mondor University Hospital, 94010 Créteil, France
- Faculty of Medicine, Université Paris Est Créteil, F-94010 Créteil, France
| | - Graça Morgado
- Inserm, Centre d’Investigation Clinique 1430, APHP, Hôpital Henri Mondor, 94010 Créteil, France
| | - Philippe Remy
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Robin Schubert
- George Huntington Institute, Technology-Park, 48149 Muenster, Germany
- Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
| | - Ralf Reilmann
- George Huntington Institute, Technology-Park, 48149 Muenster, Germany
- Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
- Department of Clinical Radiology, University of Muenster, 48149 Muenster, Germany
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff CF14 4EP, UK
- Wales Brain Research And Intracranial Neurotherapeutics (BRAIN) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
| | - David Craufurd
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
| | - Renaud Massart
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Anne Rosser
- Wales Brain Research And Intracranial Neurotherapeutics (BRAIN) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
- Cardiff School of Medicine, Neuroscience and Mental Health Institute, Cardiff CF24 4HQ, UK
- School of Biosciences, Cardiff University Brain Repair Group, Cardiff CF10 3AX, UK
| | - Anne-Catherine Bachoud-Lévi
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
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Schäfer S, Mallick E, Schwed L, König A, Zhao J, Linz N, Bodin TH, Skoog J, Possemis N, ter Huurne D, Zettergren A, Kern S, Sacuiu S, Ramakers I, Skoog I, Tröger J. Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts. J Alzheimers Dis 2023; 91:1165-1171. [PMID: 36565116 PMCID: PMC9912722 DOI: 10.3233/jad-220762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. OBJECTIVE Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. METHODS Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. RESULTS The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohorts. CONCLUSION The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.
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Affiliation(s)
- Simona Schäfer
- ki:elements, Saarbrücken, Germany,Correspondence to: Simona Schäfer, ki elements GmbH, Am Holzbrunnen 1a, 66121 Saarbrücken, Germany. Tel.: +49681 372009200; E-mail:
| | | | | | - Alexandra König
- ki:elements, Saarbrücken, Germany,Institut National de Recherche en Informatique et en Automatique (INRIA), Stars Team, Sophia Antipolis, Valbonne, France
| | | | | | - Timothy Hadarsson Bodin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Johan Skoog
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nina Possemis
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Daphne ter Huurne
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Anna Zettergren
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Silke Kern
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Simona Sacuiu
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Inez Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ingmar Skoog
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Bonnechère B, Timmermans A, Michiels S. Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020875. [PMID: 36679672 PMCID: PMC9866361 DOI: 10.3390/s23020875] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 01/05/2023] [Indexed: 06/01/2023]
Abstract
The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
| | - Annick Timmermans
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
| | - Sarah Michiels
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium
- Department of Otorhinolaryngology, Antwerp University Hospital, 2650 Edegem, Belgium
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22
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Van Ooteghem K, Godkin FE, Thai V, Beyer KB, Cornish BF, Weber KS, Bernstein H, Kheiri SO, Swartz RH, Tan B, McIlroy WE, Roberts AC. User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease. Digit Health 2023; 9:20552076231179031. [PMID: 37312943 PMCID: PMC10259132 DOI: 10.1177/20552076231179031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/12/2023] [Indexed: 06/15/2023] Open
Abstract
Objective There has been tremendous growth in wearable technologies for health monitoring but limited efforts to optimize methods for sharing wearables-derived information with older adults and clinical cohorts. This study aimed to co-develop, design and evaluate a personalized approach for information-sharing regarding daily health-related behaviors captured with wearables. Methods A participatory research approach was adopted with: (a) iterative stakeholder, and evidence-led development of feedback reporting; and (b) evaluation in a sample of older adults (n = 15) and persons living with neurodegenerative disease (NDD) (n = 25). Stakeholders included persons with lived experience, healthcare providers, health charity representatives and individuals involved in aging/NDD research. Feedback report information was custom-derived from two limb-mounted inertial measurement units and a mobile electrocardiography device worn by participants for 7-10 days. Mixed methods were used to evaluate reporting 2 weeks following delivery. Data were summarized using descriptive statistics for the group and stratified by cohort and cognitive status. Results Participants (n = 40) were 60% female (median 72 (60-87) years). A total of 82.5% found the report easy to read or understand, 80% reported the right amount of information was shared, 90% found the information helpful, 92% shared the information with a family member or friend and 57.5% made a behavior change. Differences emerged in sub-group comparisons. A range of participant profiles existed in terms of interest, uptake and utility. Conclusions The reporting approach was generally well-received with perceived value that translated into enhanced self-awareness and self-management of daily health-related behaviors. Future work should examine potential for scale, and the capacity for wearables-derived feedback to influence longer-term behavior change.
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Affiliation(s)
- Karen Van Ooteghem
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - F Elizabeth Godkin
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Vanessa Thai
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kit B Beyer
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Benjamin F Cornish
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Kyle S Weber
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Hannah Bernstein
- Department of Nanotechnology Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Soha O Kheiri
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Richard H Swartz
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - William E McIlroy
- Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Angela C Roberts
- School of Communication Sciences and Disorders, Western University, London, ON, Canada
- Department of Computer Science, Western University, London, ON, Canada
- Canadian Centre for Activity and Aging, Western University, London, ON, Canada
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23
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Bonnechère B. Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine? Genes (Basel) 2022; 14:63. [PMID: 36672802 PMCID: PMC9858788 DOI: 10.3390/genes14010063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Over recent years, significant improvements have been made in the understanding of (epi)genetics and neuropathophysiological mechanisms driving the different forms of multiple sclerosis (MS). For example, the role and importance of the bidirectional communications between the brain and the gut-also referred to as the gut-brain axis-in the pathogenesis of MS is receiving increasing interest in recent years and is probably one of the most promising areas of research for the management of people with MS. However, despite these important advances, it must be noted that these data are not-yet-used in rehabilitation. Neurorehabilitation is a cornerstone of MS patient management, and there are many techniques available to clinicians and patients, including technology-supported rehabilitation. In this paper, we will discuss how new findings on the gut microbiome could help us to better understand how rehabilitation can improve motor and cognitive functions. We will also see how the data gathered during the rehabilitation can help to get a better diagnosis of the patients. Finally, we will discuss how these new techniques can better guide rehabilitation to lead to precision rehabilitation and ultimately increase the quality of patient care.
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Affiliation(s)
- Bruno Bonnechère
- REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, 3590 Diepenbeek, Belgium;
- Technology-Supported and Data-Driven Rehabilitation, Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium
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24
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Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol. PLoS One 2022; 17:e0269615. [PMID: 36201476 PMCID: PMC9536536 DOI: 10.1371/journal.pone.0269615] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/17/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. METHODS/DESIGN The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. DISCUSSION The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. TRIAL REGISTRATION ISRCTN12051706.
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25
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Tröger J, Baykara E, Zhao J, ter Huurne D, Possemis N, Mallick E, Schäfer S, Schwed L, Mina M, Linz N, Ramakers I, Ritchie C. Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment: Verification and Validation following DiME V3 Framework. Digit Biomark 2022; 6:107-116. [PMID: 36466952 PMCID: PMC9710455 DOI: 10.1159/000526471] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/06/2022] [Indexed: 08/01/2023] Open
Abstract
INTRODUCTION Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation. METHODS Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. RESULTS Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. CONCLUSION Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.
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Affiliation(s)
| | | | | | - Daphne ter Huurne
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Nina Possemis
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | | | | | | | | | - Inez Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
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26
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Schütz N, Knobel SEJ, Botros A, Single M, Pais B, Santschi V, Gatica-Perez D, Buluschek P, Urwyler P, Gerber SM, Müri RM, Mosimann UP, Saner H, Nef T. A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. NPJ Digit Med 2022; 5:116. [PMID: 35974156 PMCID: PMC9381599 DOI: 10.1038/s41746-022-00657-y] [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: 01/03/2022] [Accepted: 07/13/2022] [Indexed: 11/09/2022] Open
Abstract
Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.
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Affiliation(s)
- Narayan Schütz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
| | - Samuel E J Knobel
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Angela Botros
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Michael Single
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Bruno Pais
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Valérie Santschi
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Daniel Gatica-Perez
- Idiap Research Institute, Martigny, Switzerland.,School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Prabitha Urwyler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - René M Müri
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
| | - Urs P Mosimann
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Hugo Saner
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Tobias Nef
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
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Holmes AA, Tripathi S, Katz E, Mondesire-Crump I, Mahajan R, Ritter A, Arroyo-Gallego T, Giancardo L. A novel framework to estimate cognitive impairment via finger interaction with digital devices. Brain Commun 2022; 4:fcac194. [PMID: 35950091 PMCID: PMC9356723 DOI: 10.1093/braincomms/fcac194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/11/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Measuring cognitive function is essential for characterizing brain health and tracking cognitive decline in Alzheimer’s Disease and other neurodegenerative conditions. Current tools to accurately evaluate cognitive impairment typically rely on a battery of questionnaires administered during clinical visits which is essential for the acquisition of repeated measurements in longitudinal studies. Previous studies have shown that the remote data collection of passively monitored daily interaction with personal digital devices can measure motor signs in the early stages of synucleinopathies, as well as facilitate longitudinal patient assessment in the real-world scenario with high patient compliance. This was achieved by the automatic discovery of patterns in the time series of keystroke dynamics, i.e. the time required to press and release keys, by machine learning algorithms. In this work, our hypothesis is that the typing patterns generated from user-device interaction may reflect relevant features of the effects of cognitive impairment caused by neurodegeneration. We use machine learning algorithms to estimate cognitive performance through the analysis of keystroke dynamic patterns that were extracted from mechanical and touchscreen keyboard use in a dataset of cognitively normal (n = 39, 51% male) and cognitively impaired subjects (n = 38, 60% male). These algorithms are trained and evaluated using a novel framework that integrates items from multiple neuropsychological and clinical scales into cognitive subdomains to generate a more holistic representation of multifaceted clinical signs. In our results, we see that these models based on typing input achieve moderate correlations with verbal memory, non-verbal memory and executive function subdomains [Spearman’s ρ between 0.54 (P < 0.001) and 0.42 (P < 0.001)] and a weak correlation with language/verbal skills [Spearman’s ρ 0.30 (P < 0.05)]. In addition, we observe a moderate correlation between our typing-based approach and the Total Montreal Cognitive Assessment score [Spearman’s ρ 0.48 (P < 0.001)]. Finally, we show that these machine learning models can perform better by using our subdomain framework that integrates the information from multiple neuropsychological scales as opposed to using the individual items that make up these scales. Our results support our hypothesis that typing patterns are able to reflect the effects of neurodegeneration in mild cognitive impairment and Alzheimer’s disease and that this new subdomain framework both helps the development of machine learning models and improves their interpretability.
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Affiliation(s)
| | - Shikha Tripathi
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
| | | | | | - Rahul Mahajan
- nQ Medical , Cambridge, MA 02142 , USA
- Division of Neurocritical Care, Department of Neurology, Brigham & Women’s Hospital , Boston, MA 02115 , USA
| | - Aaron Ritter
- Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland Clinic , Las Vegas, NV 89106 , USA
| | | | - Luca Giancardo
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, TX 77030 , USA
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Mori H, Wiklund SJ, Zhang JY. Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball. Digit Biomark 2022; 6:36-46. [PMID: 35949224 PMCID: PMC9297703 DOI: 10.1159/000525255] [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: 03/09/2022] [Accepted: 05/23/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Digital biomarkers have significant potential to transform drug development, but only a few have contributed meaningfully to bring new treatments to market. There are uncertainties in how they will generate quantifiable benefits in clinical trial performance and ultimately to the chances of phase 3 success. Here we have proposed a statistical framework and ran a proof-of-concept model with hypothetical digital biomarkers and visualized them in a familiar manner to study power calculation. Methods A Monte Carlo simulation for Parkinson's disease (PD) was performed using the Captario SUM® platform and illustrative study technology impact calculations were generated. We took inspiration from the EMA-qualified wearable-derived digital endpoint stride velocity 95<sup>th</sup> centile (SV95C) for Duchenne muscular dystrophy, and we imagined a similar measurement for PD would be available in the future. DaTscan enrichment and “SV95C-like” endpoint biomarkers were assumed on a hypothetical disease-modifying drug pivotal trial aiming for an 80% probability of achieving a study p value of less than 0.05. Results Four scenarios with different combinations of technologies were illustrated. The model illustrated a way to quantify the magnitude of the contributions that enrichment and endpoint technologies could make to drug development studies. Discussion/Conclusion Quantitative models could be valuable not only for the study sponsors but also as an interactive and collaborative engagement tool for technology players and multi-stakeholder consortia. Establishing values of digital biomarkers could also facilitate business cases and financial investments.
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Lipsmeier F, Simillion C, Bamdadian A, Tortelli R, Byrne LM, Zhang YP, Wolf D, Smith AV, Czech C, Gossens C, Weydt P, Schobel SA, Rodrigues FB, Wild EJ, Lindemann M. A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study. J Med Internet Res 2022; 24:e32997. [PMID: 35763342 PMCID: PMC9277525 DOI: 10.2196/32997] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring. Objective The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. Methods This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic. Results Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90). Conclusions These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Cedric Simillion
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Atieh Bamdadian
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Rosanna Tortelli
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Lauren M Byrne
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yan-Ping Zhang
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Anne V Smith
- Ionis Pharmaceuticals Inc, Carlsbad, CA, United States
| | - Christian Czech
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.,Rare Disease Research Unit, Pfizer, Nice, France
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Patrick Weydt
- Department of Neurology, University of Ulm Medical Center, Ulm, Germany.,Department of Neurodegenerative Disease and Gerontopsychiatry/Neurology, University of Bonn Medical Center, Bonn, Germany
| | | | - Filipe B Rodrigues
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Edward J Wild
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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Advancing Digital Medicine with Wearables in the Wild. SENSORS 2022; 22:s22124576. [PMID: 35746358 PMCID: PMC9227612 DOI: 10.3390/s22124576] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023]
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Geronimo A. Remote patient monitoring in neuromuscular disease. Muscle Nerve 2022; 66:233-235. [PMID: 35674416 DOI: 10.1002/mus.27658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 05/31/2022] [Accepted: 06/03/2022] [Indexed: 11/11/2022]
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Harms RL, Ferrari A, Meier IB, Martinkova J, Santus E, Marino N, Cirillo D, Mellino S, Catuara Solarz S, Tarnanas I, Szoeke C, Hort J, Valencia A, Ferretti MT, Seixas A, Santuccione Chadha A. Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach. EPMA J 2022; 13:299-313. [PMID: 35719134 PMCID: PMC9203627 DOI: 10.1007/s13167-022-00284-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
Abstract
Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00284-3.
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Affiliation(s)
| | | | | | - Julie Martinkova
- Women’s Brain Project, Guntershausen, Switzerland
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Enrico Santus
- Women’s Brain Project, Guntershausen, Switzerland
- Bayer, NJ USA
| | - Nicola Marino
- Women’s Brain Project, Guntershausen, Switzerland
- Dipartimento Di Scienze Mediche E Chirurgiche, Università Degli Studi Di Foggia, Foggia, Italy
| | - Davide Cirillo
- Women’s Brain Project, Guntershausen, Switzerland
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain
| | | | | | - Ioannis Tarnanas
- Altoida Inc., Houston, TX USA
- Global Brain Health Institute, Dublin, Ireland
| | - Cassandra Szoeke
- Women’s Brain Project, Guntershausen, Switzerland
- Centre for Medical Research, Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Australia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
- International Clinical Research Center, St Anne’s University Hospital Brno, Brno, Czech Republic
| | - Alfonso Valencia
- Barcelona Supercomputing Center, Plaça Eusebi Güell, 1-3, 08034 Barcelona, Spain
- ICREA - Institució Catalana de Recerca I Estudis Avançats, Pg. Lluís Companys 23, 08010 Barcelona, Spain
| | | | - Azizi Seixas
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL 33136 USA
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Rapid development of an integrated remote programming platform for neuromodulation systems through the biodesign process. Sci Rep 2022; 12:2269. [PMID: 35145143 PMCID: PMC8831590 DOI: 10.1038/s41598-022-06098-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
Treating chronic symptoms for pain and movement disorders with neuromodulation therapies involves fine-tuning of programming parameters over several visits to achieve and maintain symptom relief. This, together with challenges in access to trained specialists, has led to a growing need for an integrated wireless remote care platform for neuromodulation devices. In March of 2021, we launched the first neuromodulation device with an integrated remote programming platform. Here, we summarize the biodesign steps taken to identify the unmet patient need, invent, implement, and test the new technology, and finally gain market approval for the remote care platform. Specifically, we illustrate how agile development aligned with the evolving regulatory requirements can enable patient-centric digital health technology in neuromodulation, such as the remote care platform. The three steps of the biodesign process applied for remote care platform development are: (1) Identify, (2) Invent, and (3) Implement. First, we identified the unmet patient needs through market research and voice-of-customer (VOC) process. Next, during the concept generation phase of the invention step, we integrated the results from the VOC into defining requirements for prototype development. Subsequently, in the concept screening phase, ten subjects with PD participated in a clinical pilot study aimed at characterizing the safety of the remote care prototype. Lastly, during the implementation step, lessons learned from the pilot experience were integrated into final product development as new features. Following final product development, we completed usability testing to validate the full remote care system and collected preliminary data from the limited market release experience. The VOC data, during prototype development, helped us identify thresholds for video quality and needs priorities for clinicians and patients. During the pilot study, one subject reported anticipated remote-care-related adverse events that were resolved without sequelae. For usability analysis following final product development, the failure rates for task completion for both user groups were about 1%. Lastly, during the initial 4 weeks of the limited market release experience, a total of 858 remote care sessions were conducted with a 93% success rate. Overall, we developed a remote care platform by adopting a user-centric approach. Although the system intended to address pre-COVID19 challenges associated with disease management, the unforeseen overlap of the study with the pandemic elevated the importance of such a system and an innovative development process enabled us to advance a patient-centric platform to gain regulatory approval and successfully launch the remote care platform to market.
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Page A, Yung N, Auinger P, Venuto C, Glidden A, Macklin E, Omberg L, Schwarzschild MA, Dorsey ER. A Smartphone Application as an Exploratory Endpoint in a Phase 3 Parkinson's Disease Clinical Trial: A Pilot Study. Digit Biomark 2022; 6:1-8. [PMID: 35224425 PMCID: PMC8832247 DOI: 10.1159/000521232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Smartphones can generate objective measures of Parkinson's disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. OBJECTIVE This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. METHODS A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). RESULTS Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (r = -0.16, left hand; r = -0.04, right hand) and total (r = -0.14) MDS-UPDRS. Gait speed correlated better with the same measures (r = -0.25, part III motor; r = -0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. CONCLUSIONS Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study's overall findings, which found no benefit of the active drug.
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Affiliation(s)
- Alex Page
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Norman Yung
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Peggy Auinger
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Charles Venuto
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Alistair Glidden
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Eric Macklin
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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Krokidis MG, Dimitrakopoulos GN, Vrahatis AG, Tzouvelekis C, Drakoulis D, Papavassileiou F, Exarchos TP, Vlamos P. A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes. SENSORS (BASEL, SWITZERLAND) 2022; 22:409. [PMID: 35062370 PMCID: PMC8777583 DOI: 10.3390/s22020409] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 02/04/2023]
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.
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Affiliation(s)
- Marios G. Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Georgios N. Dimitrakopoulos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Aristidis G. Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Christos Tzouvelekis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | | | | | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
| | - Panayiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece; (M.G.K.); (A.G.V.); (C.T.); (T.P.E.)
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Drew CJG, Busse M. Considerations for clinical trial design and conduct in the evaluation of novel advanced therapeutics in neurodegenerative disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2022; 166:235-279. [PMID: 36424094 DOI: 10.1016/bs.irn.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The recent advances in the development of potentially disease modifying cell and gene therapies for neurodegenerative disease has resulted in the production of a number of promising novel therapies which are now moving forward to clinical evaluation. The robust evaluation of these therapies pose a significant number of challenges when compared to more traditional evaluations of pharmacotherapy, which is the current mainstay of neurodegenerative disease symptom management. Indeed, there is an inherent complexity in the design and conduct of these trials at multiple levels. Here we discuss specific aspects requiring consideration in the context of investigating novel cell and gene therapies for neurodegenerative disease. This extends to overarching trial designs that could be employed and the factors that underpin design choices such outcome assessments, participant selection and methods for delivery of cell and gene therapies. We explore methods of data collection that may improve efficiency in trials of cell and gene therapy to maximize data sharing and collaboration. Lastly, we explore some of the additional context beyond efficacy evaluations that should be considered to ensure implementation across relevant healthcare settings.
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Affiliation(s)
- Cheney J G Drew
- Centre For Trials Research, Cardiff University, Cardiff, United Kingdom; Brain Repair and Intracranial Neurotherapeutics Unit (BRAIN), College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
| | - Monica Busse
- Centre For Trials Research, Cardiff University, Cardiff, United Kingdom; Brain Repair and Intracranial Neurotherapeutics Unit (BRAIN), College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom
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Cohen S, Cummings J, Knox S, Potashman M, Harrison J. Clinical Trial Endpoints and Their Clinical Meaningfulness in Early Stages of Alzheimer's Disease. J Prev Alzheimers Dis 2022; 9:507-522. [PMID: 35841252 PMCID: PMC9843702 DOI: 10.14283/jpad.2022.41] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
As the focus of Alzheimer's disease (AD) therapeutic development shifts to the early stages of the disease, the clinical endpoints used in drug trials, and how these might translate into clinical practice, are of increasing importance. The clinical meaningfulness of trial outcome measures is often unclear, with a lack of conclusive evidence as to how these measures correlate to changes in disease progression and treatment response. Clarifying this would benefit all, including patients, care partners, primary care providers, regulators, and payers, and would enhance our understanding of the relationship between clinical trial endpoints and assessments used in everyday practice. At present, there is a wide range of assessment tools used in clinical trials for AD and substantial variability in measures selected as endpoints across these trials. The aim of this review is to summarize the most commonly used assessment tools for early stages of AD, describe their use in clinical trials and clinical practice, and discuss what might constitute clinically meaningful change in these measures in relation to disease progression and treatment response.
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Affiliation(s)
- S. Cohen
- Toronto Memory Program, Toronto, ON, Canada
| | - J. Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada, Las Vegas (UNLV), NV, USA
| | - S. Knox
- Biogen International GmbH, Baar, Switzerland
| | | | - J. Harrison
- Metis Cognition Ltd, Wiltshire, UK,Alzheimer Center, AU Medical Center, Amsterdam, the Netherlands,Institute of Psychiatry, Psychology & Neuroscience, King’s College London, UK
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Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Front Psychiatry 2022; 13:873392. [PMID: 35757212 PMCID: PMC9225201 DOI: 10.3389/fpsyt.2022.873392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.
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Mennella C, Alloisio S, Novellino A, Viti F. Characteristics and Applications of Technology-Aided Hand Functional Assessment: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 22:199. [PMID: 35009742 PMCID: PMC8749695 DOI: 10.3390/s22010199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023]
Abstract
Technology-aided hand functional assessment has received considerable attention in recent years. Its applications are required to obtain objective, reliable, and sensitive methods for clinical decision making. This systematic review aims to investigate and discuss characteristics of technology-aided hand functional assessment and their applications, in terms of the adopted sensing technology, evaluation methods and purposes. Based on the shortcomings of current applications, and opportunities offered by emerging systems, this review aims to support the design and the translation to clinical practice of technology-aided hand functional assessment. To this end, a systematic literature search was led, according to recommended PRISMA guidelines, in PubMed and IEEE Xplore databases. The search yielded 208 records, resulting into 23 articles included in the study. Glove-based systems, instrumented objects and body-networked sensor systems appeared from the search, together with vision-based motion capture systems, end-effector, and exoskeleton systems. Inertial measurement unit (IMU) and force sensing resistor (FSR) resulted the sensing technologies most used for kinematic and kinetic analysis. A lack of standardization in system metrics and assessment methods emerged. Future studies that pertinently discuss the pathophysiological content and clinimetrics properties of new systems are required for leading technologies to clinical acceptance.
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Affiliation(s)
- Ciro Mennella
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
| | - Susanna Alloisio
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
- ETT Spa, Via Sestri 37, 16154 Genova, Italy;
| | | | - Federica Viti
- Institute of Biophysics, National Research Council, Via De Marini 6, 16149 Genova, Italy; (S.A.); (F.V.)
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Motahari-Nezhad H, Fgaier M, Mahdi Abid M, Péntek M, Gulácsi L, Zrubka Z. Scoping review of systematic reviews of digital biomarker-based studies (Preprint). JMIR Mhealth Uhealth 2021; 10:e35722. [DOI: 10.2196/35722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/20/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
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Dechant MJ, Frommel J, Mandryk RL. The Development of Explicit and Implicit Game-Based Digital Behavioral Markers for the Assessment of Social Anxiety. Front Psychol 2021; 12:760850. [PMID: 34975652 PMCID: PMC8715901 DOI: 10.3389/fpsyg.2021.760850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Social relationships are essential for humans; neglecting our social needs can reduce wellbeing or even lead to the development of more severe issues such as depression or substance dependency. Although essential, some individuals face major challenges in forming and maintaining social relationships due to the experience of social anxiety. The burden of social anxiety can be reduced through accessible assessment that leads to treatment. However, socially anxious individuals who seek help face many barriers stemming from geography, fear, or disparities in access to systems of care. But recent research suggested digital behavioral markers as a way to deliver cheap and easily accessible digital assessment for social anxiety: As earlier work shows, players with social anxiety show similar behaviors in virtual worlds as in the physical world, including tending to walk farther around other avatars and standing farther away from other avatars. The characteristics of the movement behavior in-game can be harnessed for the development of digital behavioral markers for the assessment of social anxiety. In this paper, we investigate whether implicit as well as explicit digital behavioral markers, proposed by prior work, for social anxiety can be used for predicting the level of social anxiety. We show that both, explicit and implicit digital behavioral markers can be harnessed for the assessment. Our findings provide further insights about how game-based digital behavioral markers can be used for the assessment of social anxiety.
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Affiliation(s)
- Martin Johannes Dechant
- Human-Computer-Interaction Laboratory, Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Dillenseger A, Weidemann ML, Trentzsch K, Inojosa H, Haase R, Schriefer D, Voigt I, Scholz M, Akgün K, Ziemssen T. Digital Biomarkers in Multiple Sclerosis. Brain Sci 2021; 11:brainsci11111519. [PMID: 34827518 PMCID: PMC8615428 DOI: 10.3390/brainsci11111519] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 12/19/2022] Open
Abstract
For incurable diseases, such as multiple sclerosis (MS), the prevention of progression and the preservation of quality of life play a crucial role over the entire therapy period. In MS, patients tend to become ill at a younger age and are so variable in terms of their disease course that there is no standard therapy. Therefore, it is necessary to enable a therapy that is as personalized as possible and to respond promptly to any changes, whether with noticeable symptoms or symptomless. Here, measurable parameters of biological processes can be used, which provide good information with regard to prognostic and diagnostic aspects, disease activity and response to therapy, so-called biomarkers Increasing digitalization and the availability of easy-to-use devices and technology also enable healthcare professionals to use a new class of digital biomarkers-digital health technologies-to explain, influence and/or predict health-related outcomes. The technology and devices from which these digital biomarkers stem are quite broad, and range from wearables that collect patients' activity during digitalized functional tests (e.g., the Multiple Sclerosis Performance Test, dual-tasking performance and speech) to digitalized diagnostic procedures (e.g., optical coherence tomography) and software-supported magnetic resonance imaging evaluation. These technologies offer a timesaving way to collect valuable data on a regular basis over a long period of time, not only once or twice a year during patients' routine visit at the clinic. Therefore, they lead to real-life data acquisition, closer patient monitoring and thus a patient dataset useful for precision medicine. Despite the great benefit of such increasing digitalization, for now, the path to implementing digital biomarkers is widely unknown or inconsistent. Challenges around validation, infrastructure, evidence generation, consistent data collection and analysis still persist. In this narrative review, we explore existing and future opportunities to capture clinical digital biomarkers in the care of people with MS, which may lead to a digital twin of the patient. To do this, we searched published papers for existing opportunities to capture clinical digital biomarkers for different functional systems in the context of MS, and also gathered perspectives on digital biomarkers under development or already existing as a research approach.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Tjalf Ziemssen
- Correspondence: ; Tel.: +49-351-458-5934; Fax: +49-351-458-5717
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Gielis K, Vanden Abeele ME, De Croon R, Dierick P, Ferreira-Brito F, Van Assche L, Verbert K, Tournoy J, Vanden Abeele V. Dissecting Digital Card Games to Yield Digital Biomarkers for the Assessment of Mild Cognitive Impairment: Methodological Approach and Exploratory Study. JMIR Serious Games 2021; 9:e18359. [PMID: 34734825 PMCID: PMC8603181 DOI: 10.2196/18359] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 01/28/2021] [Accepted: 08/11/2021] [Indexed: 01/19/2023] Open
Abstract
Background Mild cognitive impairment (MCI), the intermediate cognitive status between normal cognitive decline and pathological decline, is an important clinical construct for signaling possible prodromes of dementia. However, this condition is underdiagnosed. To assist monitoring and screening, digital biomarkers derived from commercial off-the-shelf video games may be of interest. These games maintain player engagement over a longer period of time and support longitudinal measurements of cognitive performance. Objective This paper aims to explore how the player actions of Klondike Solitaire relate to cognitive functions and to what extent the digital biomarkers derived from these player actions are indicative of MCI. Methods First, 11 experts in the domain of cognitive impairments were asked to correlate 21 player actions to 11 cognitive functions. Expert agreement was verified through intraclass correlation, based on a 2-way, fully crossed design with type consistency. On the basis of these player actions, 23 potential digital biomarkers of performance for Klondike Solitaire were defined. Next, 23 healthy participants and 23 participants living with MCI were asked to play 3 rounds of Klondike Solitaire, which took 17 minutes on average to complete. A generalized linear mixed model analysis was conducted to explore the differences in digital biomarkers between the healthy participants and those living with MCI, while controlling for age, tablet experience, and Klondike Solitaire experience. Results All intraclass correlations for player actions and cognitive functions scored higher than 0.75, indicating good to excellent reliability. Furthermore, all player actions had, according to the experts, at least one cognitive function that was on average moderately to strongly correlated to a cognitive function. Of the 23 potential digital biomarkers, 12 (52%) were revealed by the generalized linear mixed model analysis to have sizeable effects and significance levels. The analysis indicates sensitivity of the derived digital biomarkers to MCI. Conclusions Commercial off-the-shelf games such as digital card games show potential as a complementary tool for screening and monitoring cognition. Trial Registration ClinicalTrials.gov NCT02971124; https://clinicaltrials.gov/ct2/show/NCT02971124
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Affiliation(s)
- Karsten Gielis
- e-Media Research Lab, Katholieke Universiteit Leuven, Leuven, Belgium
| | | | - Robin De Croon
- Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Paul Dierick
- Department of Gerontopsychiatry, University Psychiatric Center, Duffel, Belgium
| | - Filipa Ferreira-Brito
- Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Lies Van Assche
- Section of Geriatric Psychiatry, University Hospital Leuven, Katholieke Universiteit Leuven, Leuven, Belgium.,Department of Psychiatry, University Hospital Leuven, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Katrien Verbert
- Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Jos Tournoy
- Department of Geriatric Medicine, University Hospital Leuven, Leuven, Belgium.,Department of Public Health and Primary Care, Gerontology and Geriatrics, Katholieke Universiteit Leuven, Leuven, Belgium
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Ntracha A, Iakovakis D, Hadjidimitriou S, Charisis VS, Tsolaki M, Hadjileontiadis LJ. Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing. Front Digit Health 2021; 2:567158. [PMID: 34713039 PMCID: PMC8521910 DOI: 10.3389/fdgth.2020.567158] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the condition, when existing diagnostic methods are employed in the clinical setting. From an alternative perspective, smartphone interaction behavioral data, unobtrusively acquired in a non-clinical setting, can assist the screening and monitoring of MCI and its symptoms' progression. In this vein, the diagnostic ability of digital biomarkers, drawn from Fine Motor Impairment (FMI)- and Spontaneous Written Speech (SWS)-related data analysis, are examined here. In particular, keystroke dynamics derived from touchscreen typing activities, using Convolutional Neural Networks, along with linguistic features of SWS through Natural Language Processing (NLP), were used to distinguish amongst MCI patients and healthy controls (HC). Analytically, three indices of FMI (rigidity, bradykinesia and alternate finger tapping) and nine NLP features, related with lexical richness, grammatical, syntactical complexity, and word deficits, formed the feature space. The proposed approach was tested on two demographically matched groups of 11 MCI patients and 12 HC, having undergone the same neuropsychological tests, producing 4,930 typing sessions and 78 short texts, within 6 months, for analysis. A cascaded-classifier scheme was realized under three different feature combinations and validated via a Leave-One-Subject-Out cross-validation scheme. The acquired results have shown: (a) keystroke features with a k-NN classifier achieved an Area Under Curve (AUC) of 0.78 [95% confidence interval (CI):0.68-0.88; specificity/sensitivity (SP/SE): 0.64/0.92], (b) NLP features with a Logistic regression classifier achieved an AUC of 0.76 (95% CI: 0.65-0.85; SP/SE: 0.80/0.71), and (c) an ensemble model with the fusion of keystroke and NLP features resulted in AUC of 0.75 (95% CI:0.63-0.86; SP/SE 0.90/0.60). The current findings indicate the potentiality of new digital biomarkers to capture early stages of cognitive decline, providing a highly specific remote screening tool in-the-wild.
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Affiliation(s)
- Anastasia Ntracha
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stelios Hadjidimitriou
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios S Charisis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Magda Tsolaki
- Third Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leontios J Hadjileontiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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45
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Polhemus A, Delgado-Ortiz L, Brittain G, Chynkiamis N, Salis F, Gaßner H, Gross M, Kirk C, Rossanigo R, Taraldsen K, Balta D, Breuls S, Buttery S, Cardenas G, Endress C, Gugenhan J, Keogh A, Kluge F, Koch S, Micó-Amigo ME, Nerz C, Sieber C, Williams P, Bergquist R, Bosch de Basea M, Buckley E, Hansen C, Mikolaizak AS, Schwickert L, Scott K, Stallforth S, van Uem J, Vereijken B, Cereatti A, Demeyer H, Hopkinson N, Maetzler W, Troosters T, Vogiatzis I, Yarnall A, Becker C, Garcia-Aymerich J, Leocani L, Mazzà C, Rochester L, Sharrack B, Frei A, Puhan M. Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes. NPJ Digit Med 2021; 4:149. [PMID: 34650191 PMCID: PMC8516969 DOI: 10.1038/s41746-021-00513-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/09/2021] [Indexed: 02/08/2023] Open
Abstract
Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.
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Affiliation(s)
- Ashley Polhemus
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Gavin Brittain
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Nikolaos Chynkiamis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Michaela Gross
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Cameron Kirk
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachele Rossanigo
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kristin Taraldsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Diletta Balta
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Sofie Breuls
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Sara Buttery
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Gabriela Cardenas
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Christoph Endress
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Julia Gugenhan
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sarah Koch
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - M Encarna Micó-Amigo
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Corinna Nerz
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Chloé Sieber
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Parris Williams
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ronny Bergquist
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ellen Buckley
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | | | - Lars Schwickert
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Kirsty Scott
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Janet van Uem
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Andrea Cereatti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Heleen Demeyer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | | | - Walter Maetzler
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Thierry Troosters
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
- Department of Respiratory Diseases, University hospitals Leuven, Leuven, Belgium
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle, UK
| | - Alison Yarnall
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clemens Becker
- Department of Clinical Gerontology, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Letizia Leocani
- Department of Neurology, San Raffaele University, Milan, Italy
| | - Claudia Mazzà
- Insigneo Institute, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust & University of Sheffield, Sheffield, England
| | - Anja Frei
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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Youn BY, Ko Y, Moon S, Lee J, Ko SG, Kim JY. Digital Biomarkers for Neuromuscular Disorders: A Systematic Scoping Review. Diagnostics (Basel) 2021; 11:diagnostics11071275. [PMID: 34359358 PMCID: PMC8307187 DOI: 10.3390/diagnostics11071275] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/30/2021] [Accepted: 07/13/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers play a vital role in clinical care. They enable early diagnosis and treatment by identifying a patient's condition and disease course and act as an outcome measure that accurately evaluates the efficacy of a new treatment or drug. Due to the rapid development of digital technologies, digital biomarkers are expected to grow tremendously. In the era of change, this scoping review was conducted to see which digital biomarkers are progressing in neuromuscular disorders, a diverse and broad-range disease group among the neurological diseases, to discover available evidence for their feasibility and reliability. Thus, a total of 10 studies were examined: 9 observational studies and 1 animal study. Of the observational studies, studies were conducted with amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy (DMD), and spinal muscular atrophy (SMA) patients. Non-peer reviewed poster presentations were not considered, as the articles may lead to erroneous results. The only animal study included in the present review investigated the mice model of ALS for detecting rest disturbances using a non-invasive digital biomarker.
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Affiliation(s)
- Bo-Young Youn
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Youme Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Seunghwan Moon
- Department of Global Public Health and Korean Medicine Management, Graduate School, Kyung Hee University, Seoul 02447, Korea; (B.-Y.Y.); (S.M.)
| | - Jinhee Lee
- Department of Korean Medicine, Graduate School, Kyung Hee University, Seoul 02447, Korea;
| | - Seung-Gyu Ko
- Department of Preventive Medicine, Kyung Hee University, Seoul 02447, Korea; (Y.K.); (S.-G.K.)
| | - Jee-Young Kim
- Department of Neurology, Cheongna Best Rehabilitation Hospital, Incheon 22883, Korea
- Correspondence:
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Hartl D, de Luca V, Kostikova A, Laramie J, Kennedy S, Ferrero E, Siegel R, Fink M, Ahmed S, Millholland J, Schuhmacher A, Hinder M, Piali L, Roth A. Translational precision medicine: an industry perspective. J Transl Med 2021; 19:245. [PMID: 34090480 PMCID: PMC8179706 DOI: 10.1186/s12967-021-02910-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/25/2021] [Indexed: 02/08/2023] Open
Abstract
In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.
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Affiliation(s)
- Dominik Hartl
- Novartis Institutes for BioMedical Research, Basel, Switzerland.
- Department of Pediatrics I, University of Tübingen, Tübingen, Germany.
| | - Valeria de Luca
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Anna Kostikova
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Jason Laramie
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Scott Kennedy
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Enrico Ferrero
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Richard Siegel
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Martin Fink
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | | | - Markus Hinder
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Luca Piali
- Roche Innovation Center Basel, Basel, Switzerland
| | - Adrian Roth
- Roche Innovation Center Basel, Basel, Switzerland
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Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021; 12:669811. [PMID: 34012452 PMCID: PMC8128142 DOI: 10.3389/fimmu.2021.669811] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 12/16/2022] Open
Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
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Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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49
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Gielis K, Vanden Abeele ME, Verbert K, Tournoy J, De Vos M, Vanden Abeele V. Detecting Mild Cognitive Impairment via Digital Biomarkers of Cognitive Performance Found in Klondike Solitaire: A Machine-Learning Study. Digit Biomark 2021; 5:44-52. [PMID: 33791448 DOI: 10.1159/000514105] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/28/2020] [Indexed: 12/31/2022] Open
Abstract
Background Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient's cognition. Objective To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts. Methods Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score. Results The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests. Conclusion The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations.
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Affiliation(s)
| | | | | | - Jos Tournoy
- Department of Geriatric Medicine, University Hospital Leuven, Leuven, Belgium.,Department of Public Healthcare and Primary Care, Gerontology and Geriatrics, KU Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering, KU Leuven, Leuven, Belgium
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50
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Schneider RB, Omberg L, Macklin EA, Daeschler M, Bataille L, Anthwal S, Myers TL, Baloga E, Duquette S, Snyder P, Amodeo K, Tarolli CG, Adams JL, Callahan KF, Gottesman J, Kopil CM, Lungu C, Ascherio A, Beck JC, Biglan K, Espay AJ, Tanner C, Oakes D, Shoulson I, Novak D, Kayson E, Ray Dorsey E, Mangravite L, Schwarzschild MA, Simuni T. Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD). Ann Clin Transl Neurol 2021; 8:308-320. [PMID: 33350601 PMCID: PMC7886038 DOI: 10.1002/acn3.51236] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/11/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression. METHODS Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online. RESULTS From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform. INTERPRETATION It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.
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Affiliation(s)
- Ruth B. Schneider
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Eric A. Macklin
- Biostatistics CenterMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Margaret Daeschler
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Lauren Bataille
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Shalini Anthwal
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Taylor L. Myers
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Elizabeth Baloga
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Sidney Duquette
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Katherine Amodeo
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Christopher G Tarolli
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Jamie L. Adams
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Joshua Gottesman
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Catherine M. Kopil
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Codrin Lungu
- Division of Clinical ResearchNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Alberto Ascherio
- Department of NutritionHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | | | - Kevin Biglan
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Eli Lilly and CompanyIndianapolisIndianaUSA
| | - Alberto J. Espay
- Department of NeurologyUniversity of CincinnatiCincinnatiOhioUSA
| | - Caroline Tanner
- Department of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan Francisco Veterans Affairs Health Care SystemSan FranciscoCaliforniaUSA
| | - David Oakes
- Department of BiostatisticsUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Ira Shoulson
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Grey Matter TechnologiesSarasotaFloridaUSA
| | - Dan Novak
- Parkinson’s FoundationNew YorkNew YorkUSA
| | - Elise Kayson
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Earl Ray Dorsey
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | | | - Tanya Simuni
- Department of NeurologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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