Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
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Background: Type 2 diabetes (T2D) is a growing concern among older adults, increasing the risk of frailty, and functional decline. In Taiwan, the convergence of population aging and high diabetes prevalence calls for innovative care strategies. This study evaluated the effectiveness of incorporating wearable step-count devices into the diabetic pay-for-performance (P4P) program to enhance physical activity and explore associations with related health outcomes.
Methods: This prospective, single-arm interventional study was conducted from February to September 2023 at a medical center in central Taiwan. T2D participants in P4P who were able to use smart phone were enrolled. At baseline, comprehensive geriatric assessment was performed to measure participants' physical, mental functions and nutritional status. Daily step data were collected Garmin trackers and synced automatically. Participants received weekly remote feedback from diabetes educators to encourage adherence in 2 months. The Wilcoxon signed-rank test assessed changes in step counts over time, and Spearman's rank correlation examined associations with baseline health indicators. An association of daily step counts with metabolic controls factors, biochemical data, disease severity, functional performance, frailty, nutritional and mood were analyzed.
Results: The study involved 66 participants, median age 72 years, with 24 males (36.4%) and 42 females (63.6%). Metabolic indicators showed fasting plasma glucose at 110.0 mg/dL (interquartile range, IQR: 97.0-137.5) and hemoglobin A1c at 6.1 (IQR: 5.7-7.2). Additionally, low-density lipoprotein was 86.5 mg/dL (IQR: 67.3-104.5), and triglycerides were 98.5 mg/dL (IQR: 76.8-139.8). Urine albumin-creatinine ratio was 15.3 (IQR: 7.6-84.9), and estimated glomerular filtration rate (eGFR) was 70.7 mL/min/1.73 m (IQR: 48.9-78.1). Functional capacity varied, with 47.0% having low muscle strength and 92.0% showing low physical performance. 15.2% showed symptoms of depression. Malnutrition and frailty were observed in 6.1% and 13.6%, respectively. Median daily steps significantly increased from 1,560.8 (IQR: 955.9-3,301.5) in week 1 to 2,652.9 (IQR: 1,271.8-4,139.3) in the final week ( < 0.001). Higher daily step counts were positively correlated with physical and nutritional status and negatively correlated with age, depressive symptoms, and frailty. Remote monitoring led to a significant and consistent increase in daily step counts across all tracking periods ( < 0.001).
Conclusions: The study found that digital mobile health monitoring improved daily step counts over time in older diabetic patients, and baseline physical functions, and nutritional status were related to the changes. Whether incorporating this wearable technology into diabetes education program improves long metabolic controls needs further researches.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255238 | PMC |
http://dx.doi.org/10.7717/peerj.19659 | DOI Listing |