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Abstract

As diabetes technologies continue to advance, their use is expanding beyond type 1 diabetes to include populations with type 2 diabetes, older adults, pregnant individuals, those with psychiatric conditions, and hospitalized patients. This review examines the psychosocial outcomes of these technologies across these diverse groups, with a focus on treatment satisfaction, quality of life, and self-management behaviors. Despite demonstrated benefits in glycemic outcomes, the adoption and sustained use of these technologies face unique challenges in each population. By highlighting existing research and identifying gaps, this review seeks to emphasize the need for targeted studies and tailored support strategies to understand and optimize psychosocial outcomes and well-being.

Keywords

diabetes and mental health diabetes in pregnancy inpatient diabetes management older adults with diabetes psychosocial impacts of diabetes technology type 2 diabetes

Introduction

As the benefits of diabetes technologies become increasingly evident among those with type 1 diabetes, there is growing interest in the use of diabetes technologies in other populations. Several specific populations that may particularly benefit include those with type 2 diabetes, older adults with diabetes, pregnant individuals with diabetes, those with psychiatric conditions and diabetes, and hospitalized individuals with diabetes. Although these technologies might be expected to improve glycemic outcomes, their potential psychosocial, behavioral, and treatment satisfaction outcomes deserve special attention.

Diabetes technologies offer diverse potential benefits, ranging from increased treatment satisfaction and reduced self-management burden to improved diabetes-related quality of life and decreased diabetes distress and fear of hypoglycemia. These technologies may positively impact self-management behaviors, such as dietary choices, physical activity, and medication adherence. In this review, we define psychosocial impacts as those related to emotional, mental, and social well-being, including diabetes distress and fear of hypoglycemia. Behaviors refer to actions individuals take to manage their diabetes, such as diet, physical activity, and medication adherence. Treatment and device satisfaction refer to users’ subjective evaluations of their experience with diabetes technologies. A comprehensive understanding of these dimensions of technology use, including the barriers and facilitators to sustained adoption, is essential. Such insights could inform iterative improvements in device design and system functionality, ultimately improving user experiences and outcomes.

This review focuses on the use of a variety of diabetes technologies in special populations, with contributions from researchers with expertise in the areas of technology use by people with type 2 diabetes (LB, WHP, and GA), older adults with diabetes (ARK and JO), pregnant women with diabetes (DI), individuals with psychiatric conditions and diabetes (LAG-F and AFMBB), and hospitalized individuals with diabetes (ERF, MSH, MMS, and SAB). By examining the current evidence and identifying gaps in research, we aim to explore how these technologies impact these different groups, highlighting the need for targeted inquiries to capture unique insights from each population.

Psychosocial Aspects of Diabetes Technology Usage in People With Type 2 Diabetes

Over the past decade, the rise of diabetes technologies, including decision support for insulin dosing, continuous glucose monitoring (CGM) systems, insulin pumps, and automated insulin delivery (AID) systems, has become a cornerstone of diabetes care.¹ Although the focus of these technologies has largely been on type 1 diabetes, there is growing evidence that people with type 2 diabetes receive similar therapeutic benefits.^2-4

Similarly to type 1 diabetes, the rationale to use these technologies is increased achievement of glycemic targets, support in self-management, and increasing overall treatment satisfaction and quality of care. However, depending on the patients’ profile, type of treatment, and treatment goals, specific needs may deviate from those of type 1 diabetes. For example, people with type 2 diabetes are often not being taught about carbohydrate counting, usually have more than one glucose-lowering agent, need to lose body weight as part of their therapeutic strategy, and may be less technology-versed.^5-7

Although scientific evaluations on the use of CGM, insulin pumps, and, more recently, AID have a primary focus on glycemic efficacy and safety, less attention is paid to other impacts of diabetes technologies. Such dimensions include potential gains in treatment satisfaction due to reduced self-management burden, improvements in diabetes-related quality of life, and reductions in diabetes distress and fear of hypoglycemia.⁸ In addition, the use of technology may positively influence the user’s self-management behavior, such as changes in dietary intake, physical activity, and adherence to medication. Finally, the psychosocial perspective on the use of technology also includes the identification of barriers to ongoing technology use as well as facilitating conditions.⁹

Quantitative methods for examining these domains consist of patient-reported outcome measures (PROMs) that are typically generated by validated questionnaires. Over the past decades, several diabetes-specific PROMs have been developed that are frequently used alongside generic (disease-agnostic) tools, such as the 36-Item Short-Form Survey Instrument (SF-36).¹⁰ Examples of diabetes-specific instruments include the Diabetes Quality of Life (DQOL) questionnaire, the Diabetes Distress Scale (DDS) and more device-oriented frameworks such as the Insulin Device Satisfaction Survey (IDSS) and the INsulin Dosing Systems: Perceptions, Ideas, Reflections, and Expectations (INSPIRE) measures, with the latter being specifically tailored to AID systems.^11-13 Qualitative evidence is generated by semi-structured interviews and focus group techniques conducted by qualified professionals. Behavioral changes can be explored using surveys, questionnaires, diaries, logs, and more objective tools such as wearable and digital technologies (eg, smartwatches).¹⁴

Given the wide spectrum of currently available types and uses of diabetes technologies, ranging from hardware to software applications, this section is limited to an examination of medical devices for glucose management with a focus on CGM, insulin pumps, AID systems, and smart pen applications. Table 1 provides an overview of diabetes technology studies in people with type 2 diabetes which included psychosocial, behavioral, and satisfaction outcomes.^2,3,15-23 The majority of investigations focused on people with insulin-treated type 2 diabetes and explored CGM devices. The longest intervention duration was 11 months in a study exploring the efficacy and usability of bolus insulin delivery with an insulin patch vs conventional pen device.²⁴ The patch was associated with greater device satisfaction, more positive experience, and preference for initiating mealtime insulin when compared with the pen. Improvement in diabetes treatment satisfaction, as assessed with the Diabetes Treatment Satisfaction Questionnaire (DTSQ)²⁵ or IDSS,¹¹ was also demonstrated in a six-month study evaluating intermittently scanned (is) CGM,¹⁵ a six-month study assessing the Omnipod Dash insulin pump,¹⁶ and a short (eight-week) single-arm trial evaluating the Omnipod 5 AID system.¹⁷ Some CGM studies demonstrated a reduction in diabetes-related distress and hypoglycemia fear.^18,26 However, several studies found no statistically significant differences in diabetes management-specific outcomes^2,3,19-22 and one study, evaluating fully AID in outpatients with type 2 diabetes, even reported greater hypoglycemia fear with AID vs usual care insulin delivery.³ Surprisingly, the observed impact on quality of life across all studies was relatively small.

Table 1.

Psychosocial Outcomes in Type 2 Diabetes Trials Using Diabetes Technology.

Author	N	DM treatment at enrollment	Study design and device under investigation	Duration	PRO measure	Results
Vigersky et al¹⁹	100	OAD ± basal insulin	Parallel RCT, rtCGM (Dexcom SEVEN, intermittent use) vs BGM	12 weeks use40 weeks F/up	PAID	No significant group differences
Beck et al²⁰	158	MDI	Parallel RCT, rtCGM (Dexcom G4) vs BGM	6 months	DDSHFSHCS	No significant group differencesNo significant group differencesNo significant group differences
Haak et al¹⁵	224	CSII, MDI	Parallel RCT, isCGM (FreeStyle Libre 1) vs BGM	6 months	DDSDQOLDTSQ (DTSQs, DTSQc)	No significant group differencesNo significant group differencesImprovement for isCGM group (P < .0001)
Yaron et al²¹	101	MDI	Parallel RCT, isCGM (FreeStyle Libre 1) vs BGM	10 weeks	DTSQ (DTSQs, DTSQc)ADDQOL-19	No significant group differencesNo significant group differences
Gilbert et al¹⁸	66	MDI	Single arm trial rtCGM (Dexcom G6)	3 months	DDSHABS	Decrease in DDS ( P < .001)Decrease in HABS (P < .001)
Soriano et al²³	174	MDI	Singe arm trial, rtCGM (Dexcom G5)	6 months	DDSHCHFSGMSS, Emotional BurdenGMSS, Trust	Improvement in DDS (P < .001)Improvement in HC (P < .001)Improvement in HFS (P = .031)Improvement in GMSS-EB (P < .001)No improvement in GMSS-T (P = .808)
Ajjan et al²²	141	Basal insulin, OADs (Mfm, SU, GLP-1 RA, SGLT2i)	Parallel RCT, isCGM (FreeStyle Libre 1 or 2) vs BGM in MI setting	76-90 days	DTSQsEQ5D-5LADDQOL	No significant group differencesNo significant group differencesNo significant group differences
Bergenstal et al²⁴	278	Basal insulin ± OAD	Crossover RCT, Bolus patch (Cequr) vs MDI	48 weeks	DSQOL	Improvement in 2 of the 6 DSQOL subscales:Daily functions (P = .03)Diet restrictions (P = .02)
Polonsky et al¹⁶	220	CSII, MDI	Prospective observational study, CSII (Omnipod Dash) vs MDI	6 months	T2-DDASDIDPHABSWHO-5IDSS-T2D	Improvement in T2-DDAS (P < .001)Improvement in DIDP (P = .003)Improvement in HABS (P < .001)Improvement in WHO-5 score (P < .001)Improvement in IDSS-T2D (P < .001)
Galindo RJ et al²	80	OAD ± basal insulin	Crossover RCT, smart insulin pen cap vs masked device without alarm notifications	12 weeks	DTSQc	No significant difference between control and intervention periods
Daly AB et al³	26	MDI (basal-only or basal-bolus)	Crossover RCT, fully AID (CamAPS HX) vs usual care	8 weeks	PAIDHCSHFS, Worry subscale	No significant group differencesNo significant group differencesGreater hypoglycemia fear with AID, although statistical significance is not indicated
Davis GM et al¹⁷	24	MDI (basal-only or basal-bolus)	Single arm trial, hybrid AID (Omnipod5)	8 weeks	IDSS-T2D	Improvement in IDSS-T2D (P < .001)

Abbreviations: ADDQOL-19 Audit of Diabetes Dependent Quality of Life 19 questionnaire; AID, automated insulin delivery; CSII, continuous subcutaneous insulin infusion; DDS, Diabetes Distress Scale; DIDP, DAWN2 Impact of Diabetes Profile; DQOL, Diabetes Quality of Life; DREB, device-related emotional burden; DRT, device-related trust; DTSQ, Diabetes Treatment Satisfaction Questionnaire; EQ5D-5L, Euro Quality of Life 5 Dimension questionnaire; GLP-1 RA, glucagon-like peptide 1 receptor agonists; HABS, Hypoglycemia Attitude and Behavior Scale; HCS, hypoglycemia confidence scale; HFS, hypoglycemic fear scale; IDSS, Insulin Device Satisfaction Survey; MDI multiple daily insulin injection; Mfm, metformin; OAD, oral antidiabetic agents; PAID, problem areas in diabetes survey; SGLT2i, sodium-glucose cotransporter-2 inhibitors; SU, sulfonylureas; T2-DDAS, Type 2 Diabetes Distress Assessment System Core Scale; WHO-5, World Health Organization-5.

Recently, one of the first studies reporting qualitative evidence of the effect of CGM on attitudes and behaviors in adults with type 2 diabetes was published.⁹ The study used semi-structured interview techniques and was conducted within an ongoing large Dexcom G6 deployment project in Hancock County, Ohio.²⁷ Insights from a total of 34 CGM users support the use of the technology, not only to foster more positive diabetes attitudes (eg, feeling a greater sense of control over diabetes), greater diabetes literacy (eg, understanding of factors influencing their glucose levels), and enhanced self-efficacy, but also to promote positive behavioral changes in terms of diet, physical activity, and medication adherence.

Overall, the available evidence on the impact of diabetes technologies on psychosocial outcomes, behavior, and satisfaction in type 2 diabetes is scarce, underscoring a clear need for more research in the field, both quantitatively and qualitatively. Despite the growing evidence on the glycemic benefits of diabetes technologies in people with type 2 diabetes, including the latest developments such as fully AID devices,^3,17 the success of these innovations depends on the attitudes and self-care behavior of the individuals who choose to adopt them. In this context, the identification of barriers and facilitating interventions is critical. In order to ensure a high and sustainable uptake of diabetes technologies in type 2 diabetes, perceived benefits (eg, glycemic outcomes, ease in self-care) have to outweigh the perceived negatives (eg, intrusiveness of the device, concerns about malfunction and insufficient reliability, out-of-pocket costs, lack of educational support). Key requirements for expanding the availability and uptake to the type 2 diabetes population include user-friendly and interoperable devices, supportive reimbursement frameworks, as well as comprehensive education and personalized support systems.

Diabetes Technology Use in Older Adult Populations

Mounting evidence suggests that the integration of diabetes technologies can improve outcomes and increase quality of care for older adults living with diabetes.^28,29 Herein, we focus on two such technologies: CGM and AID systems.

Only a handful of randomized trials have characterized the efficacy of CGM in people 65 years and older; yet, data from such studies suggest that CGM is a highly acceptable intervention and may reduce the severity and incidence of hypoglycemia in older adults with type 1 diabetes and insulin-treated type 2 diabetes.^7,29 Data from the Wireless Innovation for Seniors With Diabetes Mellitus (WISDM) study found that among adults aged 60 and older, CGM compared with standard Fingerstick Blood Glucose (FSBG) monitoring displayed a statistically significant improvement in hypoglycemia over six months.²⁹ The DIAMOND trial, which recruited individuals with type 1 and type 2 diabetes using multiple daily injections (MDI), found that CGM use was associated with reduced glycemic variability and improved hemoglobin A1c (HbA_1c) in their older adult participant subgroup.³⁰ Finally, the MOBILE study found that among older adults with type 2 diabetes treated with basal insulin without bolus insulin, there was a statistically significant reduction in hyperglycemia, accompanying an overall increase in time in range (TIR, 70-180 mg/dL) in the group assigned to use CGM.³¹

Alongside improvements in glycemic management, several studies have documented that CGM use is associated with improved patient and caregiver experience, including increased perceptions of safety³² and empowerment related to self-management.^33-35 As a result of all of these benefits, clinical practice guidelines now recommend CGM counseling as part of general diabetes education for older adults with type 1 diabetes and type 2 diabetes on insulin.⁴

Compared with evidence focused on CGM, research behind AID use in older adult populations is more limited, but forthcoming.⁷ A pilot cross-over study that tested the impact of AID compared with sensor-augmented pump therapy among 15 older adults with type 1 diabetes ≥65 years found that use of the AID system over four weeks improved TIR by approximately 10%, in addition to reducing time below range (TBR, <70 mg/dL) modestly (about 0.4%). Importantly, older adults reported high ease of use, trust, and usability of the AID system, and use was associated with a reduction in diabetes distress.³⁶ The ORACL study, which included 30 adults aged 60 years or older living with type 1 diabetes for 10 or more years, found that older adults using AID systems spent less time in hypoglycemic ranges overnight and had increased TIR compared with those using sensor-augmented pump therapy.³⁷ Protocols aimed at exploring further benefits and additional outcomes of AID system use in more medically complex and older adult populations are continuing to be introduced.

Promising efficacy data from clinical trials focused on CGM and AID have paved the way for a new era of older adults using technology,⁷ and increased additions of technology-focused recommendations and education practices in diabetes standard of care journals provide optimism for increased adoption of evidence-based technology by older adults.^4,38 Yet, despite increases over time, the use of diabetes technologies by older adults remains low, especially when compared with younger age groups.²⁸ Data from the Diabetes Prospective Follow-up (DPV) Registry on more than 6000 older adults (≥60 years) in Germany, Austria, Luxembourg, and Switzerland found that CGM use had increased for both older people with type 1 diabetes (from 28% in 2019 to 45% in 2021) and type 2 diabetes (from 10% in 2019 to 18% in 2021).³⁹ The US-based data also reflect an increase in CGM use, particularly among older people with type 1 diabetes, with 27% of adults over 60 with type 1 diabetes and 3% of adults with type 2 diabetes using a CGM in 2021.⁴⁰ Data on the prevalence of AID use are more limited, but existing studies suggest low adoption that varies significantly between countries.⁴¹

In the DPV, key predictors of CGM use were insulin pump use and younger age in older adults with type 1 diabetes and younger age, higher body mass index (BMI), insulin pump use, and longer diabetes duration in those with type 2 diabetes. These factors have been reinforced in US-based studies, alongside age-related physical and cognitive factors.^41,42

The gaps in adoption of CGM and AID by older adults likely reflect multiple barriers, including age-related factors and larger systemic health disparities. One barrier is the various comorbidities and geriatric syndromes of individuals within the older adult population, which can affect the self-care capabilities of affected older adults and limit their ability to adopt new care management strategies including technology.⁴³ For example, progressive hearing impairment, commonly seen in elderly patients, has been found to hinder a patient’s ability to hear alerts and alarms from CGM systems, thus impacting the overall optimal use of the device.²⁸ A mixed-methods feasibility study documented that individuals with increasing levels of memory problems or dementia have further limitations that make CGM implementation and adoption more challenging.⁴⁴ That older age emerges consistently as a predictor of not using technology is compelling,^45,46 and an open research question is thus how to best implement CGM technology in older adults with frailty, cognitive impairment, dexterity and mobility issues, loss of hearing and vision, or other late-stage diabetes complications.

Just as in younger populations, structural health inequities likely also limit access to technology for minority populations and individuals adversely impacted by social determinants of health.^45,46 Health disparities affecting technology use run at multiple levels and may compound for older adults from ethnic minority groups and lower income groups, who have significantly less internet usage than older adults from white and wealthy backgrounds.⁴⁷ It remains unseen how expansions in insurance coverage for CGM may help to address disparate technology use patterns, and more research is needed to understand the perceptions of diabetes technologies and key barriers across more diverse groups of older adults to inform strategies for increased access.

Even when older adults can complete the process of diabetes technology adoption, additional barriers exist that create challenges in technology adherence and optimization. It has been shown that the complexity of the technology is a major reason for increased hesitancy in older adults to adopt new technology due to the underlying fears of unfamiliarity.⁴⁸ Qualitative studies have revealed that to learn CGM is a complex process,³⁴ and different older adults have highly individualized needs for support to reap the clinical and quality of life benefits of CGM.³⁵ The abundance of data and multicomponent nature of both CGM and AID may be overwhelming for older adults, especially when likely dealing with other medical complexity.³⁴

In summary, although the utilization of technology has been shown to improve glycemic management and safety among older adults with type 1 diabetes and insulin-treated type 2 diabetes, multisector work is still required to address barriers and increase adoption, such that these benefits can be realized across more diverse and medically complex populations of older adults in the “real world.” The existing evidence reveals the promise of these technologies to address the immense challenges in diabetes care in older adulthood, but it also points to multiple barriers hindering technological adoption; now, solutions are needed to address these barriers.

Diabetes Technology Use During Pregnancy

Diabetes in pregnancy is associated with an increased risk of neonatal and maternal complications. Managing diabetes in pregnancy is especially challenging due to the increased risk of hypoglycemia in early pregnancy and insulin resistance and delayed insulin absorption later in pregnancy.⁴⁹ In addition, pregnancy CGM targets are intensive and include at least 70% time in target range for pregnancy (TIRp, 63-140 mg/dL), less than 4% TBR for pregnancy (TBRp, <63 mg/dL), less than 25% time above range for pregnancy (TARp, >140 mg/dL) along with HbA_1c targets <6.5% preconception and <6% throughout pregnancy, if they can be safely attained.^50,51 Despite the generally high motivation in this population, studies suggest only 50% of people in pregnancy reach the recommended glucose targets.^49,52,53 The tighter targets in pregnancy, along with potential risk of complications, can lead to increased anxiety and impairment of psychologic health and quality of life.⁵⁴

Therefore, there is great interest in technologies that could help people in pregnancy reach glycemic targets, reduce the risk of maternal and fetal complications, and decrease the psychological burden. In the landmark CONCEPTT trial, the use of CGM technology demonstrated less hypoglycemia, lower HbA_1c, lower rates of maternal fetal and maternal outcomes, and improved treatment satisfaction compared with FSBG monitoring alone in pregnant people with type 1 diabetes.⁵⁵

The use of AID has demonstrated reductions in hypoglycemia and improvements in TIR in some pregnancy studies.^56,57 However, most systems are not designed to meet the more intensive pregnancy targets, especially overnight fasting glucose <95 mg/dL.^58,59 Despite suboptimal adjustable settings, AID systems are often used in clinical practice due to their perceived ability to achieve higher TIR with less user burden compared with MDI or an insulin pump programmed independently of a CGM device.⁶⁰ Owing to the many factors that impact glucose, dynamic insulin delivery based on CGM can reduce the burden of frequently changing insulin pump settings, needing extended boluses, temporary basals, or manually suspending.

For example, the CRISTAL study⁶¹ was a randomized controlled trial (RCT) that included 95 pregnant participants. Mean baseline HbA_1c was 6.5%. The group using AID with Medtronic 780G system had similar TIRp as the control group, which predominantly (81%) used sensor-augmented pump therapy (66.5% vs 63.2%, P = .17). The 780G group had greater overnight TIRp, reduced TBRp, and improved treatment satisfaction assessed by the DTSQ treatment satisfaction and change satisfaction surveys. However, there were no significant differences for other PROMs. Of note, the lowest programmable target can be set for 100 mg/dL. Additional carbohydrates were entered by 61% of participants in the 780G group, starting on average at 19 weeks. This was likely needed because the algorithm can limit the bolus despite more intensive carbohydrate ratios, which can be overcome by additional carbohydrate entries.

There is one algorithm that is approved for use in the United States, Australia, and Europe in pregnancy, called CamAPS FX, and this was studied in the AiDAPT trial. In this RCT,⁵⁶ 124 participants with a mean baseline HbA_1c 7.7 ± 1.2% were randomized to AID or standard care with MDI or insulin pump. The primary outcome, percent TIRp, was 68.2 ± 10.5% in the AID group and 55.6 ± 12.5% in the control group, with an adjusted difference of 10.5% (95% confidence interval [CI] = 7.0-14.0; P < .001). The AiDAPT trial had a larger proportion of control group participants using MDI than the CRISTAL study (59% vs 19%), potentially explaining the larger TIRp differences observed. There were several surveys administered, which demonstrated slightly better health-related quality of life in the AID group; however, both groups declined throughout pregnancy. Qualitatively, participants reported taking several weeks to adjust to the system before building trust and confidence, but finding it led to better and more timely health care team input.⁶² Other metrics including diabetes distress, hypoglycemia fear, and sleep quality did not differ between groups. Of note, the CamAPS FX has the lowest programmable target of any approved system (80 mg/dL); however, it only works on an Android phone, which may limit accessibility.

When it comes to psychosocial outcomes, the data are mixed. In a randomized, crossover trial of 16 pregnant women with type 1 diabetes, sensor-augmented pump therapy was compared with AID with an older version of the CamAPS system.⁶³ Semi-structured qualitative interviews showed that perceived benefits of the program included feelings of improved glucose control, less fear of hypoglycemia, mental freedom, and being intuitive. Perceived burdens included technical glitches, worry about CGM accuracy, and burden of maintenance requirements.⁶³ However, as CGM and AID systems continue to improve with accuracy and ease of use, the perceived benefits may outweigh perceived burdens. For example, in a case series of Tandem t:slim X2 with Control IQ users in pregnancy, the four users all achieved TIRp over 70% of the time and reported reduced diabetes management burden and improved sleep.⁶⁴

Guidelines vary on recommendations of AID use in pregnancy. The American Diabetes Association (ADA) recommends an individualized approach in choosing to use AID in pregnancy. An international consensus strongly considers recommending AID systems in pregnancy complicated with type 1 diabetes with a level C recommendation stating additional studies are needed to confirm safety and efficacy in this population.⁵⁸

Some practical recommendations for off-label use of AID systems have been developed which encourage examining glucose data and psychosocial burdens at each pregnancy visit. These recommend using the lowest programmable glucose setting and consider alternating with manual modes, especially overnight to reach pregnancy-recommended fasting glucose targets, or using sensor-augmented therapy with suspend on or before low as available. Manual boluses or fake carbs may be required with off-label use of systems.⁵⁹

Overall, most AID systems are not designed for pregnancy, and even with the one approved system, most users did not achieve 70% TIRp.⁵⁶ All currently published pregnancy data is in type 1 diabetes, so outcomes in type 2 diabetes in pregnancy are lacking. Additional research should also be done on the psychosocial impacts of these systems and how best to support pregnant patients through use. In the meantime, an individualized approach with these systems is needed in terms of patient selection and user settings.

Diabetes Technology Use and Psychiatric Conditions

Research indicates that the prevalence for a wide range of psychiatric disorders is higher in both adults and youth with type 1 diabetes and type 2 diabetes.^65-67 Research also indicates, not surprisingly, that the presence of a psychiatric disorder tends to have a disruptive impact on self-management of diabetes and can lead to undesirable medical outcomes.⁶⁸ In spite of this negative synergy, very little research has focused on the ways in which mental health problems affect and interact with the use of diabetes technologies. In part, this is likely due to the relative “newness” of some diabetes technologies such as CGM and AID systems. Indeed, more research has focused on Continuous Subcutaneous Insulin Infusion (CSII), a technology that has been used in diabetes self-care for more than 40 years. This section will review studies that shed light on the extent of diabetes technology use by individuals who have a psychiatric comorbidity, the positive and negative influences technology can have on diabetes management and emotional well-being for these groups, and some of the unique challenges faced by individuals.

One of the largest studies of CSII use by individuals with both type 1 diabetes and a psychiatric diagnosis analyzed data from the German/Austrian patient registry of >48 000 individuals.⁶⁹ The study documented a psychiatric comorbidity for 6.5% of the sample (n = 3158) representing a broad range of disorders including depression, attention-deficit hyperactivity disorder (ADHD), eating disorders (EDs), needle phobia, anxiety or obsessive-compulsive disorder (OCD), and a psychotic syndrome. The CSII use varied greatly across these groups, with the highest rates in individuals with needle phobia (75.8%), depression (41.5%), and anxiety/OCD (41.4%) which were all significantly higher than the reference group with no psychiatric diagnosis or treatment. For individuals with EDs and ADHD, CSII usage was equivalent to controls without psychiatric disease and, for those with psychotic disorders, use was significantly lower. Thus, it appears that CSII use is common in individuals with psychiatric comorbidities, with high rates of usage for some groups. Only those with a psychotic disorder used insulin pumps at significantly lower rates than controls.

There are no equivalent large-scale studies for CGM, although one study assessed the presence of clinically significant levels of anxiety and depression in a group of 196 CGM users.⁷⁰ A total of 40% and 15% of these CGM users showed clinical levels of anxiety and depression, respectively, providing more evidence that the rate of diabetes technology use is high in individuals living with both type 1 diabetes and mental health problems.

There has been considerably more research into the use of diabetes technologies in individuals with ED, especially CSII, because of the documented high rates of insulin restriction and underdosing to control weight. In one study, 18.5% of boys and 20.5% of girls reported engaging in this behavior at least three times per week.⁷¹ Although most studies focus on adolescents and young adults, it is important to note that insulin restriction is not uncommon in adults. There are reasons to predict lower CSII use in people with ED, secondary to possible weight gain from better insulin delivery and body image issues with wearing the device; however, research has found equivalent rates for individuals with and without ED.⁶⁹

There is also evidence that CSII use may have a positive effect on ED.⁷² A cross-sectional comparison of adolescents on CSII vs MDI found that insulin restriction was reported by 15% of the MDI users and by none of the CSII users.⁷³ A study of adults with type 1 diabetes at high risk for ED also found that those on MDI omitted insulin significantly more often than those using CSII.⁷⁴ The authors attributed the absence of insulin restriction in the CSII group to the increased flexibility in eating that this technology offers compared with MDI, which may help to normalize eating patterns and avoid restrained eating with subsequent relapse to disinhibited overeating.⁷⁵ It is important to point out, however, that not all studies find these positive relationships,⁷² and there are clear indications for caution and assessment when recommending CSII for individuals who engage in purging and/or binge eating.^76,77

Very few studies have examined the impact of CGM use for people with ED, but early findings indicate that it too could have positive effects for some. A blinded CGM study documented the tendency for some individuals to engage in “disinhibited” eating when they believe their blood glucose is low.⁷⁸ This overeating beyond the food needed to recover glucose can be followed by feelings of guilt and insulin restrictions. These authors suggested that research should investigate whether CGM feedback, including alarms, might reduce over-treatment of hypoglycemia, and subsequent purging behaviors. The CGM has also been suggested as an assessment tool to identify ED.⁷⁹ The CGM recorded glucose data for adolescents at higher risk for ED show increased time in hyperglycemia than those at lower risk, which can reflect insulin restriction.

With the exception of ED, few studies have examined the impact of diabetes technologies on a discrete psychiatric diagnosis, which is important to do given the broad range of conditions and symptoms that characterize different disorders. An exception is Autism Spectrum Disorder (ASD), a neurodevelopmental syndrome with pervasive emotional and behavioral impairments, which has received recent scientific attention. The ASD appears to occur with equal frequency in people with and without type 1 diabetes,⁸⁰ and a recent survey found no differences in CGM use rates between individuals with and without ASD. Pump use, however, was less common in the ASD group.

A recent qualitative study by one of the authors (AFMBB) interviewed 24 caretakers of youth with ASD about the impact of this disorder on diabetes management,⁸¹ including the use of technology. Almost all of the youth in this study were using both CGM devices and insulin pumps and, in general, had relatively low HbA_1c levels, likely related to the high level of involvement caretakers exerted. Overall, the caretakers had high levels of enthusiasm for both technologies. However, they also described challenges with their child’s adaptation to and acceptance of both devices, mostly related to the child’s abnormal skin hypersensitivity, a common symptom in ASD, which caused significant resistance to cannula and sensor insertion. Caretakers had devised creative ways of coping with this problem, including developing their own “desensitization” techniques to increase their child’s tolerance for skin contact with the apparatus. This type of in-depth investigation into how families cope with optimizing diabetes technology use in the face of psychiatric syndromes can provide invaluable insights into obstacles and methods for surmounting these.

In summary, although the use of diabetes technologies in individuals with psychiatric conditions is quite common, there is very little research into the unique challenges these individuals may face, and what types of support are needed to facilitate onboarding and long-term usage. The impact of a psychiatric condition on diabetes management can vary greatly, not only across different diagnoses but also within diagnostic categories, because of wide variations in individual symptoms and syndrome severity. In spite of the complexities, from a precision medicine perspective, it is critical to include the presence of a psychiatric disorder in the development of an individual’s diabetes treatment plan,⁸² just as other medical comorbidities need to be considered. For individuals with psychiatric disorders, ideally there is a line of communication and cooperation between the diabetes and the mental health care teams. Unfortunately, the diabetes health care practitioner may sometimes not even be aware of mental health issues because individuals often withhold this information secondary to concerns about stigmatization. This is one reason why routine psychiatric screening is so important in diabetes clinics, with periodic re-screening especially when major changes occur in the treatment plan, including the introduction of diabetes technologies.

In-Hospital Diabetes Technology Use

Rapid outpatient adoption of diabetes technologies, including CGM and AID systems, has resulted in rising numbers of patients presenting to hospital care settings wearing these devices.⁸³ Clinicians must then decide whether it is appropriate to continue the devices during a hospitalization, either as stand-alone CGM or as an AID with algorithm-assisted insulin delivery.⁸⁴

Inpatient studies on diabetes-related technologies now include a variety of patient populations and clinical scenarios, including critical care areas with the use of real-time CGM. In an RCT by Fortmann et al,⁸⁵ CGM significantly reduced time spent >250 mg/dL, whereas in another RCT, Spanakis et al⁸⁶ found that CGM reduced reoccurrence of hypoglycemia episodes. However, despite some promising results, these studies, along with two additional RCTs,^87,88 did not consistently demonstrate superiority in key glycemic indices, such as CGM mean and time in target range.

Nonetheless, the Food and Drug Administration’s non-objection to emergency use of CGM during the pandemic increased inpatient CGM use.⁸⁹ Since then, many health systems have incorporated CGMs into routine inpatient care, supported by expert groups endorsing their potential to improve hospital care.⁹⁰ Diabetes technologies like CGM may improve efficiencies and reduce nursing care burden,^91-93 particularly as point-of-care (POC) FSBG glucose testing is arduous and time-consuming and often results in insufficiently frequent and mistimed testing.⁹⁴ A recent analysis evaluating nursing perspectives on inpatient CGM showed over 90% of nurses felt CGM reduced their workload, improved patient care, and made their patients safer.⁹⁵ In addition, CGM benefits patients by minimizing painful and disruptive POC testing.^93,96 For instance, in critical care settings, frequent POC testing can disrupt sleep. Proactive interventions to reduce sleep disruptions are recommended by the Society of Critical Care Medicine⁴⁷ as sleep disruptions are strongly associated with patient delirium.⁹⁷ Although CGM holds promise in improving inpatient glycemic outcomes, patient satisfaction, and nursing workload, implementation presents challenges. Training and education for nurses and hospital staff could initially increase glucose management burdens.^92,95 In addition, integrating CGM alerts into hospital systems, without sufficient implementation strategies, could increase alarm fatigue, leading to nursing unresponsiveness and desensitization.⁹⁸

Combining CGM with algorithm-assisted insulin delivery through an AID system in the hospital has yielded more promising glycemic outcomes. The CamAPS HX AID system, approved for inpatient use in Europe for type 2 diabetes, increased time in target range (100–180 mg/dL) by 20% to 32% and lowered mean glucose by 22 to 52 mg/dL compared with usual care in four RCTs in the non-ICU setting.^99-102 In addition, in a single-arm pilot by Davis et al,¹⁰³ the Omnipod 5 AID system achieved 68% TIR (70-180 mg/dL) among non-ICU patients. Looking at hospital staff burdens associated with the CamAPS HX system perioperatively, a time-motion study found that although AID initialization took longer than standard MDI therapy (32.0 vs 12.7 minutes), subsequent management tasks took 2.1 to 4.5 times less time with AID compared with MDI.¹⁰⁴

Psychosocial and other perceptual aspects of inpatient AID use are largely unexplored, with research mainly limited to patient satisfaction surveys from the aforementioned trials. In the three RCTs testing the AID CamAPS HX prototype, most participants reported greater-than-expected satisfaction with AID and CGM.^99-101 Of 99 patients across these studies, only one indicated they would not recommend the system to friends or family.^99-101 In the Omnipod 5 pilot by Davis et al,¹⁰³ all 16 survey respondents liked using the system for glucose management, but through open-ended feedback, also highlighted several areas for device improvements. Although these results are promising, there is a significant need for more qualitative research to explore patient-related outcomes and opinions regarding inpatient AID, as well as the opinions of bedside nurses, treatment team members, and other stakeholders.

Currently, expert groups recommend continuation of outpatient CGM and AID systems during hospitalizations for cognizant people with diabetes who can continue safe management within a supervised environment.^4,105,106 However, the use of personal devices in the inpatient setting presents challenges. Although hospitalized patients typically prefer this approach,¹⁰⁷ it is essential that health systems have policies in place to ensure patient safety.¹⁰⁵ Some patients may be overwhelmed by the responsibility of device self-management during acute illness, whereas others may lack essential AID device skills.¹⁰⁸ Whenever possible, hospital teams should perform device and patient evaluation, and assist individuals who prefer to continue AID during the hospitalization.¹⁰⁵

In summary, the use of diabetes technologies like CGM and AID in inpatient settings is growing, showing early promise in improving glycemic outcomes and reducing glucose management burdens for both patients and health care personnel. However, far more extensive research is needed to fully understand the psychosocial, behavioral, and satisfaction-related impacts of introducing AID in the inpatient setting, ensuring its success and positive effect across stakeholders.

Conclusion

The expansion of diabetes technologies to new populations presents opportunities to improve not only clinical outcomes but also psychosocial well-being, self-management behaviors, and treatment satisfaction. However, the success of these technologies depends on addressing specific barriers unique to each population, providing adequate support, and conducting targeted research both to explore and mitigate challenges. Ensuring equitable access, user-friendly designs, comprehensive education, and personalized support are essential for maximizing the benefits of diabetes technologies to all.

Footnotes

Abbreviations

ADA: American Diabetes Association; AID: Automated Insulin Delivery; ADDQOL-19: Audit of Diabetes Dependent Quality of Life 19 questionnaire; BGM: Blood Glucose Monitoring; CGM: Continuous Glucose Monitoring; CSII: Continuous Subcutaneous Insulin Infusion; DDS: Diabetes Distress Scale; DIDP: DAWN2 Impact of Diabetes Profile; DQOL: Diabetes Quality of Life; DREB: Device-Related Emotional Burden; DRT: Device-Related Trust; DTSQ: Diabetes Treatment Satisfaction Questionnaire; EQ5D-5L: Euro Quality of Life 5 Dimension questionnaire; FSBG: Fingerstick Blood Glucose; GLP-1 RA: Glucagon-like Peptide 1 Receptor Agonists; HABS: Hypoglycemia Attitude and Behavior Scale; HbA_1c: Hemoglobin A1c; HCS: Hypoglycemia Confidence Scale; HFS: Hypoglycemic Fear Scale; IDSS-T2D: Insulin Device Satisfaction Survey for Type 2 Diabetes; MDI: Multiple Daily Insulin Injections; OAD: Oral Antidiabetic Agents; PAID: Problem Areas in Diabetes; RCT: Randomized Controlled Trial; SGLT2i: Sodium-glucose Cotransporter-2 Inhibitors; TIR: Time in Range; TBR: Time Below Range; WHO-5: World Health Organization-5

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MSH has consulted for Dexcom and has received research support through his institution from Insulet, Medtronic, and Tandem. GA has received consultant fees from Dexcom and Insulet and research support through her employer, Northwestern University, from Bayer, Fractyl Health, Insulet, MannKind, and Tandem Diabetes. LB has received product and research support for investigator-initiated studies from Dexcom and Ypsomed, in addition to speaker honoraria from Dexcom and Ypsomed. SAB has received grant support through her institution from Dexcom, Insulet, Roche Diagnostics, Tandem Diabetes Care, and Tolerion and has participated on a DSMB for MannKind. ERF has received grant support from Dexcom and Insulet and has served as a consultant for Dexcom. DI has been a speaker for Abbott Diabetes Care, Dexcom, Eli Lilly, and MannKind Corporation and has consulted for Eli Lilly, Insulet Corporation, Medtronic, and Novo Nordisk. In partnership with the University of Virginia, LAG-F manages fees for licenses for use of the Hypoglycemia Fear Survey (HFS-II) by for-profit organizations, with part of these fees used to support ongoing research and education related to fear of hypoglycemia in people with diabetes and their families. WHP has served as a consultant for Insulet. MMS has received grant support through her institution from Dexcom, Tandem, and Insulet.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: MSH receives support through the National Institute of Diabetes and Digestive and Kidney Diseases (grants 1K23DK138267 and P30DK116074). ERF receives support through the National Institute of Nursing Research (grant K23NR020051). ARK receives support through the National Institute on Aging, National Institutes of Health (grant K01AG084971). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

ORCID iDs

Michael S. Hughes

Grazia Aleppo

Lia Bally

Sue Brown

Eileen R. Faulds

Linda Gonder-Frederick

Anna R. Kahkoska

Jacob Ortega

William H. Polonsky

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Diabetes Technology Use in Special Populations: A Narrative Review of Psychosocial Factors

Abstract

Keywords

Introduction

Psychosocial Aspects of Diabetes Technology Usage in People With Type 2 Diabetes

Diabetes Technology Use in Older Adult Populations

Diabetes Technology Use During Pregnancy

Diabetes Technology Use and Psychiatric Conditions

In-Hospital Diabetes Technology Use

Conclusion

Footnotes

Abbreviations

Declaration of Conflicting Interests

Funding

ORCID iDs

References