BackgroundFew studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.ObjectiveThe aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).MethodsThis was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.ResultsThe participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).ConclusionsFitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.
Objectives: We aimed to evaluate QoL and Ut in DM2 patients receiving vildagliptin or sulphonylureas (SU) add-on to metformin therapy in a real-world setting. MethOds: In the whole, 229 DM2 patients (mean age -61.3±9.5 years, 68.1% females) receiving vildagliptin add-on to metformin/GalvusMet (Group 1, n= 104) or SU add-on to metformin (Group 2, n= 125) were enrolled in the real-world multicenter observational study. Mean disease duration was 5.9±4.1 years, mean HbA1с level -6.9%±0.8%, late complications were revealed in 27.5% of patients, comorbidities -in 64.6%. All the patients completed SF-36 questionnaire. The Ut scores were calculated for each patient on the basis of SF-6D questionnaire. The SF-6D utility indexes were obtained from SF-36 data. The mean Ut for each treatment group was calculated with adjustment for gender, age, comorbidities, late complications and HbA1с level. Group comparisons were made using General linear models with adjustment for age, comorbidities and late complications. Results: It was shown that patients in Group 1 had higher QoL by the majority of SF-36 scales: role physical functioning -74.3 vs 50.3; bodily pain -82.9 vs 69.1; general health -56.5 vs 47.7; vitality -64.4 vs 56.0; social functioning -83.7 vs 72.5; role emotional functioning -86.1 vs 58.4 (p< 0.05). Integral QoL Index in Group 1 was higher -0.525 vs 0.351 (p< 0.001). The significant difference between Ut values for two groups was revealed: the mean Ut for Group 1 was higher as compared to mean Ut for Group 2 -0.757 vs 0.701 (p< 0.05). cOnclusiOns: Results of this real-world study demonstrate benefits of treatment with vildagliptin add-on to metformin/GalvusMet in DM2 patients as compared to SU add-on to metformin in terms of better QoL and higher health utility. The data obtained may be used for cost-utility analysis and be further applied for health technology assessment. PDB76 eu5 Patient's willingness to tRy new tecHniQues foR Managing tHeiR DiaBetes MellitusObjectives: Diabetes Mellitus is a large and growing condition in the EU5, prevalence ranges from 4.8% in Italy to 8.9% in Germany. The total direct cost of diabetes in the EU5 was € 90 Billion (2010). The management of diabetes is a growing concern given the already high prevalence and associated high costs. As we seek to improve the management of diabetes, it's important to integrate patient preferences into policy and market access decisions. MethOds: The 2014 EU Roper Diabetes Study was conducted among 3,013 diagnosed adult diabetes patients across the EU5. During 50-min telephone interviews, respondents were asked about their willingness to investigate new techniques and preferences for managing their diabetes. Data were compared across countries, diabetes type, age group, year since diagnosis and current therapy to determine relative openness to new techniques. Statistical significance testing between analytic groups was performed at the 95% confidence level. Results: In total 46.9% of diabetes patients reported that they would "like to investigate or t...
BACKGROUND Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist. OBJECTIVE The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM). METHODS This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating. RESULTS The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02). CONCLUSIONS Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.
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