Personal and Social-Built Environmental Factors of Glucose Variability Among Multiethnic Groups of Adults With Type 2 Diabetes: Research Protocol Using Ecological Momentary Assessment, Continuous Glucose Monitoring, and Actigraphy.
Glucose variability (GV)-the degree of fluctuation in glucose levels over a certain period of time-is emerging as an important parameter of dynamic glycemic control. Repeated glycemic oscillations have been reported to be the link to diabetes complications. This prospective observational study aims to: (1) identify multilevel risk factors (personal and social-built environmental factors) associated with high GV; (2) identify "within-person predictors" of high GV leveraging the intra-person data to inform future personalized diabetes interventions; and (3) examine which lifestyle factors either mediate or moderate the relationship between emotional well-being and GV among diverse adults with type 2 diabetes (T2D). We will recruit 200 adults with T2D from the community. All participants will complete baseline surveys assessing demographics, lifestyle, social-built environmental, and clinical factors. Real-time dynamic glucose levels will be measured using continuous glucose monitoring (CGM). Sleep, physical activity, diet/eating, and emotional well-being will be measured with an actigraphy device and a real-time self-report tool (ecological momentary assessment [EMA]) across 14 days. Two 24-h dietary recall data will be collected by online video calls. Generalized linear models, multilevel models, and structural equation models will be developed to achieve the study aims. The findings from the study will identify high-risk groups of high GV who would benefit from CGM to improve diabetes outcomes and inform the future development of personalized just-in-time interventions targeting lifestyle behaviors with an increased understanding of GV and by supporting healthcare providers' clinical decisions.
Nam S
，Jeon S
，Ash GI
，Weinzimer S
，Dunton GF
，Parekh N
，Grey M
，Chen K
，Lee M
，Sajdlowska A
，Whittemore R
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Assessing Dynamic Cognitive Function in the Daily Lives of Youths With and Without Type 1 Diabetes: Usability Study.
Studies have shown a relationship between worse glycemic control and lower cognitive scores in youths with type 1 diabetes (T1D). However, most studies assess long-term glucose control (eg, years-decades) and cognition at a single time point. Understanding this relationship at a higher temporal resolution (eg, minutes-hours) and in naturalistic settings has potential clinical implications. Newer technology (eg, continuous glucose monitoring [CGM] and ecological momentary assessment) provides a unique opportunity to explore the glucose dynamics that influence dynamic cognition; that is, cognitive functions that fluctuate short-term and are influenced by environmental factors.
Before we can assess this relationship, we need to determine the feasibility of measuring cognition in youths in daily life and determine the plausibility of obtaining glucose variation with CGM to be integrated with real-time cognition measures. This study's purpose was to assess the acceptability of measuring dynamic cognition using a smartphone app and adherence to cognitive testing in daily life in youths with and without T1D. Further, we assessed CGM-derived glucose measures at temporally related timeframes to cognitive testing in naturalistic settings.
Data were obtained from 3 studies including one in-laboratory study and 2 remote studies. For all studies, youths were asked to complete cognitive tests on the Ambulatory Research in Cognition (ARC) smartphone app that measured processing speed, associative memory, and working memory. For the in-laboratory study, youths completed testing 4 times during 1 session. For the remote studies, youths were asked to complete cognitive tests 5 times per day for either 10 or 14 consecutive days in daily life. Youths were asked to rate their impressions of the app. Youths with T1D wore a CGM.
74 youths (n=53 control; n=21 T1D) aged 4-16 years participated. Youths generally reported liking or understanding the ARC app tasks in a laboratory and remote setting. Youths had high testing adherence in daily life (2350/3080 to 721/900, 76.3%-80.2%) and none dropped out. The percentage of measurements within each glycemic range taken immediately before the app's cognitive testing was 3% (28/942) low glucose, 51% (484/942) euglycemia, 23% (221/942) high glucose, and 22% (210/942) very high glucose. In the 2-hour window before each cognitive task, mean glucose was 182.5 (SD 76.2) mg/dL, SD in glucose was 27.1 mg/dL (SD 18.7), and the mean maximum difference between the highest and lowest glucose was 85.5 (SD 53.7) mg/dL.
The results suggest that using the ARC smartphone app to assess dynamic cognitive functions in youths with and without T1D is feasible. Further, we showed CGM-derived glycemic variability at temporally associated timeframes of dynamic cognitive assessments. The next steps include using ecological momentary assessment in a fully powered study to determine the relationship between short-term glycemic control and cognition in youths with T1D.
Ray MK
，Fleming J
，Aschenbrenner A
，Hassenstab J
，Redwine B
，Burns C
，Arbelaez AM
，Vajravelu ME
，Hershey T
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A New Measure of Quantified Social Health Is Associated With Levels of Discomfort, Capability, and Mental and General Health Among Patients Seeking Musculoskeletal Specialty Care.
A better understanding of the correlation between social health and mindsets, comfort, and capability could aid the design of individualized care models. However, currently available social health checklists are relatively lengthy, burdensome, and designed for descriptive screening purposes rather than quantitative assessment for clinical research, patient monitoring, or quality improvement. Alternatives such as area deprivation index are prone to overgeneralization, lack depth in regard to personal circumstances, and evolve rapidly with gentrification. To fill this void, we aimed to identify the underlying themes of social health and develop a new, personalized and quantitative social health measure.
(1) What underlying themes of social health (factors) among a subset of items derived from available legacy checklists and questionnaires can be identified and quantified using a brief social health measure? (2) How much of the variation in levels of discomfort, capability, general health, feelings of distress, and unhelpful thoughts regarding symptoms is accounted for by quantified social health?
In this two-stage, cross-sectional study among people seeking musculoskeletal specialty care in an urban area in the United States, all English and Spanish literate adults (ages 18 to 89 years) were invited to participate in two separate cohorts to help develop a provisional new measure of quantified social health. In a first stage (December 2021 to August 2022), 291 patients rated a subset of items derived from commonly used social health checklists and questionnaires (Tool for Health and Resilience in Vulnerable Environments [THRIVE]; Protocol for Responding to and Assessing Patient Assets, Risks and Experiences [PRAPARE]; and Accountable Health Communities Health-Related Social Needs Screening Tool [HRSN]), of whom 95% (275 of 291; 57% women; mean ± SD age 49 ± 16 years; 51% White, 33% Hispanic; 21% Spanish speaking; 38% completed high school or less) completed all items required to perform factor analysis and were included. Given that so few patients decline participation (estimated at < 5%), we did not track them. We then randomly parsed participants into (1) a learning cohort (69% [189 of 275]) used to identify underlying themes of social health and develop a new measure of quantified social health using exploratory and confirmatory factor analysis (CFA), and (2) a validation cohort (31% [86 of 275]) used to test and internally validate the findings on data not used in its development. During the validation process, we found inconsistencies in the correlations of quantified social health with levels of discomfort and capability between the learning and validation cohort that could not be resolved or explained despite various sensitivity analyses. We therefore identified an additional cohort of 356 eligible patients (February 2023 to June 2023) to complete a new extended subset of items directed at financial security and social support (5 items from the initial stage and 11 new items derived from the Interpersonal Support Evaluation List, Financial Well-Being Scale, Multidimensional Scale of Perceived Social Support, Medical Outcomes Study Social Support Survey, and 6-item Social Support Questionnaire, and "I have to work multiple jobs in order to finance my life" was self-created), of whom 95% (338 of 356; 53% women; mean ± SD age 48 ± 16 years; 38% White, 48% Hispanic; 31% Spanish speaking; 47% completed high school or less) completed all items required to perform factor analysis and were included. We repeated factor analysis to identify the underlying themes of social health and then applied item response theory-based graded response modeling to identify the items that were best able to measure differences in social health (high item discrimination) with the lowest possible floor and ceiling effects (proportion of participants with lowest or highest possible score, respectively; a range of different item difficulties). We also assessed the CFA factor loadings (correlation of an individual item with the identified factor) and modification indices (parameters that suggest whether specific changes to the model would improve model fit appreciably). We then iteratively removed items based on low factor loadings (< 0.4, generally regarded as threshold for items to be considered stable) and high modification indices until model fit in CFA was acceptable (root mean square of error approximation [RMSEA] < 0.05). We then assessed local dependencies among the remaining items (strong relationships between items unrelated to the underlying factor) using Yen Q3 and aimed to combine only items with local dependencies of < 0.25. Because we exhausted our set of items, we were not able to address all local dependencies. Among the remaining items, we then repeated CFA to assess model fit (RMSEA) and used Cronbach alpha to assess internal consistency (the extent to which different subsets of the included items would provide the same measurement outcomes). We performed a differential item functioning analysis to assess whether certain items are rated discordantly based on differences in self-reported age, gender, race, or level of education, which can introduce bias. Last, we assessed the correlations of the new quantified social health measure with various self-reported sociodemographic characteristics (external validity) as well as level of discomfort, capability, general health, and mental health (clinical relevance) using bivariate and multivariable linear regression analyses.
We identified two factors representing financial security (11 items) and social support (5 items). After removing problematic items based on our prespecified protocol, we selected 5 items to address financial security (including "I am concerned that the money I have or will save won't last") and 4 items to address social support (including "There is a special person who is around when I am in need"). The selected items of the new quantified social health measure (Social Health Scale [SHS]) displayed good model fit in CFA (RMSEA 0.046, confirming adequate factor structure) and good internal consistency (Cronbach α = 0.80 to 0.84), although there were some remaining local dependencies that could not be resolved by removing items because we exhausted our set of items. We found that more disadvantaged quantitative social health was moderately associated with various sociodemographic characteristics (self-reported Black race [regression coefficient (RC) 2.6 (95% confidence interval [CI] 0.29 to 4.9)], divorced [RC 2.5 (95% CI 0.23 to 4.8)], unemployed [RC 1.7 (95% CI 0.023 to 3.4)], uninsured [RC 3.5 (95% CI 0.33 to 6.7)], and earning less than USD 75,000 per year [RC 2.7 (95% CI 0.020 to 5.4) to 6.8 (95% CI 4.3 to 9.3)]), slightly with higher levels of discomfort (RC 0.055 [95% CI 0.16 to 0.093]), slightly with lower levels of capability (RC -0.19 [95% CI -0.34 to -0.035]), slightly with worse general health (RC 0.13 [95% CI 0.069 to 0.18]), moderately with higher levels of unhelpful thoughts (RC 0.17 [95% CI 0.13 to 0.22]), and moderately with greater feelings of distress (RC 0.23 [95% CI 0.19 to 0.28]).
A quantitative measure of social health with domains of financial security and social support had acceptable psychometric properties and seems clinically relevant given the associations with levels of discomfort, capability, and general health. It is important to mention that people with disadvantaged social health should not be further disadvantaged by using a quantitative measure of social health to screen or cherry pick in contexts of incentivized or mandated reporting, which could worsen inequities in access and care. Rather, one should consider disadvantaged social health and its associated stressors as one of several previously less considered and potentially modifiable aspects of comprehensive musculoskeletal health.
A personalized, quantitative measure of social health would be useful to better capture and understand the role of social health in comprehensive musculoskeletal specialty care. The SHS can be used to measure the distinct contribution of social health to various aspects of musculoskeletal health to inform development of personalized, whole-person care pathways. Clinicians may also use the SHS to identify and monitor patients with disadvantaged social circumstances. This line of inquiry may benefit from additional research including a larger number of items focused on a broader range of social health to further develop the SHS.
Brinkman N
，Broekman M
，Teunis T
，Choi S
，Ring D
，Jayakumar P
... -
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ResearchGate
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