scholarly journals Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study

10.2196/22212 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e22212
Author(s):  
Anna-Katharina Böhm ◽  
Morten Lind Jensen ◽  
Mads Reinholdt Sørensen ◽  
Tom Stargardt
Keyword(s):  
Mobile Health ◽  
Real World ◽  
Diabetes Management ◽  
Patient Characteristics ◽  
Active Role ◽  
Self Management ◽  
User Engagement ◽  
User Characteristics ◽  
Real World Data ◽  
Average User

Background Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). Objective This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. Methods The analysis is based on real-world data obtained by Novo Nordisk’s Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. Results A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. Conclusions Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users’ engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.

scholarly journals Real-World Evidence of User Engagement With Mobile Health for Diabetes Management: Longitudinal Observational Study (Preprint)

2020 ◽  
Author(s):  
Anna-Katharina Böhm ◽  
Morten Lind Jensen ◽  
Mads Reinholdt Sørensen ◽  
Tom Stargardt
Keyword(s):  
Mobile Health ◽  
Real World ◽  
Diabetes Management ◽  
Patient Characteristics ◽  
Active Role ◽  
Self Management ◽  
User Engagement ◽  
User Characteristics ◽  
Real World Data ◽  
Average User

BACKGROUND Patient support apps have risen in popularity and provide novel opportunities for self-management of diabetes. Such apps offer patients to play an active role in monitoring their condition, thereby increasing their own treatment responsibility. Although many health apps require active user engagement to be effective, there is little evidence exploring engagement with mobile health (mHealth). OBJECTIVE This study aims to analyze the extent to which users engage with mHealth for diabetes and identify patient characteristics that are associated with engagement. METHODS The analysis is based on real-world data obtained by Novo Nordisk’s Cornerstones4Care Powered by Glooko diabetes support app. User engagement was assessed as the number of active days and using measures expressing the persistence, longevity, and regularity of interaction within the first 180 days of use. Beta regressions were estimated to assess the associations between user characteristics and engagement outcomes for each module of the app. RESULTS A total of 9051 individuals initiated use after registration and could be observed for 180 days. Among these, 55.39% (5013/9051) used the app for one specific purpose. The average user activity ratio varied from 0.05 (medication and food) to 0.55 (continuous glucose monitoring), depending on the module of the app. Average user engagement was lower if modules required manual data entries, although the initial uptake was higher for these modules. Regression analyses further revealed that although more women used the app (2075/3649, 56.86%), they engaged significantly less with it. Older people and users who were recently diagnosed tended to use the app more actively. CONCLUSIONS Strategies to increase or sustain the use of apps and availability of health data may target the mode of data collection and content design and should take into account privacy concerns of the users at the same time. Users’ engagement was determined by various user characteristics, indicating that particular patient groups should be targeted or assisted when integrating apps into the self-management of their disease.

Impact of pharmacist-involved collaborative care on diabetes management in a primary healthcare setting using real-world data

2021 ◽  
Author(s):  
Sara Abdulrhim ◽  
Ahmed Awaisu ◽  
Mohamed Izham Mohamed Ibrahim ◽  
Mohammad Issam Diab ◽  
Mohamed Abdelazim Mohamed Hussain ◽  
...  
Keyword(s):  
Real World ◽  
Primary Healthcare ◽  
Collaborative Care ◽  
Diabetes Management ◽  
Healthcare Setting ◽  
Real World Data ◽  
World Data

P0816CLINICAL CHARACTERISTICS AND EGFR AND UACR DISTRIBUTION ACCORDING TO THE 2012 KDIGO CKD CLASSIFICATION: A REPORT FROM THE US DISCOVER CKD COHORT

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Juan Jose Garcia Sanchez ◽  
Juan Jesus Carrero ◽  
Supriya Kumar ◽  
Roberto Pecoits-Filho ◽  
Glen James ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Real World ◽  
Patient Characteristics ◽  
Study Cohort ◽  
Diagnostic Code ◽  
Inclusion Criteria ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
World Data ◽  
Co Morbidity

Abstract Background and Aims In 2012, the Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommended categorising and prognosticating chronic kidney disease (CKD) based on estimated glomerular filtration rate (eGFR) and urine albumin to creatinine ratio (UACR). Contemporary studies describing the prevalence and characteristics of patients with CKD categorised according KDIGO 2012 and how studies of new pharmacotherapies relate to these categories are scarce. One such new therapy class of key interest are the sodium glucose co-transporter 2 inhibitors (SGLT-2i), shown to delay the progression to renal failure and prevent cardiovascular/renal death in patients with CKD. We aimed to describe patient characteristics and the prevalence of CKD according to the 2012 KDIGO categories in a large real-world US cohort of patients with CKD (part A). We also describe a subset of the population according to the DAPA-CKD trial inclusion criteria (eGFR [25-75ml/min/1.73m2] and UACR [200-5000mg/g]) (part B). Method DISCOVER-CKD is an international observational study in patients with CKD. The DISCOVER-CKD retrospective US cohort of patients was extracted using real-world data from the integrated Limited Claims and Electronic Health Record data (IBM Health, Armonk, NY) and HealthVerity. Patients were aged ≥18 years, with ≥1 UACR measure. For part A, required first diagnostic code of CKD (Stages 3A, 3B, 4, 5, or ESRD) or two eGFR of <75 mL/min/1.73 m2 recorded at least 90 days apart and for part B, two measures of eGFR 25-75 mL/min/1.73 m2 recorded at least 90 days apart between 1st January 2008 and September 2018. Index date was diagnostic code or 2nd eGFR. The first UACR, recorded +/-12 months of index, was used to categorise patients. Descriptive analyses were used to summarise prevalence and patient characteristics. Results Of the overall study cohort (N=4330, 49.1% women, mean age 65.3±10.64 years), by KDIGO categories (part A): 85.7% (n=3601) had normal to mildly increased albuminuria, 11.0% (n=463) had moderately increased albuminuria and 3.3% (n=137) had severely increased albuminuria (Figure 1). 4.6% (n=193) fulfilled DAPA-CKD trial inclusion criteria (part B). In both populations, the most common comorbidities were hypertension (HTN, 73.0% for both) and type 2 diabetes (T2D, 57.6% and 56.2%, respectively). Anti-hypertensive drugs were frequently used (76.4% and 76.9%, respectively). Conclusion This study, utilising real-world data, adds to the scarcity of knowledge reporting the characteristics of patients with CKD in different eGFR and UACR strata according to the KDIGO 2012 definitions. We observed a trend in higher UACR in the group of patients with lower eGFR and report a high prevalence of T2D and HTN in the study population, demonstrating the high co-morbidity burden in patients, for whom new therapies may be beneficial.

550 Real World Treatment Trends for Patients TBSA ≤ 20%: Evaluating Important Shifts in Care and Budget Impact of ASCS Adoption

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S115-S115
Author(s):  
James H Holmes ◽  
Stacey Kowal ◽  
Cheryl P Ferrufino
Keyword(s):  
Survey Data ◽  
Resource Use ◽  
Real World ◽  
Budget Impact ◽  
Patient Characteristics ◽  
Real World Data ◽  
Treatment Pathways ◽  
World Data ◽  
Burn Care ◽  
The Impact

Abstract Introduction Treatment pathways in burn care are typically determined based on burn center (BC) and patient characteristics, although decisions may be influenced by anecdotal experience, personal preference, and hospital policies/purchasing decisions. Health economic (HE) evaluations can support improved decision-making, identifying the most cost-effective interventions for tailored care. A novel burn care model (BEACON) was developed with burn surgeons over several years and validated through numerous publications, including an assessment of the HE impacts of autologous skin cell suspension (ASCS) use for definitive burn closure. To ensure that BEACON accurately represents the current state of care, it is vital to update data that underpins model projections. This study collected real world data on practice patterns and patient outcomes for the most commonly seen burns (TBSA ≤ 20%) to update the current understanding of standard of care (SOC) costs and outcomes and to refine estimates on the impact of ASCS use in TBSA ≤ 20% patients. Methods Data was collected from a 10% sample of BCs, including: BC and patient characteristics, resource use, inpatient costs, and length of stay (LOS). NBR based inputs in BEACON were updated to reflect survey data for patients with TBSA ≤ 20%, with the ability to view data as a national aggregate sample and across BC characteristics. BEACON estimates patient and BC costs and outcomes across a spectrum of patient profiles (age, gender, inhalation injury, comorbidity status, burn depth, TBSA) and combines information on each patient profile to understand annual budget impact. Key outcomes were compared across the survey sample and published NBR trends. Using the updated BEACON, the BC budget impact of ASCS in burns TBSA ≤ 20% was assessed. Results The survey was collected from 16+ BCs, focusing on inpatient encounters in 2018. LOS was lower than NBR estimates, with some centers reporting LOS per %TBSA far below 1 d/%TBSA. Using the detailed bottom-up estimation of cost from BEACON with survey data, trends suggest total hospital costs for SOC are lower than published NBR charges given shorter LOS and updated cost and resource use assumption. Conclusions Compared to NBR 8.0, contemporary data suggests that fewer small TBSA burns are being treated in the inpatient setting; those treated have a LOS below NBR estimates. When using real world data, the impact of ASCS use in burns TBSA ≤ 20% was still calculated to be cost saving to a BC overall, given reductions in LOS and number of definitive closure procedures. Incorporating ASCS into appropriate TBSA ≤ 20% procedures can still result in a positive financial impact for BCs. Applicability of Research to Practice

scholarly journals Three handy tips and a practical guide to improve your propensity score models

RMD Open ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e000953 ◽  
Author(s):  
Sytske Anne Bergstra ◽  
Alexandre Sepriano ◽  
Sofia Ramiro ◽  
Robert Landewé
Keyword(s):  
Real World ◽  
Observational Studies ◽  
Propensity Scores ◽  
Patient Characteristics ◽  
Confounding By Indication ◽  
Real World Data ◽  
Treatment Allocation ◽  
Popular Method ◽  
Clinical Scenarios ◽  
Treatment Responses

Real-world data are increasingly available to investigate ‘real-world’ safety and efficacy. However, since treatment in observational studies is not randomly allocated, confounding by indication may occur, in which differences in patient characteristics may influence both treatment choices and treatment responses. A popular method to adjust for this type of bias is the use of propensity scores (PS). The PS is a score between 0 and 1 that reflects the likelihood per patient of receiving one of the treatment categories of interest conditional on a set of variables. At least in theory, in patients with similar PS, the treatment prescribed will be independent of these variables (pseudorandomisation). But researchers using PS sometimes fail to recognise important methodological flaws which can lead to spurious conclusions. These include perfect prediction of treatment allocation, untied observations and lack of generalisability due to oversimplification of complex clinical scenarios. In this viewpoint we will discuss the most commonly encountered flaws and provide a stepwise description on the estimation and use of PS, such that in future publications these flaws can be avoided.

scholarly journals App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison (Preprint)

2018 ◽  
Author(s):  
Antonio Martinez-Millana ◽  
Elena Jarones ◽  
Carlos Fernandez-Llatas ◽  
Gunnar Hartvigsen ◽  
Vicente Traver
Keyword(s):  
Type 1 Diabetes ◽  
Disease Management ◽  
Mobile Health ◽  
Diabetes Management ◽  
Patient Empowerment ◽  
Self Management ◽  
Inclusion Criteria ◽  
Data Record ◽  
Level 1

BACKGROUND Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored. OBJECTIVE To study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes. METHODS The methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market. RESULTS The literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (P=.42, F=.81) nor the type of feature (P=.20, χ2=11.7). Apps were classified according to the number of level 1 features and sorted into quartiles. First quartile apps had a regular distribution of the ten features in the taxonomy whereas the other 3 quartiles had an irregular distribution. CONCLUSIONS There are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.

scholarly journals A Novel User Utility Score for Diabetes Management Using Tailored Mobile Coaching: Secondary Analysis of a Randomized Controlled Trial

10.2196/17573 ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. e17573
Author(s):  
Min-Kyung Lee ◽  
Da Young Lee ◽  
Hong-Yup Ahn ◽  
Cheol-Young Park
Keyword(s):  
Randomized Controlled Trial ◽  
Glycemic Control ◽  
Mobile Health ◽  
Patient Engagement ◽  
Utility Score ◽  
Diabetes Management ◽  
Controlled Trial ◽  
Self Management ◽  
Glucose Testing ◽  
Randomized Controlled

Background Mobile health applications have been developed to support diabetes self-management, but their effectiveness could depend on patient engagement. Therefore, patient engagement must be examined through multifactorial tailored behavioral interventions from an individual perspective. Objective This study aims to evaluate the usefulness of a novel user utility score (UUS) as a tool to measure patient engagement by using a mobile health application for diabetes management. Methods We conducted a subanalysis of results from a 12-month randomized controlled trial of a tailored mobile coaching (TMC) system among insurance policyholders with type 2 diabetes. UUS was calculated as the sum of the scores for 4 major core components (range 0-8): frequency of self-monitoring blood glucose testing, dietary and exercise records, and message reading rate. We explored the association between UUS for the first 3 months and glycemic control over 12 months. In addition, we investigated the relationship of UUS with blood pressure, lipid profile, and self-report scales assessing diabetes self-management. Results We divided 72 participants into 2 groups based on UUS for the first 3 months: UUS:0-4 (n=38) and UUS:5-8 (n=34). There was a significant between-group difference in glycated hemoglobin test (HbA1c) levels for the 12-months study period (P=.011). The HbA1c decrement at 12 months in the UUS:5-8 group was greater than that of the UUS:0-4 group [–0.92 (SD 1.24%) vs –0.33 (SD 0.80%); P=.049]. After adjusting for confounding factors, UUS was significantly associated with changes in HbA1c at 3, 6, and 12 months; the regression coefficients were –0.113 (SD 0.040; P=.006), –0.143 (SD 0.045; P=.002), and –0.136 (SD 0.052; P=.011), respectively. Change differences in other health outcomes between the 2 groups were not observed throughout a 12-month follow-up. Conclusions UUS as a measure of patient engagement was associated with changes in HbA1c over the study period of the TMC system and could be used to predict improved glycemic control in diabetes self-management through mobile health interventions. Trial Registration ClinicalTrial.gov NCT03033407; https://clinicaltrials.gov/ct2/show/NCT03033407

scholarly journals Mobile apps for real-world evidence in health care

2020 ◽  
Vol 27 (6) ◽  
pp. 976-980
Author(s):  
Madison Milne-Ives ◽  
Michelle Helena van Velthoven ◽  
Edward Meinert
Keyword(s):  
Health Care ◽  
Mobile Health ◽  
Real World ◽  
Patient Empowerment ◽  
Real World Data ◽  
Privacy And Security ◽  
World Data ◽  
Health Apps ◽  
Real World Evidence ◽  
Mobile Health Apps

Abstract The use of real-world evidence for health care research and evaluation is growing. Mobile health apps have often-overlooked potential to contribute valuable real-world data that are not captured by other sources and could provide data that are more cost-effective and generalizable than can randomized controlled trials. However, there are several challenges that must be overcome to realize the potential value of patient-used mobile health app real-world data, including data quality, motivation for long-term use, privacy and security, methods of analysis, and standardization and integration. Addressing these challenges will increase the value of data from mobile health apps to inform real-world evidence and improve patient empowerment, clinical management, disease research, and treatment development.

What are the challenges for building real world comparative cohorts in oncology?

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19285-e19285
Author(s):  
Nikita Jeswani ◽  
Amanda McDonell ◽  
Finlay MacDougall ◽  
Jeff Paul Hodge
Keyword(s):  
Language Processing ◽  
Real World ◽  
International Prognostic Index ◽  
Evaluation Criteria ◽  
Prognostic Index ◽  
Cost Effective ◽  
Patient Characteristics ◽  
Outcome Evaluation ◽  
Real World Data ◽  
Indolent Lymphomas

e19285 Background: There has been a proliferation of single arm trials in oncology being presented to regulators. An external comparator using real world data (RWD) can help establish context to trial results and direct comparison to the treatment arm by mirroring the inclusion/exclusion criteria for the trial and examining trial outcomes in an RWD external comparator cohort. RWD has successfully supported some regulatory submissions, although there have been failures as well, with comparability of patients and endpoints under scrutiny. Methods: IQVIA has partnered with life sciences companies on > 20 external comparator projects from 2017-present using a mix of data acquisition strategies (e.g., chart review, database extraction) and methods (e.g., real-world cohort vs. real-world benchmark, propensity matching vs. inverse probability treatment weighting). We collated, reviewed, and synthesised the challenges and risk mitigations documented throughout study execution in aggressive and indolent lymphomas, multiple myeloma, Merkel cell carcinoma, and synovial sarcoma. Results: Patient characteristics used to assess trial eligibility (e.g., biomarker expression, International Prognostic Index, ECOG status) and outcomes (e.g., response rate) are not often captured in a structured format in RWD or recorded in routine clinical practice. As a result, there is a greater reliance on chart reviews today over database extractions to fulfil external comparator requirements, though in some diseases, databases have proven to be a faster and more cost-effective solution. Conclusions: To improve the success of external comparator studies, trial design should be informed by existing RWD so that relevant real-world endpoints and outcome evaluation criteria are included alongside RCT standards. Advances are needed to facilitate RWD capture that matches trial data more closely. This could include bespoke data collaborations, biobank linkage, and natural language processing to drive study execution in databases in the future.

645-P: Mobile Health Application as a Real-World Data Resource: Self-Recorded Weight Reduction with Once-Weekly Semaglutide

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 645-P
Author(s):  
ULRIK BODHOLDT ◽  
SOPHIE BIROT ◽  
ANDREI-MIRCEA CATARIG ◽  
UMUT ERHAN ◽  
FILIP K. KNOP
Keyword(s):  
Mobile Health ◽  
Real World ◽  
Weight Reduction ◽  
Real World Data ◽  
Data Resource ◽  
World Data ◽  
Mobile Health Application ◽  
Health Application
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