Emotional well-being and everyday life function are important outcomes for people with type 1 diabetes (T1D) and are robust contributors to overall quality of life. Various indicators of emotional distress (e.g., negative mood, depressive symptoms, diabetes distress) are elevated in people with T1D. Likewise, function, or the ability to perform necessary or desired daily life activities, is also adversely impacted: symptoms of hypo- and hyperglycemia and T1D self-management tasks can disrupt participation in everyday work, leisure, and social activities among people with T1D. While acute blood glucose levels and variability are thought to contribute to diminished function and well-being in people with T1D, these relationships are poorly understood. Diabetes research to date has primarily relied on global, retrospective measures of blood glucose, function and emotional well-being that cannot capture their complex dynamic relationships as they unfold in everyday life contexts. To overcome this limitation, the Function and Emotion in Everyday Life with Type 1 Diabetes (FEEL- T1D) study is the first large-scale study to integrate continuous glucose monitoring (CGM), accelerometry, and ecological momentary assessment (EMA) to disentangle the short-term dynamic relationships between blood glucose, function, and emotional well-being in adults with type 1 diabetes. In the FEEL-T1D study, 200 adults with T1D will complete a baseline survey battery followed by 14 days of intensive longitudinal data collection using blinded CGM coupled with EMA surveys, ambulatory cognitive tasks, and accelerometer wear. These tools will be used to assess blood glucose, emotional well-being (stress, positive and negative affect, diabetes distress), function (self-reported activity performance, objective cognitive function, physical activity), and covariates that may moderate or confound the relationships of interest. Multiple metrics characterizing blood glucose (acute glucose level, glycemic excursions, glycemic variability, and % time in glycemic ranges) will be derived from CGM data to determine which have the most pronounced relationships with well-being and function on both a short-term and day-to-day basis. By clarifying which metrics of blood glucose are most closely related to clinical and patient-reported outcomes, these analyses will be foundational to the individualization of treatment recommendations and lead to the development of innovative just-in-time adaptive interventions to address the most potent predictors of health and well-being. The study's specific aims are as follows:
Aim 1 : Understand within-person dynamic relationships between blood glucose metrics, function, and emotional well-being through multi-level time-series analyses using CGM, EMA, and accelerometer data.
Aim 2 : Examine moderators of short-term and daily relationships, such as demographics, clinical characteristics, and HbA1c, between blood glucose metrics, function, and emotional well-being.
Aim 3 : Understand how short-term dynamics between blood glucose metrics, function, and emotional well-being are predictive of global function, well-being, and quality of life.
/PUBLIC HEALTH RELEVANCE It is generally understood that fluctuations in blood glucose are both distressing and intrusive for people with type 1 diabetes, yet diabetes research to date has primarily relied on global, retrospective measures that cannot capture the relationships between acute blood glucose fluctuations, emotional well-being, and function in everyday life. To overcome this limitation, the proposed Function and Emotion in Everyday Life with Type 1 Diabetes (FEEL-T1D) study is the first large-scale study to integrate continuous glucose monitoring (CGM), ecological momentary assessment (EMA), and accelerometry to disentangle the short-term dynamic relationships between blood glucose, function, and emotional well-being in adults with type 1 diabetes. These analyses will contribute essential basic knowledge about these relationships that will be foundational to the individualization of treatment recommendations and development of innovative interventions that optimize both clinical and patient-reported outcomes.
