METHODS: A systematic search from the inception till May 31, 2021, in the MEDLINE, Embase, and PubMed databases was conducted, and 16 randomized controlled trials were included in the analysis.
RESULTS: The results showed significant benefits on glycosylated hemoglobin (HbA1c) (mean difference -0.24%; 95% confidence interval [CI]: -0.44, -0.05; p = 0.01), postprandial blood glucose (-2.91 mmol/L; 95% CI: -4.78, -1.03; p = 0.002), and triglycerides (-0.09 mmol/L; 95% CI: -0.17, -0.02; p = 0.010), but not on low-density lipoprotein cholesterol (-0.06 mmol/L; 95% CI: -0.14, 0.02; p = 0.170), high-density lipoprotein cholesterol (0.05 mmol/L; 95% CI: -0.03, 0.13; p = 0.220), and blood pressure (systolic blood pressure -0.82 mm Hg; 95% CI: -4.65, 3.00; p = 0.670; diastolic blood pressure -1.71 mmHg; 95% CI: -3.71, 0.29; p = 0.090).
CONCLUSIONS: Among older adults with T2DM, mHealth interventions were associated with improved cardiometabolic outcomes versus usual care. Its efficacy can be improved in the future as the current stage of mHealth development is at its infancy. Addressing barriers such as technological frustrations may help strategize approaches to further increase the uptake and efficacy of mHealth interventions among older adults with T2DM.
METHODS: Eight scientific databases were searched. Two independent reviewers screened the literature in title and abstract stages, followed by full-text appraisal, data extraction, and synthesis of eligible studies. Studies were extracted to capture details of the mhealth tools used, the service issues addressed, the study design, and the outcomes evaluated. We then mapped the included studies using the 20 sub-strategies of the WHO Framework on Integrated People-Centred Health Services (IPCHS); as well as with the RE-AIM (Reach, effectiveness, adoption, implementation and maintenance) framework, to understand how studies implemented and evaluated interventions.
RESULTS: We identified 39 studies, predominantly from Australia (n = 16), China (n = 7), Malaysia (n = 4) and New Zealand (n = 4), and little from low income countries. The mHealth modalities included text messaging, voice and video communication, mobile applications and devices (point-of-care, GPS, and Bluetooth). Health issues addressed included: medication adherence, smoking cessation, cardiovascular disease, heart failure, asthma, diabetes, and lifestyle activities respectively. Almost all were community-based and focused on service issues; only half were disease-specific. mHealth facilitated integrated IPCHS by: enabling citizens and communities to bypass gatekeepers and directly access services; increasing affordability and accessibility of services; strengthening governance over the access, use, safety and quality of clinical care; enabling scheduling and navigation of services; transitioning patients and caregivers between care sectors; and enabling the evaluation of safety and quality outcomes for systemic improvement. Evaluations of mHealth interventions did not always report the underlying theories. They predominantly reported cognitive/behavioural changes rather than patient outcomes. The utility of mHealth to support and improve IPCHS was evident. However, IPCHS strategy 2 (participatory governance and accountability) was addressed least frequently. Implementation was evaluated in regard to reach (n = 30), effectiveness (n = 24); adoption (n = 5), implementation (n = 9), and maintenance (n = 1).
CONCLUSIONS: mHealth can transition disease-centred services towards people-centred services. Critical appraisal of studies highlighted methodological issues, raising doubts about validity. The limited evidence for large-scale implementation and international variation in reporting of mHealth practice, modalities used, and health domains addressed requires capacity building. Information-enhanced implementation and evaluation of IPCHS, particularly for participatory governance and accountability, is also important.
MATERIALS AND METHODS: A literature search was performed in 10 databases from inception until February 2018. All economic evaluations assessing the economic evaluation of telemedicine in diabetes were eligible for inclusion. We subsequently evaluated the study quality in terms of effectiveness measures, cost measure, economic model, as well as time horizon.
RESULTS: Of the 1877 studies identified, 14 articles were included in our final review. The healthcare providers' fees are a major predictor for total cost. In particular, the use of telemedicine for retinal screening was beneficial and cost-effective for diabetes management, with an incremental cost-effectiveness ratio between $113.48/quality-adjusted life year (QALY) and $3,328.46/QALY (adjusted to 2017 inflation rate). Similarly, the use of telemonitoring and telephone reminders was cost-effective in diabetes management.
CONCLUSIONS: Among all telemedicine strategies examined, teleophthalmology was the most cost-effective intervention. Future research is needed to provide evidence on the long-term experience of telemedicine and facilitate resource allocation.
METHODS: We searched PubMed, EMBASE and the Cochrane Database of Systematic Reviews from database inception to 31 August 2018 for systematic reviews and/or meta-analyses of studies that examined the impact of distal technology and reported any clinical or patient-related outcomes among people with type 1 or type 2 diabetes.
RESULTS: The umbrella review identified 95 reviews, including 162 meta-analyses with 46 unique outcomes. Evidence from meta-analyses of randomized controlled studies supports the use of distal technology, especially telehealth and mHealth (healthcare delivered by mobile technology), in people with diabetes for improving HbA1c values by 2-4 mmol/mol (0.2-0.4%). For other health outcomes, such as changes in fasting plasma glucose levels, risk of diabetic ketoacidosis or frequency of severe hypoglycaemia, the evidence was weaker. No evidence was reported for most patient-reported outcomes including quality of life, self-efficacy and medication-taking. The evidence base was poor, with most studies rated as low to very low quality.
CONCLUSION: Distal technologies were associated with a modest improvement in glycaemic control, but it was unclear if they improved major clinical outcomes or were cost-effective in people with diabetes. More robust research to improve wider outcomes in people with diabetes is needed before such technologies can be recommended as part of routine care for any patient group.
DESIGN: In-depth and focus group interviews were conducted with participants who have engaged in telemedicine. Questions included were participants' perception on the programme being used, satisfaction as well as engagement with the telemedicine programme. All interviews and focus groups were audio-recorded and transcribed verbatim. Data were analysed using a thematic approach.
PARTICIPANTS AND SETTING: People with type 2 diabetes (n=48) who participated in a randomised controlled study which examined the use of telemedicine for diabetes management were recruited from 11 primary care clinics located within the Klang Valley.
RESULTS: Twelve focus groups and two in-depth interviews were conducted. Four themes emerged from the analysis: (1) generational difference; (2) independence and convenience, (3) sharing of health data and privacy and (4) concerns and challenges. The main obstacles found in patients using the telemedicine systems were related to internet connectivity and difficulties experienced with system interface. Cost was also another significant concern raised by participants. Participants in this study were primarily positive about the benefits of telemedicine, including its ability to provide real-time data and disease monitoring and the reduction in clinic visits.
CONCLUSION: Despite the potential benefits of telemedicine in the long-term care of diabetes, there are several perceived barriers that may limit the effectiveness of this technology. As such, collaboration between educators, healthcare providers, telecommunication service providers and patients are required to stimulate the adoption and the use of telemedicine.NCT0246680.
METHODS: We conducted a rapid scoping review following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched Medline, Embase and PsychInfo databases and Google Scholar using a search strategy developed in consultation with a biomedical librarian. We included records related to mental health or psychosocial risk factors and COVID-19 among at-risk groups; that referred to one or more APEC member economies or had a global, thus generalizable, scope; English language papers, and papers with full text available.
RESULTS: A total of 132 records published between December 2019 and August 2020 were included in the final analysis. Several priority at-risk populations, risk factors, challenges and recommendations for standard and e-mental health care were identified. Results demonstrate that e-mental health care can be a viable option for care delivery but that specific accessibility and acceptability considerations must be considered. Options for in-person, hybrid or "low-tech" care must also remain available.
CONCLUSIONS: The COVID-19 pandemic has highlighted the urgent need for equitable standard and e-mental health care. It has also highlighted the persistent social and structural inequities that contribute to poor mental health. The APEC region is vast and diverse; findings from the region can guide policy and practice in the delivery of equitable mental health care in the region and beyond.
OBJECTIVE: Our objective was to create a framework that can guide future implementation and research on the use of eHealth tools to support patients with growth disorders who require growth hormone therapy.
METHODS: A total of 12 pediatric endocrinologists with experience in eHealth, from a wide geographical distribution, participated in a series of online discussions. We summarized the discussions of 3 workshops, conducted during 2020, on the use of eHealth in the management of growth disorders, which were structured to provide insights on existing challenges, opportunities, and solutions for the implementation of eHealth tools across the patient journey, from referral to the end of pediatric therapy.
RESULTS: A total of 815 responses were collected from 2 questionnaire-based activities covering referral and diagnosis of growth disorders, and subsequent growth hormone therapy stages of the patient pathway, relating to physicians, nurses, and patients, parents, or caregivers. We mapped the feedback from those discussions into a framework that we developed as a guide to integration of eHealth tools across the patient journey. Responses focused on improved clinical management, such as growth monitoring and automation of referral for early detection of growth disorders, which could trigger rapid evaluation and diagnosis. Patient support included the use of eHealth for enhanced patient and caregiver communication, better access to educational opportunities, and enhanced medical and psychological support during growth hormone therapy management. Given the potential availability of patient data from connected devices, artificial intelligence can be used to predict adherence and personalize patient support. Providing evidence to demonstrate the value and utility of eHealth tools will ensure that these tools are widely accepted, trusted, and used in clinical practice, but implementation issues (eg, adaptation to specific clinical settings) must be addressed.
CONCLUSIONS: The use of eHealth in growth hormone therapy has major potential to improve the management of growth disorders along the patient journey. Combining objective clinical information and patient adherence data is vital in supporting decision-making and the development of new eHealth tools. Involvement of clinicians and patients in the process of integrating such technologies into clinical practice is essential for implementation and developing evidence that eHealth tools can provide value across the patient pathway.
METHODS: A cross-sectional survey was conducted among caregivers and patients attending geriatric outpatient services in Kuala Lumpur, Malaysia. The survey measured the availability of equipment for virtual consultations, prior knowledge and experience of telemedicine, and willingness to consult geriatricians through virtual technology, using the Unified Theory of Acceptance and Use of Technology (UTAUT) scale.
RESULTS: A total of 197 caregivers and 42 older patients with a mean age of 54.28 (±13.22) and 75.62 (±7.32) years, respectively, completed the survey. One hundred and fifty-six (79.2%) of the caregivers were adult children accompanying patients. The mean UTAUT score was 65.97 (±13.71) out of 90, with 66.64 (±13.25) for caregivers and 62.79 (±15.44) for older adults, suggesting a high acceptance of adopting virtual consultations in lieu of face-to-face care. The independent predictors of acceptance of virtual consultation were : possession of an electronic device capable of video-communication, living with someone, living in a care home, weekly online banking usage, and perceived familiarity with virtual platforms.
CONCLUSION: Caregivers and patients indicated a high level of acceptance of virtual medical consultations, which is likely facilitated by caregivers such as adult children or spouses at home or staff in care homes. To minimize the transmission of COVID-19 in a highly vulnerable group, virtual consultations are an acceptable alternative to face-to-face consultations for older people and their caregivers in our setting.
AIMS: This study aimed to assess the readiness and ability to use mHealth apps among patients with CKD in north-east Peninsular Malaysia.
METHODS: A cross-sectional study was undertaken, using a convenience sample of 100 CKD medical inpatients in a tertiary teaching hospital. A structured, self-administered questionnaire on readiness and ability to use mHealth apps was adopted.
FINDINGS: Nearly one in five patients (18%) actively used health applications. More than three-quarters (77%) were aged >40 years and a similar proportion were ready to use mHealth apps (78%), and nearly half (46%) were confident about connecting their device to wifi. There was a correlation between ability and readiness to use mHealth apps (r=0.4; P<0.05).
CONCLUSIONS: Fewer than half of participants had a good command of mHealth applications. Therefore, support on the use of these apps is needed, and healthcare managers need to consider this.