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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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Konnyu KJ, Yogasingam S, Lépine J, Sullivan K, Alabousi M, Edwards A, Hillmer M, Karunananthan S, Lavis JN, Linklater S, Manns BJ, Moher D, Mortazhejri S, Nazarali S, Paprica PA, Ramsay T, Ryan PM, Sargious P, Shojania KG, Straus SE, Tonelli M, Tricco A, Vachon B, Yu CH, Zahradnik M, Trikalinos TA, Grimshaw JM, Ivers N. Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes. Cochrane Database Syst Rev 2023; 5:CD014513. [PMID: 37254718 PMCID: PMC10233616 DOI: 10.1002/14651858.cd014513] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
BACKGROUND There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations. OBJECTIVES To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care. SEARCH METHODS We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section. SELECTION CRITERIA We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy. DATA COLLECTION AND ANALYSIS We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors. Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available. MAIN RESULTS We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted. Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three. Combinations of the three most effective QI strategies were estimated to lead to the below effects: - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%; - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg; - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other). AUTHORS' CONCLUSIONS There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.
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Affiliation(s)
- Kristin J Konnyu
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sharlini Yogasingam
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Johanie Lépine
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Katrina Sullivan
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | | | - Alun Edwards
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Michael Hillmer
- Institute for Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | - Sathya Karunananthan
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Canada
| | - John N Lavis
- McMaster Health Forum, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
| | - Stefanie Linklater
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Braden J Manns
- Department of Medicine and Community Health Sciences, University of Calgary, Calgary, Canada
| | - David Moher
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Sameh Mortazhejri
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Samir Nazarali
- Department of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, Canada
| | - P Alison Paprica
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Timothy Ramsay
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | | | - Peter Sargious
- Department of Medicine, University of Calgary, Calgary, Canada
| | - Kaveh G Shojania
- University of Toronto Centre for Patient Safety, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Sharon E Straus
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital and University of Toronto, Toronto, Canada
| | - Marcello Tonelli
- Department of Medicine and Community Health Sciences, University of Calgary, Calgary, Canada
| | - Andrea Tricco
- Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital and University of Toronto, Toronto, Canada
- Epidemiology Division and Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
- Queen's Collaboration for Health Care Quality: A JBI Centre of Excellence, Queen's University, Kingston, Canada
| | - Brigitte Vachon
- School of Rehabilitation, Occupational Therapy Program, University of Montreal, Montreal, Canada
| | - Catherine Hy Yu
- Department of Medicine, St. Michael's Hospital, Toronto, Canada
| | - Michael Zahradnik
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Thomas A Trikalinos
- Departments of Health Services, Policy, and Practice and Biostatistics, Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Jeremy M Grimshaw
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Noah Ivers
- Department of Family and Community Medicine, Women's College Hospital, Toronto, Canada
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Cai J, Xu H, Jiang S, Sung J, Sawhney R, Broadley S, Sun J. Effectiveness of telemonitoring intervention on glycaemic control in patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Diabetes Res Clin Pract 2023; 201:110727. [PMID: 37217016 DOI: 10.1016/j.diabres.2023.110727] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 02/18/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a rising global health concern that requires long-term treatment and close monitoring. Telemonitoring has been shown to be a promising tool to facilitate patient-physician interaction and improve glycaemic control. METHOD Randomised controlled trials (RCT) of telemonitoring in T2DM published between 1990 and 2021 were searched through multiple electronic databases. The primary outcome variables included HbA1c and fasting blood glucose (FBG), and BMI was a secondary outcome variable. RESULTS Thirty RCT with a total of 4,678 participants were included in this study. Twenty-six studies reported on HbA1c, which was shown to be significantly lower in participants on telemonitoring when compared to conventional care. Ten studies investigated FBG which collectively showed no statistically significant difference. Subgroup analysis demonstrated the effect of telemonitoring on glycaemic control is influenced by a range of factors concerning system practicality, user engagement, patient characteristics and disease education. CONCLUSION Telemonitoring exhibited a great potential to improve T2DM management. Several technical features and patient factors may influence the effectiveness of telemonitoring. Further studies are needed to verify the findings and address limitations before its implementation into routine practice.
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Affiliation(s)
- Jinxuan Cai
- School of Medicine and Dentistry Griffith University, Q4215, Australia.
| | - Huaying Xu
- School of Medicine and Dentistry Griffith University, Q4215, Australia.
| | - Stephen Jiang
- School of Medicine and Dentistry Griffith University, Q4215, Australia.
| | - Jerry Sung
- School of Medicine and Dentistry Griffith University, Q4215, Australia.
| | - Rakshat Sawhney
- School of Medicine and Dentistry Griffith University, Q4215, Australia.
| | - Simon Broadley
- School of Medicine and Dentistry Griffith University, Q4215, Australia; Menzies Health Institute Queensland, Griffith University, Q4215, Australia; Department of Neurology, Gold Coast University Hospital, Q4222, Australia.
| | - Jing Sun
- School of Medicine and Dentistry Griffith University, Q4215, Australia; Menzies Health Institute Queensland, Griffith University, Q4215, Australia; Institute for Integrated and Intelligent System, Griffith University, Q4222, Australia.
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Zhang X, Zhang L, Lin Y, Liu Y, Yang X, Cao W, Ji Y, Chang C. Effects of E-health-based interventions on glycemic control for patients with type 2 diabetes: a Bayesian network meta-analysis. Front Endocrinol (Lausanne) 2023; 14:1068254. [PMID: 37214251 PMCID: PMC10196691 DOI: 10.3389/fendo.2023.1068254] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/22/2023] [Indexed: 05/24/2023] Open
Abstract
The high disease burden of type 2 diabetes seriously affects the quality of life of patients, and with the deep integration of the Internet and healthcare, the application of electronic tools and information technology to has become a trend for disease management. The aim of this study was to evaluate the effectiveness of different forms and durations of E-health interventions in achieving glycemic control in type 2 diabetes patients. PubMed, Embase, Cochrane, and Clinical Trials.gov were searched for randomized controlled trials reporting different forms of E-health intervention for glycemic control in type 2 diabetes patients, including comprehensive measures (CM), smartphone applications (SA), phone calls (PC), short message service (SMS), websites (W), wearable devices (WD), and usual care. The inclusion criteria were as follows: (1) adults (age≥18) with type 2 diabetes mellitus; (2) intervention period ≥1 month; (3) outcome HbA1c (%); and (4) randomized control of E-health based approaches. Cochrane tools were used to assess the risk of bias. R 4.1.2 was used to conduct the Bayesian network meta-analysis. A total of 88 studies with 13,972 type 2 diabetes patients were included. Compared to the usual care group, the SMS-based intervention was superior in reducing HbA1c levels (mean difference (MD)-0.56, 95% confidence interval (CI): -0.82 to -0.31), followed by SA (MD-0.45, 95% CI: -0.61 to -0.30), CM (MD-0.41, 95% CI: -0.57 to -0.25), W (MD-0.39, 95% CI: -0.60 to -0.18) and PC (MD-0.32, 95% CI: -0.50 to -0.14) (p < 0.05). Subgroup analysis revealed that intervention durations of ≤6 months were most effective. All type of E-health based approaches can improve glycemic control in patients with type 2 diabetes. SMS is a high-frequency, low-barrier technology that achieves the best effect in lowering HbA1c, with ≤6 months being the optimal intervention duration. Systematic review registration https://www.crd.york.ac.uk/prospero, identifier CRD42022299896.
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Zulfiqar AA, Massimbo DND, Hajjam M, Gény B, Talha S, Hajjam J, Ervé S, Hassani AHE, Andrès E. Glycemic Disorder Risk Remote Monitoring Program in the COVID-19 Very Elderly Patients: Preliminary Results. Front Physiol 2021; 12:749731. [PMID: 34777011 PMCID: PMC8579000 DOI: 10.3389/fphys.2021.749731] [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: 07/29/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. This was the basis for the “GER-e-TEC COVID study,” an experiment involving the use of the smart MyPrediTM e-platform to automatically detect the exacerbation of glycemic disorder risk in COVID-19 older diabetic patients. Methods: The MyPrediTM platform is connected to a medical analysis system that receives physiological data from medical sensors in real time and analyzes this data to generate (when necessary) alerts. An experiment was conducted between December 14th, 2020 and February 25th, 2021 to test this alert system. During this time, the platform was used on COVID-19 patients being monitored in an internal medicine COVID-19 unit at the University Hospital of Strasbourg. The alerts were compiled and analyzed in terms of sensitivity, specificity, positive and negative predictive values with respect to clinical data. Results: 10 older diabetic COVID-19 patients in total were monitored remotely, six of whom were male. The mean age of the patients was 84.1 years. The patients used the telemedicine solution for an average of 14.5 days. 142 alerts were emitted for the glycemic disorder risk indicating hyperglycemia, with an average of 20.3 alerts per patient and a standard deviation of 26.6. In our study, we did not note any hypoglycemia, so the system emitted any alerts. For the sensitivity of alerts emitted, the results were extremely satisfactory, and also in terms of positive and negative predictive values. In terms of survival analysis, the number of alerts and gender played no role in the length of the hospital stay, regardless of the reason for the hospitalization (COVID-19 management). Conclusion: This work is a pilot study with preliminary results. To date, relatively few projects and trials in diabetic patients have been run within the “telemedicine 2.0” setting, particularly using AI, ICT and the Web 2.0 in the era of COVID-19 disease.
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Affiliation(s)
- Abrar-Ahmad Zulfiqar
- Service de Médecine Interne, Diabète et Maladies Métaboliques de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 "Mitochondrie, Stress Oxydant et Protection Musculaire," Faculté de Médecine-Université de Strasbourg, Strasbourg, France
| | | | | | - Bernard Gény
- Faculté de Médecine-Université de Strasbourg, Service de Physiologie et d'Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 "Mitochondrie, Stress Oxydant et Protection Musculaire," Strasbourg, France
| | - Samy Talha
- Faculté de Médecine-Université de Strasbourg, Service de Physiologie et d'Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 "Mitochondrie, Stress Oxydant et Protection Musculaire," Strasbourg, France
| | - Jawad Hajjam
- Centre d'Expertise des TIC pour l'Autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM)-Angers, Angers, France
| | - Sylvie Ervé
- Centre d'Expertise des TIC pour l'Autonomie (CenTich) et Mutualité Française Anjou-Mayenne (MFAM)-Angers, Angers, France
| | - Amir Hajjam El Hassani
- Laboratoire IRTES-SeT, Université de Technologie de Belfort-Montbéliard (UTBM), Belfort, France
| | - Emmanuel Andrès
- Service de Médecine Interne, Diabète et Maladies Métaboliques de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 "Mitochondrie, Stress Oxydant et Protection Musculaire," Faculté de Médecine-Université de Strasbourg, Strasbourg, France.,Faculté de Médecine-Université de Strasbourg, Service de Physiologie et d'Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg et Equipe EA 3072 "Mitochondrie, Stress Oxydant et Protection Musculaire," Strasbourg, France
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Andrès E, Meyer L, Zulfiqar AA, Hajjam M, Talha S, Bahougne T, Ervé S, Hajjam J, Doucet J, Jeandidier N, Hajjam El Hassani A. Telemonitoring in diabetes: evolution of concepts and technologies, with a focus on results of the more recent studies. J Med Life 2019; 12:203-214. [PMID: 31666818 PMCID: PMC6814890 DOI: 10.25122/jml-2019-0006] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
This is a narrative review of telemonitoring (remote monitoring) projects and studies within the field of diabetes, with a focus on results of the more recent studies. Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development. Two examples are the DIABETe and Telesage telemonitoring project which perfectly fits within the telemedicine 2.0 framework – the first to include artificial intelligence (AI) with MyPrediTM and DiabeoTM. Mainly, these projects and studies show that telemonitoring diabetic result in: improvements in control of blood glucose (BG) level and significant reduction in HbA1c (e.g., for Telescot et TELESAGE studies); positive impact on co-morbidities (arterial hypertension, weight, dyslipidemia) (e.g., for Telescot and DIABETe studies); better patient’s quality of life (e.g., for DIABETe study); positive impact on appropriation of the disease by patients and/or greater adherence to therapeutic and hygiene-dietary measures (e.g., The Utah Remote Monitoring Project); and at least, good receptiveness by patients and their empowerment. To date, the magnitude of its effects remains debatable, especially with the variation in patients’ characteristics (e.g., background, ability for self-management, medical condition), samples selection and approach for the treatment of control groups. All of the recent studies have been classified as “Moderate” to “High”.
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Affiliation(s)
- Emmanuel Andrès
- Service de Médecine Interne, Diabète et Maladies Métaboliques de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg, 1, porte de l'Hôpital, 67091 Strasbourg cedex France.,Equipe de recherche EA 3072 «Mitochondrie, Stress oxydant et Protection musculaire», Faculté de Médecine de Strasbourg, Université de Strasbourg (Unistra), Strasbourg, France
| | - Laurent Meyer
- Service d'Endocrinologie et de Diabétologie de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Abrar-Ahmad Zulfiqar
- Equipe de recherche EA 3072 «Mitochondrie, Stress oxydant et Protection musculaire», Faculté de Médecine de Strasbourg, Université de Strasbourg (Unistra), Strasbourg, France.,Service de Médecine Interne, Gériatrie et Thérapeutique, CHU de Rouen, France
| | | | - Samy Talha
- Equipe de recherche EA 3072 «Mitochondrie, Stress oxydant et Protection musculaire», Faculté de Médecine de Strasbourg, Université de Strasbourg (Unistra), Strasbourg, France.,Service de Physiologie et d'Explorations Fonctionnelles, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Thibault Bahougne
- Service d'Endocrinologie et de Diabétologie de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Sylvie Ervé
- Centre d'expertise des Technologies de l'Information et de la Communication pour l'autonomie (CENTICH) et Mutualité Française Anjou-Mayenne (MFAM), Angers, France
| | - Jawad Hajjam
- Centre d'expertise des Technologies de l'Information et de la Communication pour l'autonomie (CENTICH) et Mutualité Française Anjou-Mayenne (MFAM), Angers, France
| | - Jean Doucet
- Service de Médecine Interne, Gériatrie et Thérapeutique, CHU de Rouen, France
| | - Nathalie Jeandidier
- Service d'Endocrinologie et de Diabétologie de la Clinique Médicale B, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Amir Hajjam El Hassani
- Equipe de recherche EA 4662 «Nanomédecine, Imagerie, Thérapeutiques», Université de Technologie de Belfort-Montbéliard (UTBM), Belfort-Montbéliard, France
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Alwashmi MF, Mugford G, Abu-Ashour W, Nuccio M. A Digital Diabetes Prevention Program (Transform) for Adults With Prediabetes: Secondary Analysis. JMIR Diabetes 2019; 4:e13904. [PMID: 31350833 PMCID: PMC6688434 DOI: 10.2196/13904] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/12/2019] [Accepted: 06/06/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The prevalence of diabetes is increasing among adults globally. Research has demonstrated that a diabetes prevention program (DPP), which focuses on developing and maintaining health-promoting lifestyle modifications, can prevent or delay the onset of type 2 diabetes among at-risk individuals. The implementation of a digitally adapted DPP has the potential to prevent prediabetes on a national and global scale by using technology and behavior change science. OBJECTIVE This study aimed to investigate the effects of a novel digital therapeutic DPP (Transform) on weight loss, body mass index (BMI), exercise frequency, and work absenteeism. METHODS This study was a secondary analysis of retrospective data of adults with prediabetes who were enrolled in the Transform DPP from December 2016 to December 2017. The program incorporates interactive mobile computing, remote monitoring, an evidence-based curriculum, behavior tracking tools, health coaching, and online peer support to prevent or delay the onset of type 2 diabetes. The analysis included data that were collected at baseline and after 4 months of the Transform DPP. RESULTS The sample (N=273) comprised people with prediabetes who completed 4 months of the Transform program. Participants included 70.3% women, with a mean age of 54.0 (SD 11.2) years. On average, participants decreased their weight by 13.3 lbs (6.5%) and their BMI by 1.9 kg/m2. On average, participants increased their exercise frequency by 1.7 days per week, and absenteeism was reduced by almost half a day per month. CONCLUSIONS These results suggest that the digital therapeutic DPP (Transform) is effective at preventing type 2 diabetes through a significant reduction in body weight and an increase of physical activity. A prospective, controlled clinical study is warranted to validate these findings.
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Affiliation(s)
| | | | | | - Misa Nuccio
- Blue Mesa Health, New York, NY, United States
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Mise au point sur les projets de recherche dans le domaine de la télémédecine dans le diabète, avec un focus sur les projets de télésurveillance 2.0. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/s1957-2557(19)30027-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Rasche P, Mertens A, Miron-Shatz T, Berzon C, Schlick CM, Jahn M, Becker S. Seamless recording of glucometer measurements among older experienced diabetic patients - A study of perception and usability. PLoS One 2018; 13:e0197455. [PMID: 29799861 PMCID: PMC5969745 DOI: 10.1371/journal.pone.0197455] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 05/02/2018] [Indexed: 11/19/2022] Open
Abstract
Self-measurement and documentation of blood-glucose are critical elements of diabetes management, particularly in regimes including insulin. In this study, we analyze the usability of iBG-STAR, the first blood glucose meter connectable to a smartphone. This technology records glucometer measurements, removing the burden of documentation from diabetic patients. This study assesses the potential for implementation of iBG-STAR in routine care. Twelve long-term diabetic patients (4 males; median age of 66.5 years) were enrolled in the study. N = 4/12 reported diabetic polyneuropathy. Reported subjective mental workload for all tasks related to iBG-STAR was on average lower than 12 points, corresponding to the verbal code 'nearly no effort needed'. A "Post Study System Usability Questionnaire", evaluated the glucometer at an average value of 2.06 (SD = 1.02) on a 7-Likert-scale (1 = 'I fully agree' to 7 = 'I completely disagree') for usability. These results represent a positive user-experience. Patients with polyneuropathy may experience physical difficulties in completing the tasks, thereby affecting usability. Technologically savvy patients (n = 6) with a positive outlook on diabetes assessed the product as a suitable tool for themselves and would recommend to other diabetic patients. The main barrier to regular use was treating physicians' inability to retrieve digitally recorded data. This barrier was due to a shortcoming in interoperability of mobile devices and medical information systems.
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Affiliation(s)
- Peter Rasche
- Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Aachen, Germany
| | - Alexander Mertens
- Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Aachen, Germany
| | - Talya Miron-Shatz
- Center for Medical Decision Making, Business School, Ono Academic College, Kiryat Ono, Israel
- Center for Medicine in the Public Interest, New York, NY, United States of America
| | - Corinne Berzon
- Center for Medical Decision Making, Business School, Ono Academic College, Kiryat Ono, Israel
| | - Christopher M. Schlick
- Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Aachen, Germany
| | - Michael Jahn
- Department of Nephrology, University Duisburg-Essen, Essen, Germany
| | - Stefan Becker
- Department of Nephrology, University Duisburg-Essen, Essen, Germany
- * E-mail:
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