Depression symptoms are persistent in Type 2 diabetes: risk factors and outcomes of 5-year depression trajectories using latent class growth analysis

2017 ◽  
Vol 34 (8) ◽  
pp. 1108-1115 ◽  
Author(s):  
S. R. Whitworth ◽  
D. G. Bruce ◽  
S. E. Starkstein ◽  
W. A. Davis ◽  
T. M. E. Davis ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Growth Analysis ◽  
Latent Class ◽  
Diabetes Risk ◽  
Depression Symptoms ◽  
Latent Class Growth Analysis ◽  
Diabetes Risk Factors ◽  
Latent Class Growth

Youth prediabetes and type 2 diabetes: Risk factors and prevalence of dysglycaemia

2021 ◽  
Author(s):  
Mohamed Saleh ◽  
Joon Young Kim ◽  
Christine March ◽  
Nour Gebara ◽  
Silva Arslanian
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Diabetes Risk ◽  
Diabetes Risk Factors

223-OR: Heart Failure with Reduced, Midrange and Preserved Ejection Fraction in Chinese with Type 2 Diabetes: Risk Factors and Prognosis

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 223-OR
Author(s):  
ANDREA LUK ◽  
XINGE ZHANG ◽  
ERIK FUNG ◽  
HONGJIANG WU ◽  
ERIC S. LAU ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Type 2 Diabetes ◽  
Ejection Fraction ◽  
Diabetes Risk ◽  
Preserved Ejection Fraction ◽  
Diabetes Risk Factors

scholarly journals Comorbidity of depression and type 2 diabetes: Risk factors and clinical significance

2010 ◽  
Vol 67 (6) ◽  
pp. 493-500
Author(s):  
Zana Stankovic ◽  
Miroslava Jasovic-Gasic
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Clinical Significance ◽  
Diabetes Risk ◽  
Diabetes Risk Factors

nema

scholarly journals Exercise prevents HFD- and OVX-induced type 2 diabetes risk factors by decreasing fat storage and improving fuel utilization

2018 ◽  
Vol 6 (13) ◽  
pp. e13783 ◽  
Author(s):  
Brittany K. Gorres-Martens ◽  
Tyler J. Field ◽  
Emma R. Schmidt ◽  
Karen A. Munger
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Diabetes Risk ◽  
Fuel Utilization ◽  
Fat Storage ◽  
Diabetes Risk Factors

P4-337: A genotype risk score based on Type 2 diabetes risk factors is associated with lower risk for Alzheimer's disease in APOE e4 carriers

2011 ◽  
Vol 7 ◽  
pp. e28-e29
Author(s):  
Petroula Proitsi ◽  
Michelle Lupton ◽  
Magda Tsolaki ◽  
Makrina Daniilidou ◽  
Hilkka Soininen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Type 2 Diabetes ◽  
Alzheimer's Disease ◽  
Risk Score ◽  
Lower Risk ◽  
Diabetes Risk ◽  
Diabetes Risk Factors

scholarly journals Type 2 Diabetes Risk Factors in Mexican-American Youth

1999 ◽  
Vol 45 (4, Part 2 of 2) ◽  
pp. 90A-90A
Author(s):  
Daniel E Hale ◽  
Jorge E Gomez
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Mexican American ◽  
Diabetes Risk ◽  
Diabetes Risk Factors ◽  
Mexican American Youth

scholarly journals Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

10.2196/17730 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17730
Author(s):  
Qing Yang ◽  
Daniel Hatch ◽  
Matthew J Crowley ◽  
Allison A Lewinski ◽  
Jacqueline Vaughn ◽  
...  
Keyword(s):  
Clinical Characteristics ◽  
Growth Analysis ◽  
Latent Class ◽  
Self Management ◽  
Latent Class Growth Analysis ◽  
Self Monitoring ◽  
Monitoring Behavior ◽  
Latent Class Growth ◽  
Management Behaviors

Background Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors. Objective The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics. Methods This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test. Results The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A1c level were more likely to be in the low and waning engagement group. Conclusions We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition. International Registered Report Identifier (IRRID) RR2-10.2196/13517

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