Abstract
Introduction
This year, we screened 2640 potentially eligible titles related to exercise and diabetes, as well as 4639 titles related to nutrition and diabetes that were published between July 1, 2023, and June 30, 2024. Various search strategies, including PubMed and Google Scholar, were used to capture all relevant manuscripts. We shortlisted 85 original peer-reviewed manuscripts that focused on “exercise” and/or “nutrition” in diabetes mellitus or diabetes prevention. In our final selection of the 11 abstracts highlighted below, we grouped them into three main categories: (1) lifestyle interventions that delay type 2 diabetes and/or target diabetes-related complications, (2) physical activity and glycemic outcomes in type 1 diabetes, and (3) wearable technologies and algorithm developments for activity-associated hypoglycemia prevention. Overall, these manuscripts emphasize the importance of how exercise and/or nutrition influence disease development and/or its progression and management.
Influence of a Diet and/or Exercise Intervention on Long-Term Mortality and Vascular Complications in People with Impaired Glucose Tolerance: Da Qing Diabetes Prevention Outcome Study
Yu L1, Wang J2, Gong Q3, An Y3, Chen F1, Chen Y3, Chen X1, He S3, Qian X3, Chen B4, Dong F5, Li H2, Zhao F1, Zhang B1, Li G1,3; for the Da Qing Diabetes Prevention Study Group
1Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China; 2Department of Cardiology, Da Qing First Hospital, Da Qing, China; 3Center of Endocrinology, National Center of Cardiology & Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; 4Division of Non-Communicable Disease Control and Community Health, Chinese Center for Disease Control and Prevention, Beijing, China; 5Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
Diabetes Obes Metab 2024; 26: 1188–1196
We aimed to investigate the long-term influence of a diet and/or exercise intervention on long-term mortality and cardiovascular disease (CVD) events.
The Da Qing Diabetes Prevention Study had 576 participants with impaired glucose tolerance (IGT) randomized to diet-only, exercise-only, and diet-plus-exercise intervention group and control group. The participants underwent lifestyle interventions for 6 years. The subsequent Da Qing Diabetes Prevention Outcome Study was a prospective cohort study to follow-up the participants for up to 24 years after the end of 6-year intervention. In total, 540 participants completed the follow-up, while 36 subjects lost in follow-up. Cox proportional hazards analysis was applied to assess the influence of lifestyle interventions on targeted outcomes.
Compared with controls, the diet-only intervention in people with IGT was significantly associated with a reduced risk of all-cause death [hazard ratio (HR) 0.77, 95% confidence interval (CI) (0.61–0.97)], CVD death [HR 0.67, 95% CI (0.46–0.97)], and CVD events [HR 0.72, 95% CI (0.54–0.96)]. The diet-plus-exercise intervention was significantly associated with a decreased risk of all-cause death [HR 0.64, 95% CI (0.48–0.84)], CVD death [HR 0.54, 95% CI (0.30–0.97)], and CVD events [HR 0.68, 95% CI (0.52–0.90)]. Unexpectedly, the exercise-only intervention was not significantly associated with the reduction of any of these outcomes, although there was a consistent trend toward reduction.
A diet-only intervention and a diet-plus-exercise intervention in people with IGT were significantly associated with a reduced risk of all-cause death, CVD death, and CVD events, whereas an exercise-only intervention was not. It suggests that diet-related interventions may have a potentially more reliable influence on long-term vascular complications and mortality.
Numerous high quality randomized controlled trials (RCTs) have convincingly demonstrated that various dietary strategies that limit caloric intake, and/or modify food choices, and regular physical activity (PA) help to prevent or delay the onset of type 2 diabetes (1–3). In the Da Qing Diabetes Prevention Outcome Study (Da Qing DPOS) that began in 1986, it was shown that a caloric restriction diet alone (25–30 kcal/kg body weight per day; 55–65% calories from carbohydrate), regular PA alone (i.e., increased leisure-time PA by “1–2 units”; with one unit defined as 30 min mild, 20 min moderate, 10 min strenuous, or 5 min very strenuous PA), or combined diet plus PA could all effectively reduce diabetes risk by about 50% over a span of six years in individuals with impaired glucose tolerance (4). In a 20-year follow-up study, the three lifestyle intervention groups were pooled and compared to the “no lifestyle intervention group” to show a lifestyle-related reduction in the incidence of cardiovascular disease (CVD) events, microvascular disease, and early mortality risk (3). However, that study did not have a long enough follow up time to make clear comparisons between CVD and death rate within the three diverse types of lifestyle intervention. In other words, what lifestyle option might be best in the long run to keep us alive and free from CVD and microvascular disease? This more recent follow-up analysis highlighted here examined the 30-year follow up data to determine the long-term effects of the three separate lifestyle strategies (versus the control group) on the risk of CVD events, microvascular outcomes, all-cause mortality, and CVD mortality. Although the benefits of the PA-only intervention trended in the same direction as the other two types of intervention (diet only and diet plus PA), PA alone did not significantly attenuate all-cause death rate or CVD-associated death. By far, the best outcome for most metrics occurred in the combined diet plus PA intervention that was reinforced over the six-year study protocol. The combined lifestyle approach of diet plus exercise resulted in superior, long-lasting impacts, reducing the hazard ratio for CVD-related death by about 50% by the end of the 30-year follow-up. It appeared that over the six years, the combined lifestyle approach was enough to result in long-lasting behavior change, whereas the other two singular interventions may have been more difficult to sustain. In addition, the authors acknowledged that vigorous exercise might be risky for some older persons with underlying CVD, and this might explain why a little less activity with some focus on diet too might be a better prescription. This reminds us of something you might have heard before: “You can’t outrun a bad diet.” It looks like diet-only or diet-plus-PA interventions are the most effective strategies for the prevention of long-term vascular complications and early death in older individuals with type 2 diabetes or prediabetes.
Leisure-Time Physical Activity May Attenuate the Impact of Diabetes on Cognitive Decline in Middle-Aged and Older Adults: Findings from the ELSA-Brasil Study
Feter N1, de Paula D1, Dos Reis RCP1, Raichlen D2, Patrão AL3, Barreto SM4, Suemoto CK5, Duncan BB1, Schmidt MI1
1Post Graduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; 2Human and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA; 3Center for Psychology at University of Porto, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal; 4Department of Preventive and Social Medicine, Faculdade de Medicina and Clinical Hospital/Empresa Brasileira de Serviços Hospitalares, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; 5Division of Geriatrics, University of São Paulo Medical School, São Paulo, Brazil
Diabetes Care 2024; 47: 427–434
The objective of this study is to assess leisure-time physical activity (LTPA) as a modifier of the diabetes/cognitive decline association in middle-aged and older participants in the Estudo Longitudinal de Saude do Adulto (ELSA-Brasil) study.
ELSA-Brasil is a cohort of 15,105 participants (age 35–74 years) enrolled between 2008 and 2010. We evaluated global cognitive function, summing the scores of six standardized tests evaluating memory and verbal fluency, including the Trail-Making Test, at baseline and follow-up. Incident cognitive impairment was defined as a global cognitive function score at follow-up lower than −1 SD from baseline mean. Participants reporting ≥150 min/week of moderate to vigorous LTPA at baseline were classified as physically active. We assessed the association of LTPA with global cognition change in those with diabetes in the context of our overall sample through multivariable regression models.
Participants' (N = 12,214) mean age at baseline was 51.4 (SD 8.8) years, and 55.5% were women. During a mean follow-up of 8.1 (SD 0.6) years, 9345 (76.5%) inactive participants and 1731 (14.1%) participants with diabetes at baseline experienced faster declines in global cognition than those who were active (β −0.003, −0.004, and −0.002) and those without diabetes (β = −0.004, −0.005, and −0.003), respectively. Diabetes increased the risk of cognitive impairment (hazard ratio [HR] 1.71; 95% Cl 1.22, 2.39) in inactive but not in active adults (HR 1.18; 95% CI 0.73, 1.90). Among participants with diabetes, those who were active showed a delay of 2.73 (95% CI 0.94, 4.51) years in the onset of cognitive impairment.
In adults living with diabetes, LTPA attenuated the deleterious association between diabetes and cognitive function.
As noted above, it is fairly well established that regular physical activity (PA) and a number of dietary strategies that include some caloric restriction can effectively delay the onset of type 2 diabetes. However, less is known about how age-related cognitive decline in diabetes might be influenced by lifestyle interventions. Both type 1 diabetes and type 2 diabetes increase the risk for early cognitive decline and dementia through mechanisms of long-term exposure to recurrent dysglycemia/hyperglycemia and early vascular disease (5). However, the effects of lifestyle on limiting age-related decline in cognitive function in diabetes is a little less clear based on one recent meta-analysis (6). This large Brazilian study of over 12,000 participants studied over eight years reported that having diabetes and being physically inactive were both associated with faster cognitive decline, higher risk of cognitive impairment, and earlier onset of cognitive impairment. In fact, cognitive impairment was ∼2x more prevalent in those with diabetes as compared to those without diabetes after about the age of 50 years. The research clearly showed that physical inactivity was strongly associated with impaired cognitive function regardless of diabetes status and that being active with diabetes could attenuate the age-related decline in Global Cognition Scores. The researchers categorized being active as meeting or achieving 150 minutes per week of PA as measured by self-report, which we know can overestimate true PA levels. Although a powerful study, we wish that the authors could have examined PA more objectively, using PA monitors perhaps, but that would have been impossible for such a large study that has been ongoing since 2008. Moreover, it would be nice to quantify and report PA as a continuous variable, perhaps by using weekly metabolic equivalents (METS), since we acknowledge that the ideal PA prescription to enhance cognitive function is currently unclear (6). Nonetheless, the news is that meeting the current American Diabetes Association PA guidelines of 150 minutes per week appears to delay the onset of cognitive impairment in diabetes by about 2.7 years, according to this research. This finding fits with a recent meta-analysis demonstrating that weekly PA levels have an inverse linear, quadratic, and/or cubic inverse association with all-cause dementia and vascular dementia in aging adults without diabetes (7). Future studies should aim to better understand the dose-response relationship between weekly PA amount, type, and intensity on cognitive function in aging adults living with diabetes.
Comparative Evaluation of a Low-Carbohydrate Diet and a Mediterranean Diet in Overweight/Obese Patients with Type 2 Diabetes Mellitus: A 16-Week Intervention Study
Currenti W1, Losavio F2, Quiete S3, Alanazi AM4, Messina G2, Polito R2, Ciolli F2, Zappalà RS1, Galvano F1, Cincione RI2
1Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy; 2Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy; 3Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy; 4Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
Nutrients 2023; 16: 95
The worldwide prevalence of type 2 diabetes mellitus (T2DM) and obesity has been steadily increasing over the past four decades, with projections indicating a significant rise in the number of affected individuals by 2045. Therapeutic interventions in T2DM aim to control blood glucose levels and reduce the risk of complications. Dietary and lifestyle modifications play a crucial role in the management of T2DM and obesity. Although conventional medical nutritional therapy (MNT) often promotes a high-carbohydrate, low-fat Mediterranean diet as an elective treatment, low-carbohydrate diets (LCDs), specifically those restricting carbohydrate intake to less than 130 g/day, have gained popularity due to their multifaceted benefits. Scientific research supports the efficacy of LCDs in improving glycemic control, weight loss, blood pressure, lipid profiles, and overall quality of life. However, sustaining these benefits over the long term remains challenging. This trial aimed to compare the effects of a Mediterranean diet vs. a low-carbohydrate diet (carbohydrate intake <130 g/day) on overweight/obese patients with T2DM over a 16-week period. The study will evaluate the differential effects of these diets on glycemic regulation, weight reduction, lipid profile, and cardiovascular risk factors.
The study population comprises 100 overweight/obese patients with poorly controlled T2DM. Anthropometric measurements, bioimpedance analysis, and blood chemistry assessments will be conducted at baseline and after the 16-week intervention period. Both dietary interventions were hypocaloric, with a focus on maintaining a 500 kcal/day energy deficit.
After 16 weeks, both diets had positive effects on various parameters, including weight loss, blood pressure, glucose control, lipid profile, and renal function. However, the low-carbohydrate diet appears to result in a greater reduction in BMI, blood pressure, waist circumference, glucose levels, lipid profiles, cardiovascular risk, renal markers, and overall metabolic parameters compared to the Mediterranean diet at the 16-week follow up.
These findings suggest that a low-carbohydrate diet may be more effective than a Mediterranean diet in promoting weight loss and improving various metabolic and cardiovascular risk factors in overweight/obese patients with T2DM. However, it is important to note that further research is needed to understand the clinical implications and long-term sustainability of these findings.
We discovered a bunch of great new papers on dietary interventions for people living with diabetes in this year’s search. Interventions with a Mediterranean diet (MD) were shown to be associated with reduced type 2 diabetes risk in one study (8), and a reduction in serum advanced glycation end products in individuals with type 2 diabetes with mildly decreased kidney function in another study (9). As far as technology-assisted dietary strategies are concerned, a new Australian, online, web-based study (called the T2Diet study) focused on type 2 diabetes and a low carbohydrate diet (LCD) strategy (50–100 g/day carbohydrate intake but with ad libitum consumption of nonstarchy vegetables). This study showed significantly improved glycemic and clinical outcomes in adults with type 2 diabetes (10). In another important diet-related publication, it was discovered that the sophisticated measurement of various plasma protein changes linked to weight loss in the DiRECT and DIADEM-I studies predicted the interventions observed cardiovascular benefits and improvements in cardiovascular fitness (11). Although any of the abovementioned studies could have been highlighted here in our chapter, we selected the above featured paper since it contrasts two of the more common trends in dietary interventions in diabetes—the MD and the LCD. On the one hand, the benefits of the MD appear to be linked to the consumption of fiber-rich foods, complex carbohydrates, monounsaturated fats, antioxidants, and anti-inflammatory compounds (12). On the other hand, the LCD appears to be beneficial for some because it can promote more weight loss (perhaps by causing more satiety and thus dietary restriction) and lower post meal glucose excursions (13). In the featured study, both MD and LCD resulted in a significant reduction in weight loss and improvements in blood pressure, glucose management, lipid profile, cardiovascular risk, and renal function after 16 weeks of treatment. This result may be attributable firstly to the marked caloric restriction induced by both diets (about 500 kcals per day) and the adherence to a structured food plan monitored by qualified personnel. We also note that the study was probably too small (n = 50 in each group with no control group) and too short (16 weeks) to determine long-term adherence success. However, the authors’ efforts should be celebrated since it was a great head-to-head dietary comparison. But we should still conclude that no one dietary strategy is universally superior. If you can’t follow it…it’s not likely to work!
Association of Timing of Moderate-to-Vigorous Physical Activity with Changes in Glycemic Control over 4 Years in Adults with Type 2 Diabetes from the Look AHEAD Trial
Qian J1,2, Xiao Q3, Walkup MP4, Coday M5, Erickson ML6, Unick J7, Jakicic JM8, Hu K1,2, Scheer FAJL1,2, Middelbeek RJW9; and the Look AHEAD Research Group
1Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA; 2Division of Sleep Medicine, Harvard Medical School, Boston, MA; 3Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX; 4School of Medicine, Wake Forest University, Winston-Salem, NC; 5Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN; 6Translational Research Institute, AdventHealth, Orlando, FL; 7Weight Control and Diabetes Research Center, Miriam Hospital, Providence, RI; 8Division of Physical Activity and Weight Management, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS; 9Joslin Diabetes Center, Harvard Medical School, Boston, MA
Diabetes Care 2023; 46: 1417–1424
We aimed to determine the association of the time-of-day of bout-related moderate-to-vigorous physical activity (bMVPA) with changes in glycemic control across 4 years in adults with overweight/obesity and type 2 diabetes.
Among 2416 participants (57% women; mean age, 59 years) with 7-day waist-worn accelerometry recording at year 1 or 4, we assigned bMVPA timing groups based on the participants' temporal distribution of bMVPA at year 1 and recategorized them at year 4. The time-varying exposure of bMVPA (≥10-min bout) timing was defined as ≥50% of bMVPA occurring during the same time period (morning, midday, afternoon, or evening), <50% of bMVPA in any time period (mixed), and ≤1 day with bMVPA per week (inactive).
HbA1c reduction at year 1 varied among bMVPA timing groups (P = 0.02), independent of weekly bMVPA volume and intensity. The afternoon group had the greatest HbA1c reduction versus inactive (−0.22% [95%CI −0.39%, −0.06%]), the magnitude of which was 30–50% larger than the other groups. The odds of discontinuation versus maintaining or initiating glucose-lowering medications at year 1 differed by bMVPA timing (P = 0.04). The afternoon group had the highest odds (odds ratio 2.13 [95% CI 1.29, 3.52]). For all the year-4 bMVPA timing groups, there were no significant changes in HbA1c between year 1 and 4.
bMVPA performed in the afternoon is associated with improvements in glycemic control in adults with diabetes, especially within the initial 12 months of an intervention. Experimental studies are needed to examine causality.
Is there an optimal time of day for exercise for people with diabetes? Well, it turns out, improving glycemic outcomes is not as simple as just hitting the gym. Although traditional recommendations suggest that exercise at any time of day is beneficial for glucose management, emerging evidence indicates that the timing of physical activity may significantly impact metabolic outcomes (14–17). Specifically, morning exercise may be advantageous for mitigating nocturnal hypoglycemia in individuals with type 1 diabetes, whereas afternoon exercise could offer improved glycemic outcomes in adults with type 2 diabetes (14). Qian et al. (15) from the Look AHEAD trial examined the relationship between the timing of moderate to vigorous physical activity (MVPA) and long-term glycemic outcomes in adults with type 2 diabetes. Over four years, researchers found that participants who performed MVPA in the afternoon had more pronounced reductions in HbA1c levels compared to those who exercised in the morning or evening. These findings highlight that the timing of exercise may in fact be one more key factor in optimizing glycemic outcomes in type 2 diabetes. Overall, based on this excellent study, MVPA in the afternoon may be the best form and time of day for glucose management. In a population-based prospective study of individuals aged 45–65 years, with an oversampling of individuals with overweight or obesity, it was also shown that MVPA in the afternoon or evening was associated with a reduction of up to 25% in whole body insulin resistance (18). Taken together, a consistent message is that tailoring exercise timing to each patient's lifestyle and needs is essential to ensure both effectiveness and adherence to treatment plans. But if timing is not an issue, afternoon might be a little better for those with pre-diabetes or type 2 diabetes. This growing body of evidence calls for a shift in how we approach exercise recommendations, emphasizing that when you work out might be just as important as how much you work out. Nonetheless, in most cases, we feel that all exercise is good exercise, at any time of day!
Strength Training Is More Effective than Aerobic Exercise for Improving Glycaemic Control and Body Composition in People with Normal-Weight Type 2 Diabetes: A Randomized Controlled Trial
Kobayashi Y1,2, Long J3, Dan S4, Johannsen NM5,6, Talamoa R7, Raghuram S7, Chung S8, Kent K3, Basina M9, Lamendola C1,8, Haddad F1,2, Leonard MB3, Church TS5,6,10, Palaniappan L8
1Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA; 2Stanford Cardiovascular Institute, Stanford, CA; 3Department of Pediatrics, Stanford University School of Medicine, Stanford, CA; 4Center for Asian Health Research and Education, Stanford University School of Medicine, Stanford, CA; 5Pennington Biomedical Research Center, Baton Rouge, LA; 6Louisiana State University, Baton Rouge, LA; 7Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA; 8Department of Medicine, Stanford University School of Medicine, Stanford, CA; 9Division of Endocrinology, Gerontology, and Metabolism, Stanford University School of Medicine, Stanford, CA; 10 Wondr Health, Dallas, TX
Diabetologia 2023; 66: 1897–1907
Type 2 diabetes in people in the healthy weight BMI category (<25 kg/m2), herein defined as “normal-weight type 2 diabetes,” is associated with sarcopenia (low muscle mass). Given this unique body composition, the optimal exercise regimen for this population is unknown.
We conducted a parallel-group RCT in individuals with type 2 diabetes (age 18–80 years, HbA1c 47.5–118.56 mmol/mol [6.5–13.0%], and BMI <25 kg/m2). Participants were recruited in outpatient clinics or through advertisements and randomly assigned to a 9-month exercise program of strength training alone (ST), aerobic training alone (AER), or both interventions combined (COMB). We used stratified block randomization with a randomly selected block size. Researchers and caregivers were blinded to participants' treatment group; however, participants themselves were not. Exercise interventions were conducted at community-based fitness centers. The primary outcome was absolute change in HbA1c level within and across the three groups at 3, 6, and 9 months. Secondary outcomes included changes in body composition at 9 months. Per adherence to recommended exercise protocol (PP) analysis included participants who completed at least 50% of the sessions.
Among 186 individuals (ST, n = 63; AER, n = 58; COMB, n = 65) analyzed, the median (IQR) age was 59 (53–66) years, 60% were men and 83% were Asian. The mean (SD) HbA1c level at baseline was 59.6 (13.1) mmol/mol (7.6% [1.2%]). In intention-to-treat analysis, the ST group showed a significant decrease in HbA1c levels (mean [95% CI] −0.44 percentage points [−0.78, −0.12], P = 0.002), while no significant change was observed in either the COMB group (−0.35 percentage points, P = 0.13) or the AER group (−0.24 percentage points, P = 0.10). The ST group had a greater improvement in HbA1c levels than the AER group (P = 0.01). Appendicular lean mass relative to fat mass increased only in the ST group (P = 0.0008), which was an independent predictor of HbA1c change (beta coefficient −7.16, P = 0.01). Similar results were observed in PP analysis. Only one adverse event, in the COMB group, was considered to be possibly associated with the exercise intervention.
In normal-weight type 2 diabetes, strength training was superior to aerobic training alone, whereas no significant difference was observed between strength training and combination training for HbA1c reduction. Increased lean mass relative to decreased fat mass was an independent predictor of reduction in HbA1c level.
Now that we’ve addressed the importance of the time of day for exercise (above), another key piece to the exercise puzzle is whether the type of exercise matters in relation to HbA1c outcomes? Kobayashi et al. (19) evaluated the efficacy of strength training alone versus aerobic exercise alone versus strength and aerobic exercise combined in managing glycemic outcomes and body composition in individuals with normal-weight type 2 diabetes. The study demonstrated that strength training significantly outperformed aerobic exercise in reducing HbA1c levels and improving body composition metrics, including fat mass and lean muscle mass. In addition, there were no significant differences observed between strength training and combination training for reductions in HbA1c (19). These findings suggest that strength training may be preferable as the exercise modality for optimizing several metabolic outcomes in this population, highlighting its potential as a key lifestyle management strategy. A letter in response to the Kobayashi et al. (19) publication highlighted some important considerations that are worth noting (20). Literature suggests that exercise interventions are dose dependent (i.e., the amount of exercise performed determines the magnitude of the benefit of the intervention). However, this was not controlled for in the study highlighted above. Finally, due to a substantial amount of missing data (>30% for the primary outcome of HbA1c, >50% for the secondary outcomes) that were excluded from the analysis, the overall conclusions may require further verification (21). Another randomized controlled trial published within the same search period that is worth us noting here evaluated the impact of home‐based resistance exercise training on HbA1c, as well as muscle strength and body composition, in people with type 2 diabetes (22). Interestingly, this study showed no obvious effect on HbA1c, but an increase in the number of push‐ups one could do in good form, led to clear gains in arm and leg lean mass and strength, along with significant decreases in liver fat content. The difference in findings between these studies might reflect how the resistance training was implemented. Home-based resistance training programs typically face many challenges related to adherence, intensity, and progression, which could limit its impact on HbA1c levels in type 2 diabetes. In contrast, structured strength training programs, whether supervised or not, may offer more consistent and intensive interventions in this patient population (23).
Exploring Factors that Influence Postexercise Glycemia in Youth with Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) Study
Sherr JL1, Bergford S2, Gal RL2, Clements MA3, Patton SR4, Calhoun P2, Beaulieu LC2, Riddell MC5
1Yale School of Medicine, New Haven, CT; 2Jaeb Center for Health Research, Tampa, FL; 3Children's Mercy Hospital, Kansas City, MO; 4Nemours Children's Health, Jacksonville, FL; 5Muscle Health Research Centre, School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada
Diabetes Care 2024; 47: 849–857
To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia.
This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected.
A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h.
In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines and decision support tools and refine insulin delivery algorithms to better support exercise in youth with diabetes.
For individuals with type 1 diabetes, glucose management during and after exercise is inherently complex. Over the past few years, we have been seeing more amazing publications from the Type 1 Diabetes Exercise Initiative (T1DEXI) than we can keep track of! T1DEXI is the largest, publicly-available database examining the effects of real-world exercise on glycemic management in both youth and adults with type 1 diabetes. The Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study investigated factors affecting postexercise glycemia in youth with type 1 diabetes. This study found that participants meeting exercise consensus guidelines (>60 minutes of total physical activity per day) versus participants with lower physical activity levels had a greater risk of nocturnal hypoglycemia (24). These increased rates of nocturnal hypoglycemia were also noted in individuals with lower HbA1c levels and when continuous glucose monitoring (CGM) time below range was >4% in the previous 24 hours. Therefore, effective strategies for insulin-dosing and carbohydrate intake are crucial for the management of glucose levels around exercise. These findings underscore the importance of individualized adjustments to insulin and nutrition that are based on specific exercise characteristics to optimize glycemia in youth with type 1 diabetes. Other published T1DEXI manuscripts report that factors such as the intensity and duration of exercise, starting glucose level, and timing of carbohydrate intake also significantly influence the risk of both hypoglycemia and hyperglycemia during and after exercise according to the T1DEXIP analytics (25, 26). With so many factors to consider, it can be extremely burdensome and overwhelming to navigate glucose management around exercise for youth with type 1 diabetes and their families. To reduce the risk of dysglycemia, one key takeaway for physically active adolescents with type 1 diabetes based on the reported data from this research team could be to initiate exercise when baseline glucose is between 130 and 160 mg/dL (7.2 and 8.9 mmol/L) and the CGM value is stable or slightly declining (25). Overall, a flood of confirmatory and new type 1 diabetes exercise data is coming from these and other real-world studies. Now, if only we could do something more with this information to help make exercise safer and more appealing for everyone with type 1 diabetes!
Impact of a 4-Week Intensive Track and Field Training Intervention on Glycaemia in Adolescents with Type 1 Diabetes: The ChilDFiT1 Study
Zimmer RT1, Birnbaumer P2, Sternad C3, Zunner BEM1, Schierbauer J1, Fritsch M4, Fröhlich-Reiterer E4, Hofmann P2, Sourij H3, Aberer F1,3, Moser O1,3
1Division of Exercise Physiology and Metabolism, BaySpo-Bayreuth Center of Sport Science, University Bayreuth, Bayreuth, Germany; 2Exercise Physiology, Training & Training Therapy Research Group, Institute of Human Movement Science, Sport and Health, University of Graz, Graz, Austria; 3Interdisciplinary Metabolic Medicine Trials Unit, Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria; 4Department of Pediatrics and Adolescent Medicine, Division of General Pediatrics, Medical University Graz, Graz, Austria
Diabetes Obes Metab 2024; 26: 631–641
This study aims to investigate the safety and efficacy of track and field training compared with intensification of insulin treatment only in adolescents with type 1 diabetes (T1D).
Eighteen adolescents (seven females) with T1D were included (age 15.1 ± 1.1 years, HbA1c 7.3% ± 1.0% [56.3 ± 10.9 mmol/mol]). After a 4-week observational control phase, participants were randomized to either stand-alone intensive glycaemic management (IT; telemedicine or on-site visits, three times/week) or additionally performed track and field exercise (EX; three 60-minute sessions/week) for 4 weeks. Glycemia was assessed via continuous glucose monitoring during observational control and intervention phases.
Time in range (70–180 mg/dL; 3.9–10.0 mmol/L) significantly improved from the observational control phase to the exercise intervention phase in EX (69% ± 13% vs. 72% ± 11%, P = .049), but not in IT (59% ± 22% vs. 62% ± 16%, P = .399). Time below range 1 (54–69 mg/dL; < 3.9 mmol/L) improved in IT (3.1% ± 1.9% vs. 2.0% ± 0.8%, P = .017) and remained stable in EX (2.0% ± 1.7 vs. 1.9% ± 1.1%, P = .999). The EX group's HbA1c ameliorated preintervention to postintervention (mean difference: ΔHbA1c −0.19% ± 0.17%, P = .042), which was not seen within the IT group (ΔHbA1c −0.16% ± 0.37%, P = .40). Glucose standard deviation was reduced significantly in EX (55 ± 11 vs. 51 ± 10 mg/dL [3.1 ± 0.6 vs. 2.8 ± 0.6 mmol/L], P = .011), but not in IT (70 ± 24 vs. 63 ± 18 mg/dL [3.9 ± 1.3 vs. 3.5 ± 1.0 mmol/L], P = .186).
Track and field training combined with intensive glycemic management improved glycemia in adolescents with T1D, which was not observed in the nonexercise group.
Regular physical activity is important for children and adolescents with any form of diabetes to improve numerous cardiometabolic and mental health outcomes (27). However, adequate support is needed for the child or adolescent with type 1 diabetes to help with activity-related hypo- and/or hyperglycemia. This study examined a novel 4-week intervention of intensive glycemic management (IT) camp with or without track and field training in adolescents with type 1 diabetes. The intensive training only intervention group underwent intensive glucose management education about three days per week over a 4-week period, while the exercise (EX) group also attended an in-person track and field training camp three days per week for the four weeks. Although the number of participants was small in each group (n = 10 for IT only and n = 7 completed for the EX group), HbA1c levels dropped 0.2 percentage points (from 7.0 to 6.8%) in the EX group (and glucose time in range improved) but remained unchanged significantly in the IT group (7.4 to 7.3%). Although the size of the study was small, and the investment of resources high (in person camps and online education three times per week over one month), the measured outcomes were positive. Similarly, the 4T Exercise program is a telehealth, structured exercise education program for newly diagnosed youth with type 1 diabetes (28). The 4T Exercise mixed methods study demonstrated that participants valued the practical guidance and strategies for glycemic management before, during, and after exercise. The integration of these educational tools with early continuous glucose monitoring (CGM) use allowed youth to resume sports and other activities with greater confidence in their ability to manage glucose levels and reduce the risk of hypoglycemia more effectively. As such, these studies emphasize the importance and need around exercise education to support youth with type 1 diabetes. In summary, the study highlighted above illustrates that real-life learning (i.e., sports camps) are beneficial for glycemic outcomes for youth with type 1 diabetes, not to mention the psychosocial benefits of an in-person (29–31), or virtual camp (32). We love camps for kids with type 1 diabetes!
High-Intensity Interval Training as a Novel Treatment for Impaired Awareness of Hypoglycemia in People with Type 1 Diabetes (HIT4HYPOS): A Randomized Parallel-Group Study
Farrell CM1, McNeilly AD1, Hapca S2, Fournier PA3, Jones TW3, Facchinetti A4, Cappon G4, West DJ5, McCrimmon RJ1
1Division of Systems Medicine, School of Medicine, University of Dundee, Dundee, UK; 2Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, UK; 3University of Western Australia, Perth, WA, Australia; 4Department of Information Engineering, University of Padova, Padova, Italy; 5Population Health Sciences Institute, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK
Diabetologia 2024; 67: 392–402
Impaired awareness of hypoglycemia (IAH) in type 1 diabetes may develop through a process referred to as habituation. Consistent with this, a single bout of high intensity interval exercise as a novel stress stimulus improves counterregulatory responses (CRR) to next-day hypoglycemia, referred to as dishabituation. This longitudinal pilot study investigated whether 4 weeks of high intensity interval training (HIIT) has sustained effects on counterregulatory and symptom responses to hypoglycemia in adults with type 1 diabetes and IAH.
HIT4HYPOS was a single-center, randomized, parallel-group study. Participants were identified using the Scottish Diabetes Research Network (SDRN) and from diabetes outpatient clinics in NHS Tayside, UK. The study took place at the Clinical Research Centre, Ninewells Hospital and Medical School, Dundee, UK. Participants were aged 18–55 years with type 1 diabetes of at least 5 years' duration and HbA1c levels <75 mmol/mol (<9%). They had IAH confirmed by a Gold score ≥4, modified Clarke score ≥4 or Dose Adjustment For Normal Eating [DAFNE] hypoglycemia awareness rating of 2 or 3, and/or evidence of recurrent hypoglycemia on flash glucose monitoring. Participants were randomly allocated using a web-based system to either 4 weeks of real-time continuous glucose monitoring (RT-CGM) or RT-CGM+HIIT. Participants and investigators were not masked to group assignment. The HIIT program was performed for 20 min on a stationary exercise bike three times a week. Hyperinsulinemic-hypoglycemic (2.5 mmol/l) clamp studies with assessment of symptoms, hormones, and cognitive function were performed at baseline and after 4 weeks of the study intervention. The predefined primary outcome was the difference in hypoglycemia-induced adrenaline (epinephrine) responses from baseline following RT-CGM or RT-CGM+HIIT.
Eighteen participants (nine men and nine women) with type 1 diabetes (median [IQR] duration 27 [18.75–32] years) and IAH were included, with nine participants randomized to each group. Data from all study participants were included in the analysis. During the 4-week intervention there were no significant mean (SEM) differences between RT-CGM and RT-CGM+HIIT in exposure to level 1 (28 [7] vs 22 [4] episodes, P = 0.45) or level 2 (9 [3] vs 4 [1] episodes, P = 0.29) hypoglycemia. The CGM-derived mean glucose level, SD of glucose and glucose management indicator (GMI) did not differ between groups. During the hyperinsulinemic-hypoglycemic clamp studies, mean (SEM) change from baseline was greater for the noradrenergic responses (RT-CGM vs RT-CGM+HIIT: −988 [447] vs 514 [732] pmol/l, P = 0.02) but not the adrenergic responses (−298 [687] vs 1,130 [747] pmol/l, P = 0.11) in those participants who had undergone RT-CGM+HIIT. There was a benefit of RT-CGM+HIIT for mean (SEM) change from baseline in the glucagon CRR to hypoglycemia (RT-CGM vs RT-CGM+HIIT: 1 [4] vs 16 [6] ng/l, P = 0.01). Consistent with the hormone response, the mean (SEM) symptomatic response to hypoglycemia (adjusted for baseline) was greater following RT-CGM+HIIT (RT-CGM vs RT-CGM+HIIT: −4 [2] vs 0 [2], P < 0.05).
In this pilot clinical trial in people with type 1 diabetes and IAH, we found continuing benefits of HIIT for overall hormonal and symptomatic CRR to subsequent hypoglycemia. Our findings also suggest that HIIT may improve the glucagon response to insulin-induced hypoglycemia.
Despite the integration of newer insulin analogues, numerous wearable technologies, and educational interventions, impaired awareness of hypoglycemia (IAH) occurs in approximately 25% of individuals living with type 1 diabetes (33). IAH clearly sets the stage for a catastrophic (severe) hypoglycemic event (34). The HIT4HYPOS study is a noteworthy 4-week study which explored the effects of integrating high-intensity interval training (HIIT) with real-time continuous glucose monitoring (RT-CGM) as an innovative longitudinal approach to “dishabituate” the body's response to hypoglycemia and improve the management of IAH versus a RT-CGM only control group (33). Although preliminary, the results are promising and demonstrate that HIIT can have a measurable impact on both physiological and symptomatic responses to hypoglycemia. The observed increase in glucagon and noradrenaline responses during hypoglycemic (clamped) events suggests that HIIT may help reverse the blunted hormonal responses that are characteristic of IAH, thereby enhancing safety and outcomes for this population of individuals with type 1 diabetes. However, it is important to note that the study did not find a significant difference in the frequency of hypoglycemic episodes between those using HIIT as an intervention and the control group. This implies that although HIIT may enhance the body’s neuroendocrine responses to hypoglycemia, it may not reduce the occurrence of level 1 or level 2 hypoglycemic events (33). Future studies should aim to validate these findings in larger, more diverse populations and explore the long-term sustainability of HIIT's benefits in managing IAH. Nonetheless, based on the findings of this pilot study, it can be cautiously suggested that HIIT could be a beneficial addition to type 1 diabetes management strategies, particularly for those with IAH. At least HIIT does not appear to increase hypoglycemia risk!
Interrupting Prolonged Sitting with Frequent Short Bouts of Light-Intensity Activity in People with Type 1 Diabetes Improves Glycemic Control without Increasing Hypoglycemia: The SIT-LESS Randomized Controlled Trial
Campbell MD1,2,3, Alobaid AM4,5, Hopkins M4, Dempsey PC3,6,7,8, Pearson SM2, Kietsiriroje N2,9, Churm R10, Ajjan RA2
1John Dawson Drug Discovery and Development Institute, University of Sunderland, Sunderland, UK; 2Leeds Institute of Cardiovascular and Metabolic Medicine, Faculty of Medicine, University of Leeds, Leeds, UK; 3Institute of Metabolic Science, University of Cambridge, Cambridge, UK; 4School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, UK; 5Ministry of Health, Farwaniya Hospital, Kuwait, Kuwait; 6Diabetes Research Centre, University of Leicester, Leicester, UK; 7Baker Heart and Diabetes Institute, Melbourne, Australia; 8Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia; 9Endocrinology and Metabolism Unit, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand; 10Applied Sports Technology, Exercise and Medicine (A-STEM) Research Centre, Faculty of Science and Engineering, Swansea University, Swansea, UK
Diabetes Obes Metab 2023; 25: 3589–3598
This study aims to examine the impact of interrupting prolonged sitting with frequent short bouts of light-intensity activity on glycemic control in people with type 1 diabetes (T1D).
In total, 32 inactive adults with T1D [aged 27.9 ± 4.7 years, 15 men, diabetes duration 16.0 ± 6.9 years and glycated hemoglobin 8.4 ± 1.4% (68 ± 2.3 mmol/mol)] underwent two 7-h experimental conditions in a randomized crossover fashion with >7-day washout consisting of: uninterrupted sitting (SIT), or interrupted sitting with 3-min bouts of self-paced walking at 30-min intervals (SIT-LESS). Standardized mixed-macronutrient meals were administered 3.5 h apart during each condition. Blinded continuous glucose monitoring captured interstitial glucose responses during the 7-h experimental period and for a further 48-h under free-living conditions.
SIT-LESS reduced total mean glucose (SIT 8.2 ± 2.6 vs. SIT-LESS 6.9 ± 1.7 mmol/L, P = .001) and increased time in range (3.9–10.0 mmol/L) by 13.7% (SIT 71.5 ± 9.5 vs. SIT-LESS 85.1 ± 7.1%, P = .002). Hyperglycemia (>10.0 mmol/L) was reduced by 15.0% under SIT-LESS (SIT 24.2 ± 10.8 vs. SIT-LESS 9.2 ± 6.4%, P = .002), whereas hypoglycemia exposure (<3.9 mmol/L) (SIT 4.6 ± 3.0 vs. SIT-LESS 6.0 ± 6.0%, P = .583) was comparable across conditions. SIT-LESS reduced glycemic variability (coefficient of variation %) by 7.8% across the observation window (P = .021). These findings were consistent when assessing discrete time periods, with SIT-LESS improving experimental and free-living postprandial, whole-day and night-time glycemic outcomes (P < .05).
Interrupting prolonged sitting with frequent short bouts of light-intensity activity improves acute postprandial and 48-h glycaemia in adults with T1D. This pragmatic strategy is an efficacious approach to reducing sedentariness and increasing physical activity levels without increasing risk of hypoglycemia in T1D.
The SIT-LESS study presents a novel approach to managing type 1 diabetes by shifting the focus from traditional physical activity recommendations, which emphasize moderate-to-vigorous-intensity exercise, to brief light-intensity activities (35). This approach addresses common activity barriers such as time constraints, physical limitations, and the fear of exercise-induced hypoglycemia (36), making the intervention both effective and sustainable for a broad range of individuals with type 1 diabetes. Unlike other exercise studies in type 1 diabetes, which sometimes focus on a more athletic or habitually active population, this study demonstrated that in otherwise inactive individuals with type 1 diabetes, breaking up sitting time with three minutes of walking every 30 minutes can significantly improve glycemic outcomes without increasing the risk of hypoglycemia (35). These findings align with growing evidence that modest increases in physical activity can yield glycemic benefits. For instance, another paper published in this same search period showed that individuals with type 1 diabetes have ∼2% higher 24-hour time in range (TIR: 70–180 mg/dL; 3.9–10.0 mmol/L) when they have higher steps per day (>7,000 steps per day) vs when they have lower step count days (<7,000 steps per day), albeit time below range also went up (37). Collectively, these studies suggest that daily engagement in short bouts of light-intensity activity could be a low-risk, high-reward strategy to improve glycemic outcomes for individuals living with type 1 diabetes.
A Randomized Controlled Trial Assessing the Impact of Continuous Glucose Monitoring with a Predictive Hypoglycemia Alert Function on Hypoglycemia in Physical Activity for People with Type 1 Diabetes (PACE)
Rilstone S1,2, Oliver N2, Godsland I2, Tanushi B2, Thomas M2, Hill N3
1Department of Nutrition & Dietetics, Imperial College Healthcare NHS Trust, London, UK; 2Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; 3Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK
Diabetes Technol Ther 2024; 26: 95–102
Uptake of exercise in people with type 1 diabetes (T1D) is low despite significant health benefits. Fear of hypoglycemia is the main barrier to exercise. Continuous glucose monitoring (CGM) with predictive alarms warning of impending hypoglycemia may improve self-management of diabetes around exercise.
This study aims to assess the impact of Dexcom G6 real-time CGM system with a predictive hypoglycemia alert function on the frequency, duration, and severity of hypoglycemia occurring during and after regular (≥150 min/week) physical activity in people with T1D.
After 10 days of blinded run-in (Baseline), CGM was unblinded and participants randomized 1:1 to have the “urgent low soon” (ULS) alert switched “on” or “off” for 40 days. Participants then switched alerts “off” or “on,” respectively, for a further 40 days. Physical activity and carbohydrate and insulin doses were recorded.
Twenty-four participants (8 men, 16 women) were randomized. There was no difference in change from baseline of hypoglycemia <3.0 and <3.9 mmol/L with the ULS on or off during the 24 h after exercise. With ULS alert “on” time spent below 2.8 mmol/L compared with baseline was significantly (P = 0.04) lower than with ULS “off” in the 24 h after exercise. In mixed effects regression, timing of the exercise and baseline HbA1c independently affected risk of hypoglycemia during exercise; exercise timing also affected hypoglycemia risk after exercise.
A CGM device with an ULS alert reduces exposure to hypoglycemia below 2.8 mmol/L overall and in the 24 h after exercise compared with a threshold alert.
Although the benefits of physical activity for people with type 1 diabetes are well-known, 67% of individuals with type 1 diabetes are not taking part in enough regular physical activity, with the fear of hypoglycemia being the main obstacle (38). Can using diabetes-related technologies be beneficial in helping reduce this barrier? Two studies demonstrate the various novel ways that continuous glucose monitoring (CGM) systems can be used to help promote activity and optimize glucose self-management strategies around exercise for individuals with type 1 diabetes. The PACE study implemented an Urgent Low Soon (ULS) alert in Dexcom G6 and G7 devices and investigated its impact on hypoglycemia. Participants in the PACE study had a 45% occurrence of a hypoglycemic event in the 24 hours after exercise, with 12% of them being below 50 mg/dL (2.8 mmol/L) (39). Although ULS alerts did not affect hypoglycemia 12 hours after physical activity, they reduced hypoglycemia below 50 mg/dL (2.8 mmol/L) (39). Using a ULS alert can be a useful tool for individuals with type 1 diabetes who refrain from exercise due to fear of hypoglycemia. Another study by Morrison et al. (40) investigated the difference between exercise and sedentary days with a novel metric of glycemic profiles in people with type 1 diabetes. The Glycemia Risk Index (GRI) provides a single numerical rating that weighs in glucose time below range (TBR) significantly, as compared to time in range (TIR) and time above range (TAR). Overall, this study demonstrated that GRI was improved on exercise days compared to sedentary days (mean [SD]; 29.9 [24.0] versus 34.0 [26.1], respectively) despite higher TBR (40), which shows how exercise can help people with type 1 diabetes achieve improved overall glycemic outcomes. Another helpful study finding for people with type 1 diabetes to reduce the risk of hypoglycemia is exercise timing in regard to their prandial state. A higher GRI was noted in postprandial versus postabsorptive exercise, which is reflective of increased TBR exposure (40). The study recommends that to reduce the risk of hypoglycemia, individuals with type 1 diabetes should avoid exercise (or perhaps be more cautious) within two hours of a meal bolus (40). Future studies can now focus on using novel metrics such as GRI and low alerts to study hypoglycemia around a variety of exercises and subpopulations of individuals with type 1 diabetes, especially those who have elevated TBR and are less physically active. This could help all individuals with type 1 diabetes become more comfortable in partaking in physical activity without an added fear of hypoglycemia.
Integrating Metabolic Expenditure Information from Wearable Fitness Sensors into an AI-Augmented Automated Insulin Delivery System: A Randomized Clinical Trial
Jacobs PG1, Resalat N1, Hilts W1, Young GM1, Leitschuh J1, Pinsonault J1, El Youssef J2, Branigan D2, Gabo V2, Eom J2, Ramsey K3, Dodier R1, Mosquera-Lopez C1, Wilson LM2, Castle JR2
1Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Center for Health and Healing, Oregon Health and Science University, Portland, OR; 2Harold Schnitzer Diabetes Health Center, Oregon Health and Science University, Portland, OR; 3Oregon Clinical and Translational Research Institute Biostatistics and Design Program, Oregon Health and Science University, Portland, OR
Lancet Digit Health 2023; 5: e607–e617
Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesized that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycemia during exercise and free-living conditions compared with an AID not automating use of fitness data.
Our study population comprised individuals (aged 21–50 years) with type 1 diabetes from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-center, two-arm randomized (4-block randomization), nonblinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and two free-living out-patient days. Primary outcome was time below range (<3.9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3.9–10 mmol/L).
Between April 13, 2021, and October 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n = 24) versus exAPD (n = 22) in time below range (mean [SD] 1.3% [2.9] vs 2.5% [7.0]) or time in range (63.2% [23.9] vs 59.4% [23.1]) during the primary in-clinic session. In the 2-h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7.3 [1.6] vs 8.0 [1.7] mmol/L, P = 0·023) and comparable time below range (1.4% [4.2] vs 4.9% [14.4]). Across the 76-h study, both algorithms achieved clinical time in range targets (71.2% [16] and 75.5% [11]) and time below range (1.0% [1.2] and 1.3% [2.2]), significantly lower than run-in period (2.4% [2.4], P = 0.0004 vs exMPC; P = 0.012 vs exAPD). No adverse events occurred.
AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycemia.
Most forms of exercise increase the glucose disposal rate and result in hypoglycemia in insulin users, unless insulin is reduced, or carbohydrates are ingested. However, some forms of intensive or competitive exercise activity can cause glucose levels to rise in those on insulin therapy. Many of us in the diabetes and technology space hope that we can see improvements in the way automated insulin delivery (AID) systems deal with “exercise,” but for now, all we can do is try to educate AID users that most forms of physical activity or exercise increase hypoglycemia risk, even when the individual is using an AID system. To help mitigate hypoglycemia risk, AID users are instructed to temporarily change to a higher glucose target for exercise well before the exercise starts (41), but this process is by no means automated as of yet and most users don’t even bother with this advice anyway (42). Jacobs’ and team evaluated their hypothesis that physical activity wearables can detect “exercise” and can be used to automatically advise an AID system of alterations in insulin delivery (i.e., reduce it). The team evaluated two different AID algorithm approaches: 1) exAPD that requires user confirmation of exercise; 2) exMPC that requires no confirmation. Both strategies worked similarly to improve glucose time in range and reduce exercise-associated time below range, albeit exMPC appeared to result in slightly lower (i.e., better, with less hyperglycemia) mean glucose in the 2-hour post-exercise period. Future studies should investigate larger sample sizes with a more diverse range of physical activities. This research provides solid “proof of concept” data to support the notion that exercise-aware AID systems can be improved by integrating physical activity signals from body worn wearables. Other highly respected experts are already commenting on the pros and shortcomings of the various algorithm approaches (43). But we hope these new wearable technologies become a reality soon, despite the issues related to interoperable systems and the possibility of “false positive” and “false negatives” for what one might call “exercise”!
Authors Disclosure Statement
MCR reports receiving consulting fees from the Jaeb Center for Health Research, Eli Lilly, embecta, Zealand Pharma, and Zucara Therapeutics; and speaker fees from Sanofi Diabetes, Eli Lilly, Dexcom Canada, and Novo Nordisk. DPZ has received honoraria for speaking engagements from Ascensia Diabetes, Insulet Canada, Medtronic Diabetes, and Dexcom Inc. DPZ also serves as a member of the Dexcom Advisory Board. DPZ has received research support from the Leona M. and Harry B. Helmsley Charitable Trust (G-2002-04251-2) and the ISPAD-JDRF Research Fellowship. The other authors have nothing to disclose.
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