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1.
Models of care in tele-ophthalmology: A scoping review.
Caffery, LJ, Taylor, M, Gole, G, Smith, AC
Journal of telemedicine and telecare. 2019;(2):106-122
Abstract
The objective of this review was to identify and describe telehealth models of care for ophthalmic services. We conducted a scoping review of the literature to identify how ophthalmic care can be delivered by telehealth. We searched the PubMed database to identify relevant articles which were screened based on pre-defined inclusion criteria. For included articles, data were extracted, categorised and analysed. Synthesis of findings was performed narratively. The scoping review included 78 articles describing 62 discrete tele-ophthalmic models of care. Tele-ophthalmic models of care can be used for consultative service, screening, triage and remote supervision. The majority of services were for general eye care and triage ( n = 17; 26%) or emergency services ( n = 8; 12%). The most common conditions for disease-specific models of care were diabetic retinopathy ( n = 14; 21%), and glaucoma ( n = 8; 12%). Most models of care involved local clinicians capturing images and transmitting them to an ophthalmologist for assessment. This scoping review demonstrated tele-ophthalmology to be feasible for consultation, screening, triage and remote supervision applications across a broad range of ophthalmic conditions. A large number of models of care have been identified and described in this review. Considerable collaboration between patient-end clinicians and substantial infrastructure is typically required for tele-ophthalmology.
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Engagement and Weight Loss: Results from the Mobile Health and Diabetes Trial.
Muralidharan, S, Ranjani, H, Mohan Anjana, R, Jena, S, Tandon, N, Gupta, Y, Ambekar, S, Koppikar, V, Jagannathan, N, Allender, S, et al
Diabetes technology & therapeutics. 2019;(9):507-513
Abstract
Background: Prevalence of type 2 diabetes (T2D) is increasing worldwide. Identifying and targeting individuals at high risk, is essential for preventing T2D. Several studies point to mobile health initiatives delivered through personal smart devices being a promising approach to diabetes prevention, through weight loss. The aim of the mobile health and diabetes (mDiab) trial was twofold: to achieve 5% weight loss and to look at the association of weight loss with degree of engagement with the mDiab app. Methods: The mDiab randomized control trial was carried out among smartphone users who are at high risk for T2D mellitus in three cities-Chennai, Bengaluru, and New Delhi in India. The intervention was delivered through a mobile phone application along with weekly coach calls for 12 weeks. While individuals in the intervention group individuals received the app, which enabled tracking their weight, physical activity, and diet along with 12 weekly video lessons on T2D prevention and coach calls, the control group received usual care. Results: The intervention group experienced a significant 1 kg weight loss while the control group lost 0.3 kg (P < 0.05). More individuals in the intervention group (n = 139, 15%) met the 5% weight loss target than in the control group (n = 131, 9%). In the intervention group those who viewed the videos experienced greater weight loss (2.4 kg) than those who only attended coach calls (0.9 kg) (P < 0.01). Conclusions: An mHealth intervention helped to achieve moderate weight loss. Future studies should explore the sustainability of this weight loss.
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Effects and costs of real-time cardiac telerehabilitation: randomised controlled non-inferiority trial.
Maddison, R, Rawstorn, JC, Stewart, RAH, Benatar, J, Whittaker, R, Rolleston, A, Jiang, Y, Gao, L, Moodie, M, Warren, I, et al
Heart (British Cardiac Society). 2019;(2):122-129
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Abstract
OBJECTIVE Compare the effects and costs of remotely monitored exercise-based cardiac telerehabilitation (REMOTE-CR) with centre-based programmes (CBexCR) in adults with coronary heart disease (CHD). METHODS Participants were randomised to receive 12 weeks of telerehabilitation or centre-based rehabilitation. REMOTE-CR provided individualised exercise prescription, real-time exercise monitoring/coaching and theory-based behavioural strategies via a bespoke telerehabilitation platform; CBexCR provided individualised exercise prescription and coaching via established rehabilitation clinics. Outcomes assessed at baseline, 12 and/or 24 weeks included maximal oxygen uptake (V̇O2max, primary) modifiable cardiovascular risk factors, exercise adherence, motivation, health-related quality of life and programme delivery, hospital service utilisation and medication costs. The primary hypothesis was a non-inferior between-group difference in V̇O2max at 12 weeks (inferiority margin=-1.25 mL/kg/min); inferiority margins were not set for secondary outcomes. RESULTS 162 participants (mean 61±12.7 years, 86% men) were randomised. V̇O2 max was comparable in both groups at 12 weeks and REMOTE-CR was non-inferior to CBexCR (REMOTE-CR-CBexCR adjusted mean difference (AMD)=0.51 (95% CI -0.97 to 1.98) mL/kg/min, p=0.48). REMOTE-CR participants were less sedentary at 24 weeks (AMD=-61.5 (95% CI -117.8 to -5.3) min/day, p=0.03), while CBexCR participants had smaller waist (AMD=1.71 (95% CI 0.09 to 3.34) cm, p=0.04) and hip circumferences (AMD=1.16 (95% CI 0.06 to 2.27) cm, p=0.04) at 12 weeks. No other between-group differences were detected. Per capita programme delivery (NZD1130/GBP573 vs NZD3466/GBP1758) and medication costs (NZD331/GBP168 vs NZD605/GBP307, p=0.02) were lower for REMOTE-CR. Hospital service utilisation costs were not statistically significantly different (NZD3459/GBP1754 vs NZD5464/GBP2771, p=0.20). CONCLUSION REMOTE-CR is an effective, cost-efficient alternative delivery model that could-as a complement to existing services-improve overall utilisation rates by increasing reach and satisfying unique participant preferences.
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A Tailored Behavioral Intervention to Promote Adherence to the DASH Diet.
Rodriguez, MA, Friedberg, JP, DiGiovanni, A, Wang, B, Wylie-Rosett, J, Hyoung, S, Natarajan, S
American journal of health behavior. 2019;(4):659-670
Abstract
Objectives: In this study, we evaluated the effects of a Transtheoretical model (TTM)-based tailored behavioral intervention (TBI), a non-tailored intervention (NTI) or usual care (UC) on: (1) the Dietary Approaches to Stop Hypertension (DASH) dietary pattern in 533 individuals with uncontrolled hypertension; and (2) the change from baseline to 6 months in proportion of participants in action or maintenance stages of change (SOC). Methods: This was a randomized clinical trial. Diet was evaluated using the validated Harvard DASH score calculated from Willett Food Frequency Questionnaires (range 8-40). The randomized groups were compared using the Wilcoxon rank-sum test, with adjustment for clustering by physician and baseline DASH scores. Results: At 6 months, compared to UC, TBI had a 1.28 point increase in DASH score (p ≤ .01) while NTI was not significant. At 6-month follow-up, TBI was more effective in advancing dietary SOC when compared to UC (56% vs 43%, p < .01) and NTI was not effective (46% vs 43%, p = .64). Conclusions: A phone-delivered tailored TTM-based intervention achieved greater improvement in DASH score and dietary SOC, suggesting that TTM-based tailored interventions can increase patients' dietary adherence.
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Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.
Berrouiguet, S, Barrigón, ML, Castroman, JL, Courtet, P, Artés-Rodríguez, A, Baca-García, E
BMC psychiatry. 2019;(1):277
Abstract
BACKGROUND The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone's native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. METHOD/DESIGN The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. DISCUSSION Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients' data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants' daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient. TRIAL REGISTRATION NUMBER NCT03720730. Retrospectively registered.
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Improving chronic pain management with eHealth and mHealth: study protocol for a randomised controlled trial.
Jaén, I, Suso-Ribera, C, Castilla, D, Zaragoza, I, García-Palacios, A, Gómez Palones, JL
BMJ open. 2019;(12):e033586
Abstract
INTRODUCTION Chronic pain has become a matter of public health concern due to its high prevalence and because public costs associated with treatment and disability increase each year. Research suggests that limitations in the traditional assessment of chronic pain patients limit the effectiveness of current medical treatments. The use of technology might serve change patient traditional monitoring into ecological momentary assessments, which might be visualised by physicians live. This study describes a randomised control trial designed to test the utility of a technology-based solution for pain telemonitoring consisting of a smartphone app for patients and a web application for physicians. The goal of this study will be to explore whether this combination of eHealth and mHealth improves the effectiveness of existing pain treatments. METHODS AND ANALYSIS Participants will be 250 patients randomly assigned to one of these two conditions: treatment-as-usual (TAU) and TAU +app+ web. All participants will receive the usual treatment for their pain. Only the TAU +app+ web group use Pain Monitor app, which generates alarms that are sent to the physicians in the face of previously established undesired events. Physicians will be able to monitor app reports using a web application, which might result in an adjustment of treatment. We anticipate that the use of Pain Monitor plus the therapist web will result in a reduction of pain intensity and side effects of the medication. Improvements on secondary outcomes, namely fatigue, mood, pain interference, rescue medication use and quality of life, are also expected. Mixed repeated-measure multivariate analyses of variances will be conducted to investigate whether there are differences between preassessment and postassessment scores as a function of the experimental condition. ETHICS AND DISSEMINATION Ethical approval from the Hospital General Universitari de Castellon was obtained. The findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT03606265.
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An Educational Intervention to Improve Statin Use: Cluster RCT at the Primary Care Level in Argentina.
Gulayin, PE, Lozada, A, Beratarrechea, A, Gutierrez, L, Poggio, R, Chaparro, RM, Santero, M, Masson, W, Rubinstein, A, Irazola, V
American journal of preventive medicine. 2019;(1):95-105
Abstract
INTRODUCTION Statins are essential drugs for high cardiovascular disease (CVD) risk management; however, there is still low adherence to good clinical practice guidelines for statin use at the primary care level in low- and middle-income countries. This study aimed to test whether a complex intervention targeting physicians improves treatment and control of hypercholesterolemia among patients with moderate to high CVD risk in Argentina. STUDY DESIGN Cluster RCT. SETTING/PARTICIPANTS Ten primary care centers from the public healthcare system of Argentina. INTERVENTION Primary care physicians in the intervention group received an educational program with three main components: (1) an intensive 2-day training workshop; (2) educational outreach visits; and (3) a mobile health application installed on the physician's smartphones. MAIN OUTCOME MEASURES Reduction in mean low-density lipoprotein cholesterol level, reduction in mean Framingham risk score, proportion of patients receiving an appropriate statin dose, and mean annual number of primary care center visits. RESULTS Data were analyzed in 2017-2018. Between April 2015 and April 2016, a total of 357 participants were enrolled (179 patients in the intervention group and 178 in the control group). The global follow-up rate was 97.2%. At the end of the follow-up period, there was no difference in low-density lipoprotein cholesterol levels in any of the follow-up points among the groups. Mean CVD risk had a significant net difference in the first 6 months in the intervention group versus the control group (-4.0, 95% CI = -6.5, -1.5). At the end of follow-up, there was an absolute 41.5% higher rate of participants receiving an appropriate statin dose in the intervention group versus the control group. CONCLUSIONS Although the intervention did not reach a reduction in cholesterol levels, it had a significant positive impact on the promotion of adequate use of clinical practice guidelines. TRIAL REGISTRATION This study is registered at www.clinicaltrials.gov NCT02380911.
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Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature.
Triantafyllidis, AK, Tsanas, A
Journal of medical Internet research. 2019;(4):e12286
Abstract
BACKGROUND Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. OBJECTIVE Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. METHODS We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). RESULTS Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. CONCLUSIONS This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.
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Adding Telephone and Text Support to an Obesity Management Program Improves Behavioral Adherence and Clinical Outcomes. A Randomized Controlled Crossover Trial.
Lewis, E, Huang, HC, Hassmén, P, Welvaert, M, Pumpa, KL
International journal of behavioral medicine. 2019;(6):580-590
Abstract
BACKGROUND Behavioral treatment strategies improve adherence to lifestyle intervention for adults with obesity, but can be time and resource intensive when delivered via traditional face-to-face care. This study aimed to investigate the efficacy and optimal timing of using telephone calls and text message as adjunctive tools to support a community-based obesity management program. METHOD This 8-month randomized controlled crossover trial recruited 61 adults with class III obesity (BMI > 40 kg/m2) enrolled in a publicly funded obesity management service (OMS). Participants were randomly assigned to receive telephone and text message support in addition to standard OMS care, or standard OMS care alone. After 4 months, participants crossed over to the alternative sequence. The technological support was based on self-determination theory. Outcome measures included diet, physical activity, anthropometry, self-efficacy, and treatment self-regulation. RESULTS Telephone and text message support improved lifestyle intervention adherence and clinical outcomes when compared with standard care. Participants who received the intervention in the first 4-month period lost 4.87 kg, compared with no weight loss (+ 0.38 kg) in the standard care only group. There was no evidence to indicate an optimal timing of the intervention, with both groups achieving significant results by the end of the intervention. CONCLUSION These results suggest a high degree of promise for the incorporation of telephone and text message support into community-based obesity management services. The findings have the potential to improve existing practices and reduce the burden on the health care system by demonstrating a resource-effective improvement to obesity management service delivery.
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Accuracy of Detection and Grading of Diabetic Retinopathy and Diabetic Macular Edema Using Teleretinal Screening.
Date, RC, Shen, KL, Shah, BM, Sigalos-Rivera, MA, Chu, YI, Weng, CY
Ophthalmology. Retina. 2019;(4):343-349
Abstract
PURPOSE To determine the accuracy of a county teleretinal screening program of detecting referable diabetic retinopathy (DR) and treatable diabetic macular edema (DME), as well as to evaluate patient compliance with clinic follow-up after referral from teleretinal screening. DESIGN Retrospective observational study. PARTICIPANTS Patients in the Harris Health System (HHS, Houston, TX) older than 18 years of age who underwent teleretinal screening between July 2014 and July 2016. METHODS Teleretinal imaging (TRI) consisting of single-field 45-degree nonmydriatic color fundus photography with referral thresholds of severe nonproliferative DR, proliferative DR, and significant DME. Teleretinal imaging results for all referred subjects were obtained and cross-referenced with dilated fundus examination findings with regard to DR severity and the presence of DME. Follow-up status was also noted. Subjects underwent OCT if deemed necessary by the examining specialist. Agreement between TRI and dilated fundus examination (DFE) findings was determined by calculating the Cohen κ coefficient. MAIN OUTCOME MEASURES The primary outcome measure is agreement between TRI results and DFE findings with regard to DR severity and the presence of DME. The secondary outcome measure is compliance with follow-up. RESULTS Of 1767 patients who were screened and referred for clinical examination, 935 (52.9%) attended their clinic appointment. Overall agreement between DFE and TRI was moderate (weighted κ 0.45) in terms of DR severity. There was agreement within one DR severity level in 86.2% of patients. The positive predictive value for detecting referable disease was 71.3%. Of patients referred for DME, 30.4% were deemed to have treatable DME. CONCLUSIONS The HHS teleretinal screening program demonstrates a high level of accuracy in the detection and classification of referable DR, but a lesser degree of accuracy in the detection of treatable DME. Only slightly more than half of participants were compliant with follow-up after a TRI referral. This large-scale study provides insight into the utility of teleretinal screening in a county health care system.