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Gashi S, Oldrati P, Moebus M, Hilty M, Barrios L, Ozdemir F, Kana V, Lutterotti A, Rätsch G, Holz C. Modeling multiple sclerosis using mobile and wearable sensor data. NPJ Digit Med 2024; 7:64. [PMID: 38467710 PMCID: PMC10928076 DOI: 10.1038/s41746-024-01025-8] [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: 07/03/2023] [Accepted: 02/02/2024] [Indexed: 03/13/2024] Open
Abstract
Multiple sclerosis (MS) is a neurological disease of the central nervous system that is the leading cause of non-traumatic disability in young adults. Clinical laboratory tests and neuroimaging studies are the standard methods to diagnose and monitor MS. However, due to infrequent clinic visits, it is fundamental to identify remote and frequent approaches for monitoring MS, which enable timely diagnosis, early access to treatment, and slowing down disease progression. In this work, we investigate the most reliable, clinically useful, and available features derived from mobile and wearable devices as well as their ability to distinguish people with MS (PwMS) from healthy controls, recognize MS disability and fatigue levels. To this end, we formalize clinical knowledge and derive behavioral markers to characterize MS. We evaluate our approach on a dataset we collected from 55 PwMS and 24 healthy controls for a total of 489 days conducted in free-living conditions. The dataset contains wearable sensor data - e.g., heart rate - collected using an arm-worn device, smartphone data - e.g., phone locks - collected through a mobile application, patient health records - e.g., MS type - obtained from the hospital, and self-reports - e.g., fatigue level - collected using validated questionnaires administered via the mobile application. Our results demonstrate the feasibility of using features derived from mobile and wearable sensors to monitor MS. Our findings open up opportunities for continuous monitoring of MS in free-living conditions and can be used to evaluate and guide the effectiveness of treatments, manage the disease, and identify participants for clinical trials.
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Affiliation(s)
- Shkurta Gashi
- Department of Computer Science, ETH Zürich, Zürich, Switzerland.
- ETH AI Center, ETH Zürich, Zürich, Switzerland.
| | - Pietro Oldrati
- Institute for Implementation Science in Health Care, University of Zürich, Zürich, Switzerland
| | - Max Moebus
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
| | - Marc Hilty
- Department of Neurology, University Hospital Zürich, Zürich, Switzerland
| | - Liliana Barrios
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
| | - Firat Ozdemir
- Swiss Data Science Center, ETH Zürich & EPFL, Zürich, Switzerland
| | - Veronika Kana
- Department of Neurology, University Hospital Zürich, Zürich, Switzerland
| | - Andreas Lutterotti
- Department of Neurology, University Hospital Zürich, Zürich, Switzerland
| | - Gunnar Rätsch
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- ETH AI Center, ETH Zürich, Zürich, Switzerland
| | - Christian Holz
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- ETH AI Center, ETH Zürich, Zürich, Switzerland
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Riley C, Venkatesh S, Dhand A, Doshi N, Kavak K, Levit E, Perrone C, Weinstock-Guttman B, Longbrake E, De Jager P, Xia Z. Impact of the COVID-19 Pandemic on the Personal Networks and Neurological Outcomes of People With Multiple Sclerosis: Cross-Sectional and Longitudinal Case-Control Study. JMIR Public Health Surveill 2024; 10:e45429. [PMID: 38319703 PMCID: PMC10879979 DOI: 10.2196/45429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 08/05/2023] [Accepted: 08/31/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has negatively affected the social fabric. OBJECTIVE We evaluated the associations between personal social networks and neurological function in people with multiple sclerosis (pwMS) and controls in the prepandemic and pandemic periods. METHODS During the early pandemic (March-December 2020), 8 cohorts of pwMS and controls completed a questionnaire quantifying the structure and composition of their personal social networks, including the health behaviors of network members. Participants from 3 of the 8 cohorts had additionally completed the questionnaire before the pandemic (2017-2019). We assessed neurological function using 3 interrelated patient-reported outcomes: Patient Determined Disease Steps (PDDS), Multiple Sclerosis Rating Scale-Revised (MSRS-R), and Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function. We identified the network features associated with neurological function using paired 2-tailed t tests and covariate-adjusted regressions. RESULTS In the cross-sectional analysis of the pandemic data from 1130 pwMS and 1250 controls during the pandemic, having a higher percentage of network members with a perceived negative health influence was associated with worse disability in pwMS (MSRS-R: β=2.181, 95% CI 1.082-3.279; P<.001) and poor physical function in controls (PROMIS Physical Function: β=-5.707, 95% CI -7.405 to -4.010; P<.001). In the longitudinal analysis of 230 pwMS and 136 controls, the networks of all participants contracted, given an increase in constraint (pwMS-prepandemic: mean 52.24, SD 15.81; pwMS-pandemic: mean 56.77, SD 18.91; P=.006. Controls-prepandemic: mean 48.07, SD 13.36; controls-pandemic: mean 53.99, SD 16.31; P=.001) and a decrease in network size (pwMS-prepandemic: mean 8.02, SD 5.70; pwMS-pandemic: mean 6.63, SD 4.16; P=.003. Controls-prepandemic: mean 8.18, SD 4.05; controls-pandemic: mean 6.44, SD 3.92; P<.001), effective size (pwMS-prepandemic: mean 3.30, SD 1.59; pwMS-pandemic: mean 2.90, SD 1.50; P=.007. Controls-prepandemic: mean 3.85, SD 1.56; controls-pandemic: mean 3.40, SD 1.55; P=.01), and maximum degree (pwMS-prepandemic: mean 4.78, SD 1.86; pwMS-pandemic: mean 4.32, SD 1.92; P=.01. Controls-prepandemic: mean 5.38, SD 1.94; controls-pandemic: mean 4.55, SD 2.06; P<.001). These network changes were not associated with worsening function. The percentage of kin in the networks of pwMS increased (mean 46.06%, SD 29.34% to mean 54.36%, SD 30.16%; P=.003) during the pandemic, a change that was not seen in controls. CONCLUSIONS Our findings suggest that high perceived negative health influence in the network was associated with worse function in all participants during the pandemic. The networks of all participants became tighter knit, and the percentage of kin in the networks of pwMS increased during the pandemic. Despite these perturbations in social connections, network changes from the prepandemic to the pandemic period were not associated with worsening function in all participants, suggesting possible resilience.
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Affiliation(s)
- Claire Riley
- Columbia University Irving Medical Center, New York, NY, United States
| | | | - Amar Dhand
- Brigham and Women's Hospital, Boston, MA, United States
| | - Nandini Doshi
- University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Elle Levit
- Yale University, New Haven, CT, United States
| | | | | | | | - Philip De Jager
- Columbia University Irving Medical Center, New York, NY, United States
| | - Zongqi Xia
- University of Pittsburgh, Pittsburgh, PA, United States
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Yang W, Yang L, Mao S, Liu D, Wang L. Analysis of the effect of nursing care based on action research method on the prevention of postoperative lymphedema in breast cancer patients. Medicine (Baltimore) 2023; 102:e36743. [PMID: 38206748 PMCID: PMC10754543 DOI: 10.1097/md.0000000000036743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/30/2023] [Indexed: 01/13/2024] Open
Abstract
RATIONALE In recent times, the pervasive adoption of the action research method has garnered substantial attention both domestically and internationally. Its integration has traversed various domains of nursing research, nursing education, and nursing practice, yielding commendable outcomes. However, a notable gap persists, as this method remains untapped in the realm of nursing care concerning the prevention of postoperative lymphedema in breast cancer patients. DIAGNOSIS To employ the action research methodology in the context of patients undergoing axillary lymph node dissection surgery for breast cancer, aiming to investigate its impact on mitigating postoperative lymphedema and assessing its influence on the patient's quality of life, as well as levels of anxiety and depression postoperatively. INTERVENTION The study focused on breast cancer patients admitted to our hospital from January 2022 to December 2022. Among them, 44 patients from January to June constituted the control group, while 44 patients from July to December comprised the observation group. Conventional nursing measures were applied to the control group, whereas the observation group received nursing interventions rooted in the action research method. A comparative analysis was conducted between the 2 groups, assessing the incidence of postoperative lymphedema, daily life ability, as well as levels of anxiety and depression. OUTCOMES The prevalence of edema was notably reduced in the observation group (20.93%) compared to the control group (42.22%), with a statistically significant difference. Throughout the study, patients in both groups exhibited increased Barthel Index Scale scores from the study's initiation, and the scores for the observation group surpassed those of the control group, reaching statistical significance (P < .05). Furthermore, by the study's conclusion, anxiety and depression scores for patients in both groups were diminished compared to the study's commencement, and the observation group demonstrated significantly lower scores in anxiety and depression compared to the control group (P < .05). LESSONS The implementation of nursing care grounded in the action research methodology exhibits a capacity to diminish both the occurrence and intensity of postoperative lymphedema in breast cancer patients. Concurrently, it enhances the patients' daily life functionality and mitigates symptoms of anxiety and depression.
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Affiliation(s)
- Weijuan Yang
- Breast Surgery Department, Jiangsu Province Hospital, and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Li Yang
- Breast Surgery Department, Jiangsu Province Hospital, and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shuangwei Mao
- Breast Surgery Department, Jiangsu Province Hospital, and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Dandan Liu
- Breast Surgery Department, Jiangsu Province Hospital, and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lili Wang
- Breast Surgery Department, Jiangsu Province Hospital, and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Pinarello C, Elmers J, Inojosa H, Beste C, Ziemssen T. Management of multiple sclerosis fatigue in the digital age: from assessment to treatment. Front Neurosci 2023; 17:1231321. [PMID: 37869507 PMCID: PMC10585158 DOI: 10.3389/fnins.2023.1231321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
Fatigue is one of the most disabling symptoms of Multiple Sclerosis (MS), affecting more than 80% of patients over the disease course. Nevertheless, it has a multi-faceted and complex nature, making its diagnosis, evaluation, and treatment extremely challenging in clinical practice. In the last years, digital supporting tools have emerged to support the care of people with MS. These include not only smartphone or table-based apps, but also wearable devices or novel techniques such as virtual reality. Furthermore, an additional effective and cost-efficient tool for the therapeutic management of people with fatigue is becoming increasingly available. Virtual reality and e-Health are viable and modern tools to both assess and treat fatigue, with a variety of applications and adaptability to patient needs and disability levels. Most importantly, they can be employed in the patient's home setting and can not only bridge clinic visits but also be complementary to the monitoring and treatment means for those MS patients who live far away from healthcare structures. In this narrative review, we discuss the current knowledge and future perspectives in the digital management of fatigue in MS. These may also serve as sources for research of novel digital biomarkers in the identification of disease activity and progression.
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Affiliation(s)
- Chiara Pinarello
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
| | - Julia Elmers
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Hernán Inojosa
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technical University of Dresden, Dresden, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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Voigt I, Inojosa H, Wenk J, Akgün K, Ziemssen T. Building a monitoring matrix for the management of multiple sclerosis. Autoimmun Rev 2023; 22:103358. [PMID: 37178996 DOI: 10.1016/j.autrev.2023.103358] [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: 04/27/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023]
Abstract
Multiple sclerosis (MS) has a longitudinal and heterogeneous course, with an increasing number of therapy options and associated risk profiles, leading to a constant increase in the number of parameters to be monitored. Even though important clinical and subclinical data are being generated, treating neurologists may not always be able to use them adequately for MS management. In contrast to the monitoring of other diseases in different medical fields, no target-based approach for a standardized monitoring in MS has been established yet. Therefore, there is an urgent need for a standardized and structured monitoring as part of MS management that is adaptive, individualized, agile, and multimodal-integrative. We discuss the development of an MS monitoring matrix which can help facilitate data collection over time from different dimensions and perspectives to optimize the treatment of people with MS (pwMS). In doing so, we show how different measurement tools can combined to enhance MS treatment. We propose to apply the concept of patient pathways to disease and intervention monitoring, not losing track of their interrelation. We also discuss the use of artificial intelligence (AI) to improve the quality of processes, outcomes, and patient safety, as well as personalized and patient-centered care. Patient pathways allow us to track the patient's journey over time and can always change (e.g., when there is a switch in therapy). They therefore may assist us in the continuous improvement of monitoring in an iterative process. Improving the monitoring process means improving the care of pwMS.
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Affiliation(s)
- Isabel Voigt
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Hernan Inojosa
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Judith Wenk
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katja Akgün
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany.
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Woelfle T, Bourguignon L, Lorscheider J, Kappos L, Naegelin Y, Jutzeler CR. Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective. J Med Internet Res 2023; 25:e44428. [PMID: 37498655 PMCID: PMC10415952 DOI: 10.2196/44428] [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: 11/18/2022] [Revised: 12/19/2022] [Accepted: 05/04/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains. OBJECTIVE This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades. METHODS We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. RESULTS Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good. CONCLUSIONS Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS. TRIAL REGISTRATION PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.
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Affiliation(s)
- Tim Woelfle
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Lucie Bourguignon
- Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
| | - Johannes Lorscheider
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ludwig Kappos
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Yvonne Naegelin
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
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Triantafyllidis A, Segkouli S, Zygouris S, Michailidou C, Avgerinakis K, Fappa E, Vassiliades S, Bougea A, Papagiannakis N, Katakis I, Mathioudis E, Sorici A, Bajenaru L, Tageo V, Camonita F, Magga-Nteve C, Vrochidis S, Pedullà L, Brichetto G, Tsakanikas P, Votis K, Tzovaras D. Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3396. [PMID: 37050456 PMCID: PMC10098868 DOI: 10.3390/s23073396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.
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Affiliation(s)
- Andreas Triantafyllidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Sofia Segkouli
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Stelios Zygouris
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
- Department of Psychology, University of Western Macedonia, 53100 Florina, Greece
| | | | | | | | | | - Anastasia Bougea
- Eginition Hospital, 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Nikos Papagiannakis
- Eginition Hospital, 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, 15772 Athens, Greece
| | - Ioannis Katakis
- Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417 Nicosia, Cyprus
| | - Evangelos Mathioudis
- Department of Computer Science, School of Sciences and Engineering, University of Nicosia, 2417 Nicosia, Cyprus
| | - Alexandru Sorici
- Department of Computer Science, University Politechnica of Bucharest, 060042 Bucharest, Romania
| | - Lidia Bajenaru
- Department of Computer Science, University Politechnica of Bucharest, 060042 Bucharest, Romania
| | | | | | - Christoniki Magga-Nteve
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Stefanos Vrochidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | | | | | - Panagiotis Tsakanikas
- Institute of Communication and Computer Systems, National Technical University of Athens, 10682 Athens, Greece
| | - Konstantinos Votis
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
| | - Dimitrios Tzovaras
- Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thermi, Greece
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Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform 2023; 138:104278. [PMID: 36586498 DOI: 10.1016/j.jbi.2022.104278] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.
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Affiliation(s)
- Ivan Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil.
| | - Ariel Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil; Federal Institute of Maranhão, Brazil
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Jean Marques
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
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