Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

Gönder-Frederick, Linda; Grabman, Jesse H.; Kovatchev, Boris; Brown, Sue A.; Patek, Stephen D.; Basu, Ananda; Pinsker, Jordan E.; Kudva, Yogish C.; Wakeman, Christian; Dassau, Eyal; Cobelli, Claudio; Zisser, Howard; Doyle, Francis J.

doi:10.1177/1932296816635199

Cited by 23 publications

(30 citation statements)

References 28 publications

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“…The 18 studies included in this review consist of 5 qualitative studies [ 40 - 44 ], 5 experimental studies [ 27 , 32 - 35 ], 3 observational studies [ 29 - 31 ], 4 cross-sectional studies [ 36 - 39 ], and 1 mixed-methods study [ 45 ]. Of the studies, 5 were smaller samples drawn from parent clinical trials [ 30 , 32 , 38 , 40 , 41 ].…”

Section: Resultsmentioning

confidence: 99%

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review

Waite¹,

Martin²,

Franklin³

et al. 2018

JMIR Hum Factors

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BackgroundPeople with type 1 diabetes (T1D) undertake self-management to prevent short and long-term complications. Advanced technology potentially supports such activities but requires consideration of psychological and behavioral constructs and usability issues. Economic factors and health care provider capacity influence access and uptake of advanced technology. Previous reviews have focused upon clinical outcomes or were descriptive or have synthesized studies on adults with those on children and young people where human factors are different.ObjectiveThis review described and examined the relationship between human factors and adherence with technology for data logging processes in adults with T1D.MethodsA systematic literature search was undertaken by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality appraisal was undertaken and data were abstracted and categorized into the themes that underpinned the human factor constructs that were examined.ResultsA total of 18 studies were included. A total of 6 constructs emerged from the data analysis: the relationship between adherence to data logging and measurable outcomes; satisfaction with the transition to advanced technology for self-management; use of advanced technology and time spent on diabetes-related activities; strategies to mediate the complexities of diabetes and the use of advanced technology; cognition in the wild; and meanings, views, and perspectives from the users of technology.ConclusionsIncreased treatment satisfaction was found on transition from traditional to advanced technology use—insulin pump and continuous glucose monitoring (CGM); the most significant factor was when blood glucose levels were consistently <7.00 mmol/L (P ≤.01). Participants spent considerable time on their diabetes self-care. Logging of data was positively correlated with increasing age when using an app that provided meaningful feedback (regression coefficient=55.8 recordings/year; P ≤.01). There were benefits of CGM for older people in mediating complexities and fears of hypoglycemia with significant differences in well-being (P ≤.001).Qualitative studies explored the contextual use and uptake of technology. The results suggested frustrations with CGM, continuous subcutaneous insulin infusion, calibration of devices, and alarms. Furthermore implications for “body image” and the way in which “significant others” impacted on the behavior and attitude of the individual toward technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts, which has implications for the way in which future technologies should be designed.Quantitative studies were limited by small sample sizes, making it difficult to generalize findings to other contexts. This was further limited by a sample that was predominantly white, well-controlled, and engaged with self-care. The use of critical appraisal frameworks demonstrated where research into human factors and dat...

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Section: Resultsmentioning

confidence: 99%

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review

Waite¹,

Martin²,

Franklin³

et al. 2018

JMIR Hum Factors

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“…The 18 studies included in this review consist of 5 qualitative studies [40][41][42][43][44], 5 experimental studies [31][32][33][34][35], 3 observational studies [28][29][30], 4 cross-sectional studies [36][37][38][39] and 1 mixed methods [45]. Five of the studies were smaller samples drawn from parent clinical trials [29,32,38,40,41].…”

Section: Resultsmentioning

confidence: 99%

“…The data suggests that those from higher socioeconomic groups are more likely to have access to and engage with technology in their self-management behaviors [38]. Two studies [29,45] purposefully selected participants with prior adherent behaviors, however one study recruited participants who were less engaged with technology and adherence [32].…”

Section: Data Abstraction and Analysismentioning

confidence: 99%

“…Gonder-Frederick et al [29] investigated the relationship between routine daily stressors , glucose levels, and diabetes management strategies in a naturalistic setting using a CGM data to generate BG profiles (adults with T1DM were also participating in multi-center cross-over RCT CLC study). There was no relationship found between stress ratings and average daily glucose.…”

Section: The Potential Of Cgm To Explore Stressorsmentioning

confidence: 99%

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Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review (Preprint)

Waite¹,

Martin²,

Franklin³

et al. 2017

Preprint

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Background: People with T1DM are confronted with self-management tasks and strategies to reduce risks of long-term complications against the risk of hypoglycemic events. The potential of advanced and evolving technology to address these issues involves consideration of psychological and behavioral constructs alongside the usability of devices. Access and uptake of advanced technology is further influenced by economic factors and health care provider capacity to support such interventions. Previous reviews have focused upon either clinical outcomes or descriptively scoped the literature and sometimes synthesize studies on adults with children and young people where human factors are different. Objective:The objective of this review was to describe the relationship between human factors and adherence with technology for data logging processes in adults with T1DM and to explore the factors which influence this association. Methods: A systematic literature review.Results: 18 studies were included in the review following a literature search using the PRISMA guidelines and the quality appraisal process. Six constructs emerged from the data analysis:. The relationship between adherence to data logging and measurable outcomes; Satisfaction with the transition to advanced technology for self-management; Use of advanced technology and time spent on diabetes related activities; Strategies to mediate the complexities of diabetes and the use of advanced technology; Cognition in the wild and Meanings, views and perspectives from the users of technology.There was evidence of increased treatment satisfaction on transition to advanced technology (insulin pump and CGM), the most significant contributing factor was when BG levels were consistently < 7.00mmol/l (P =.009). However, participants spent considerable time on their diabetes self-care. There was evidence that 1 logging of data was positively correlated with increasing age with the use of an app that provided meaningful feedback (regression coefficient = 55.8 recordings/ year; P = 0.009). Furthermore, there were benefits of CGM for older people in mediating complexities and their fears of hypoglycemia with reported significant differences in well-being (P= .009).Qualitative studies within the review aimed to explore the use and uptake of technology within the context of everyday lives. There were 'frustrations' with CGM, CSII, calibration of devices and alarms. In addition, the implications of 'body image' and the way in which 'significant' others impacted on the behavior and attitude of the individual towards technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts. This has implications for the way in which future technologies are designed. Conclusions:Many of the quantitative studies in the review were limited by small sample sizes. This may make it difficult to generalize findings to other contexts. This is further limited by a sample that was predominantly Caucasian, well-contr...

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“…The need to incorporate psychological stress into the artificial pancreas is also under investigation. In [76], a significant relationship between daily stress (rated by the patient in a 5-point scale) and glucose variability was found, as well as an increase percentage of time in hypoglycemia and reduced carbohydrate consumption. However, mean glucose was not affected.…”

Section: Open Challengesmentioning

confidence: 93%

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

et al. 2018

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is currently the safest infusion route for a commercial artificial pancreas, as opposed to the endogenous pancreas that secretes insulin directly into portal circulation (the vessels that connect the pancreas and other organs with the liver, which acts as a first blood filter before entering the main circulatory system). Even in approaches with concomitant infusion of glucagon (the socalled dual-hormone artificial pancreas), mechanisms are necessary to avoid an excess of insulin delivery which may lead to late hypoglycemia putting at stake the patient's safety. Independently of how these mechanisms are incorporated into the control schemes, all of them must rely on pharmacokinetic models predicting either circulating plasma insulin or a measure of "insulin-onboard", such as the insulin depot remaining at the subcutaneous tissue before entering circulation. In this article, methods to constrain insulin delivery are reviewed, as well as the subcutaneous insulin pharmacokinetic models and estimators on which they rely upon. A big challenge in the prediction of physiological signals, such as insulin concentration, is the large intra-subject variability that patients suffer. Indeed, in terms of control engineering, a patient is a highly time-varying uncertain plant. The intra-day and day-today patient's behavior change due to circadian rhythms (24-hour rhythmic physiological oscillations driven by the body clock, for instance, daily patterns in insulin sensitivity), and other multiple sources of uncertainty arise in key physiological processes such as meal absorption and subcutaneous insulin absorption. Despite this fact, the use of population models for the prediction of insulin pharmacokinetics, that is, how the infused insulin appears in blood, is still common practice. The impact of variability on the model prediction and its implication in closed-loop performance is analyzed. Nevertheless, large intra-subject variability suggests that real-time state and pharmacokinetic parameters estimation is convenient, even when individualized models are considered. The availability of continuous glucose measurements allows to address this problem should an observable glucose-insulin model be available. Different observer techniques proposed to this purpose are reviewed and discussed. The subcutaneous insulin route The pancreas secretes insulin into the portal vein towards the liver, which acts as a first filter before insulin reaches systemic circulation. In the liver, insulin promotes glucose storage in hepatic cells decreasing glucose production. In the fat and muscle cells, insulin acts as a key that triggers the mobilization of glucose transporters to the cell membrane promoting glucose uptake by the cell. As a result of both actions, plasma glucose concentration decreases. The dynamic lag of insulin action is estimated to be about 30 minutes [3]. An artificial pancreas is a classic closed-loop glucose control system (see Figure 1) that

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Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

Cited by 23 publications

References 28 publications

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review (Preprint)

Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control

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