OBJECTIVE: To test whether adding mobile application coaching and patient/provider web portals to community primary care compared with standard diabetes management would reduce glycated hemoglobin ...levels in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: A cluster-randomized clinical trial, the Mobile Diabetes Intervention Study, randomly assigned 26 primary care practices to one of three stepped treatment groups or a control group (usual care). A total of 163 patients were enrolled and included in analysis. The primary outcome was change in glycated hemoglobin levels over a 1-year treatment period. Secondary outcomes were changes in patient-reported diabetes symptoms, diabetes distress, depression, and other clinical (blood pressure) and laboratory (lipid) values. Maximal treatment was a mobile- and web-based self-management patient coaching system and provider decision support. Patients received automated, real-time educational and behavioral messaging in response to individually analyzed blood glucose values, diabetes medications, and lifestyle behaviors communicated by mobile phone. Providers received quarterly reports summarizing patient’s glycemic control, diabetes medication management, lifestyle behaviors, and evidence-based treatment options. RESULTS: The mean declines in glycated hemoglobin were 1.9% in the maximal treatment group and 0.7% in the usual care group, a difference of 1.2% (P < 0.001) over 12 months. Appreciable differences were not observed between groups for patient-reported diabetes distress, depression, diabetes symptoms, or blood pressure and lipid levels (all P > 0.05). CONCLUSIONS: The combination of behavioral mobile coaching with blood glucose data, lifestyle behaviors, and patient self-management data individually analyzed and presented with evidence-based guidelines to providers substantially reduced glycated hemoglobin levels over 1 year.
Evaluation of digital health applications to support older adults' independence and family caregiving is needed. Digital health is increasingly providing opportunities for older adults and their ...family caregivers to educate, engage, and share health information across digital platforms. Few apps have documented evidence of usability by older adults and their caregivers.
The objective of this study was to determine the usability of a mobile app in a community-based older adult population aged ≥65 years. The app was designed to improve engagement of the patient-informal caregiver team.
This observational usability study was conducted in participants' homes and independent living facilities in Baltimore, Maryland. Community-dwelling older adults aged ≥65 years and their caregivers enrolled as a dyad (n=24, 12 dyads). The usability evaluation was a mobile and Web-based app that allowed older adult users to record social and health information and share this information with their caregivers. The older adult-caregiver dyad downloaded the app to a smart phone or accessed the Web version, participated in training and onboarding, and used the app for a 1-month period. Participants responded to weekly surveys sent by app push notifications and to the usability and satisfaction surveys at the end of the study. Participant satisfaction and usability were assessed using the Modified Mobile Application Rating Scale (M-MARS) and the System Usability Scale (SUS).
The final sample comprised 16 people (8 dyads). Responses to the M-MARS were comparable between older adults and caregiver respondents in terms of engagement and functionality. Caregivers rated aesthetics slightly higher (mean 3.7) than older adult participants did (mean 3.3). Although most responses to the SUS were around the mean (2.3-3.4), older adults and their caregivers differed with regard to integration of app features (mean 3.7 vs 2.8) and the need to learn more before using the app (mean 2.3 vs 3.1).
Technology ownership and use among older adults and caregivers was high. Usability and engagement of the mobile app was average. Additional training is recommended for older adults and their caregivers, including that on targeted behaviors for digital health record keeping.
As people age and require more assistance with daily living and health needs, a range of housing and care options is available. Over the past four decades the market for seniors housing and ...care-including assisted living and independent living communities-has greatly expanded to accommodate people with more complex needs. These settings provide housing in a community environment that often includes personal care assistance services. Unfortunately, these settings are often out of the financial reach of many of this country's eight million middle-income seniors (those ages seventy-five and older). The private seniors housing industry has generally focused on higher-income people instead. We project that by 2029 there will be 14.4 million middle-income seniors, 60 percent of whom will have mobility limitations and 20 percent of whom will have high health care and functional needs. While many of these seniors will likely need the level of care provided in seniors housing, we project that 54 percent of seniors will not have sufficient financial resources to pay for it. This gap suggests a role for public policy and the private sector in meeting future long-term care and housing needs for middle-income seniors.
Less than 63% of individuals with diabetes meet professional guidelines target of hemoglobin A1c <7.0%, and only 7% meet combined glycemic, lipid, and blood pressure goals. The primary study aim was ...to assess the impact on A1c of a cell phone-based diabetes management software system used with web-based data analytics and therapy optimization tools. Secondary aims examined health care provider (HCP) adherence to prescribing guidelines and assessed HCPs' adoption of the technology.
Thirty patients with type 2 diabetes were recruited from three community physician practices for a 3-month study and evenly randomized. The intervention group received cell phone-based software designed by endocrinologists and CDEs (WellDoc Communications, Inc., Baltimore, MD). The software provided real-time feedback on patients' blood glucose levels, displayed patients' medication regimens, incorporated hypo- and hyperglycemia treatment algorithms, and requested additional data needed to evaluate diabetes management. Patient data captured and transferred to secure servers were analyzed by proprietary statistical algorithms. The system sent computer-generated logbooks (with suggested treatment plans) to intervention patients' HCPs.
The average decrease in A1c for intervention patients was 2.03%, compared to 0.68% (P < 0.02, one-tailed) for control patients. Of the intervention patients, 84% had medications titrated or changed by their HCP compared to controls (23%, P = 0.002). Intervention patients' HCPs reported the system facilitated treatment decisions, provided organized data, and reduced logbook review time.
Adults with type 2 diabetes using WellDoc's software achieved statistically significant improvements in A1c. HCP and patient satisfaction with the system was clinically and statistically significant.
Type 2 diabetes mellitus is a worldwide challenge. Practice guidelines promote structured self-monitoring of blood glucose (SMBG) for informing health care providers about glycemic control and ...providing patient feedback to increase knowledge, self-efficacy, and behavior change. Paired glucose testing—pairs of glucose results obtained before and after a meal or physical activity—is a method of structured SMBG. However, frequent access to glucose data to interpret values and recommend actions is challenging. A complete feedback loop—data collection and interpretation combined with feedback to modify treatment—has been associated with improved outcomes, yet there remains limited integration of SMBG feedback in diabetes management. Incorporating telehealth remote monitoring and asynchronous electronic health record (EHR) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management—employ the complete feedback loop to improve outcomes.
The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes. The primary aim was change in glycated hemoglobin (A(1c))—a measure of overall glucose management—between groups after 6 months. The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Empowerment Scale, and Diabetes Knowledge Test.
A 2-group randomized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits. Participants were aged 30-70 years, not using insulin with A1c levels between 7.5% and 10.9% (58-96 mmol/mol). The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants, analyze actionable glucose data, and provide feedback. Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly. CDEs called participants monthly to discuss paired glucose testing trends and treatment changes. Separate mixed-effects models were used to analyze data.
Participants (N=90) were primarily white (64%, 56/87), mean age 58 (SD 11) years, mean body mass index 34.1 (SD 6.7) kg/m2, with diabetes for mean 8.2 (SD 5.4) years, and a mean A(1c) of 8.3% (SD 1.1; 67 mmol/mol). Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005). Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level. The treatment group significantly improved on the SDSCA subscales carbohydrate spacing (P=.04), monitoring glucose (P=.001), and foot care (P=.02).
An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A(1c) levels compared to usual care.
Clinicaltrials.gov NCT01715649; https://www.clinicaltrials.gov/ct2/show/NCT01715649 (Archived by WebCite at http://www.webcitation.org/6ZinLl8D0).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Objectives: To compare rates of falling between nursing home residents with and without dementia and to examine dementia as an independent risk factor for falls and fall injuries.
Design: Prospective ...cohort study with 2 years of follow‐up.
Setting: Fifty‐nine randomly selected nursing homes in Maryland, stratified by geographic region and facility size.
Participants: Two thousand fifteen newly admitted residents aged 65 and older.
Measurements: During 2 years after nursing home admission, fall data were collected from nursing home charts and hospital discharge summaries.
Results: The unadjusted fall rate for residents in the nursing home with dementia was 4.05 per year, compared with 2.33 falls per year for residents without dementia (P<.0001). The effect of dementia on the rate of falling persisted when known risk factors were taken into account. Among fall events, those occurring to residents with dementia were no more likely to result in injury than falls of residents without dementia, but, given the markedly higher rates of falling by residents with dementia, their rate of injurious falls was higher than for residents without dementia.
Conclusion: Dementia is an independent risk factor for falling. Although most falls do not result in injury, the fact that residents with dementia fall more often than their counterparts without dementia leaves them with a higher overall risk of sustaining injurious falls over time. Nursing home residents with dementia should be considered important candidates for fall‐prevention and fall‐injury‐prevention strategies.
Telehealth Remote Monitoring Systematic Review Greenwood, Deborah A.; Young, Heather M.; Quinn, Charlene C.
Journal of Diabetes Science and Technology,
03/2014, Letnik:
8, Številka:
2
Book Review, Journal Article
Recenzirano
Aims:
The aim was to summarize research on telehealth remote patient monitoring interventions that incorporate key elements of structured self-monitoring of blood glucose (SMBG) identified as ...essential for improving A1C.
Methods:
A systematic review was conducted using the Medline, Cumulative Index to Nursing and Allied Health Literature, EMBASE, and OVID Medline databases with search terms “Telemedicine” AND “Monitoring, Physiologic” AND “Diabetes Mellitus, Type 2.” Study selection criteria included original randomized clinical trials evaluating the impact of telehealth remote patient monitoring on A1C among adults with type 2 diabetes and incorporated 1 or more essential elements of SMBG identified by the International Diabetes Federation (patient education, provider education, structured SMBG profile, SMBG goals, feedback, data used to modify treatment, interactive communication or shared decision making).
Results:
Fifteen studies were included, with interventions ranging from 3 to 12 months (mean 8 months) with sample sizes from 30 to 1665. Key SMBG elements were grouped into 3 categories: education, SMBG protocols, and feedback. Research incorporating 5 of the 7 elements consistently achieved significant A1C improvements between study groups. Interventions using more SMBG elements are associated with an improvement in A1C. Studies with the largest A1C decrease incorporated 6 of the 7 elements and computer decision support. Two studies with 5 of the 7 elements and active medication management achieved significant A1C decreases.
Conclusion:
Telehealth remote patient monitoring interventions in type 2 diabetes have not included all structured monitoring elements recommended by the IDF. Incorporating more elements of structured SMBG is associated with improved A1C.
The purpose of this study was to evaluate participant self-efficacy and use of a mobile phone diabetes health intervention for older adults during a 4-week period. Participants included seven adults ...(mean age, 70.3 years) with type 2 diabetes cared for by community-based primary care physicians. Participants entered blood glucose data into a mobile phone and personalized patient Internet Web portal. Based on blood glucose values, participants received automatic messages and educational information to self-manage their diabetes. Study measures included prior mobile phone/Internet use, the Stanford Self-Efficacy for Diabetes Scale, the Stanford Energy/Fatigue Scale, the Short Form-36, the Patient Health Questionnaire-9 (depression), the Patient Reported Diabetes Symptom Scale, the Diabetes Stages of Change measure, and a summary of mobile system use. Participants had high self-efficacy and high readiness and confidence in their ability to monitor changes to control their diabetes. Participants demonstrated ability to use the mobile intervention and communicate with diabetes educators.
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