OBJECTIVE

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.

RESEARCH DESIGN AND METHODS

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.

RESULTS

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 3,319 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.

CONCLUSIONS

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, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes.

Physical activity is an important component of diabetes self-care for adolescents with type 1 diabetes (1,2). Recommendations on physical activity volume for adolescents living with type 1 diabetes do not differ from recommendations for youth without diabetes (i.e., 60 min of daily moderate-to-vigorous activity) (3), but the physical and mental health benefits of regular activity for youth living with the disease may be even more essential (4). The rates of those with type 1 diabetes who are overweight/obese mirror the rising trend seen in the general population (5,6), and diagnosis of type 1 diabetes in childhood is associated with increased cardiovascular risk compared with matched control subjects (7,,9). For these and other reasons, regular exercise should be prescribed and encouraged for youth with type 1 diabetes (3), even though barriers related to exercise, including fear of hypoglycemia, may exist for the child or their parent (1013). Fostering better glucose management strategies in and around exercise may facilitate more activity engagement and help to reduce activity-related dysglycemia.

Despite the beneficial impact that exercise can have, incorporating and maintaining daily physical activity levels is challenging for youth living with diabetes. When 7 days of free-living accelerometer data collected from youth with type 1 diabetes were compared with healthy control subjects, those with diabetes had fewer steps and lower median activity, which was not impacted by insulin delivery modality (14). A recent meta-analysis including nearly 5,000 adolescents showed that the likelihood of meeting physical activity recommendations was lower for youth with type 1 diabetes compared with healthy peers; youth with type 1 diabetes were less physically active, more sedentary, and had lower cardiorespiratory fitness (15).

Studies conducted within clinical research settings have highlighted the risk of activity-associated hypoglycemia in pediatric type 1 diabetes. In a study of 50 youth with type 1 diabetes, the Diabetes Research in Children Network found that frequency of overnight hypoglycemia, between 10 p.m. and 6 a.m., was nearly twofold higher when days with afternoon exercise were compared with sedentary days (16). Supporting this notion that overnight hypoglycemia can occur within the ∼12- to 24-h time window after exercise, McMahon et al. (17) infused dextrose to clamp glucose levels following an afternoon of rest compared with one with exercise and found that both during and 7–11 h after physical activity was completed, dextrose needs were significantly higher on afternoons with exercise sessions. In a subsequent study, the finding of a biphasic increase in glucose requirements was not identified; yet glucose infusion rates were significantly higher up to 11 h after exercise, again highlighting potential risk of hypoglycemia in the postactivity recovery period (18). In line with these carefully controlled clamp studies, a hybrid closed-loop insulin delivery study demonstrated that algorithmically modulated insulin delivery was ∼20% lower on nights following afternoon exercise, reflecting greater overnight insulin sensitivity and/or heightened risk for nocturnal hypoglycemia compared with nights after sedentary days in youth with type 1 diabetes (19).

It is important to understand whether findings from a highly controlled environment with prescribed exercise are comparable to the real-world experience of adolescents engaged in their usual daily activities, where exercise type, timing, duration, intensity, and prescriptive strategies to manage glucose are not standardized. The goal of the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study was to better understand the impact of exercise on glycemia in youth with type 1 diabetes by collecting data during routine activities. Initial analyses found that several participant-level and event-level factors appear to influence the drop in glucose during exercise (20), but whether these or other factors impact postexercise glycemia, and/or postexercise hypoglycemia risk is unclear. Here, we explore participant-level and exercise event-level factors that influence postexercise glycemia and hypoglycemia risk in youth with type 1 diabetes in their free-living environment.

Study Design

The study design has been previously described in detail (20) and has been summarized herein. The JAEB Center for Health Research Institutional Review Board approved the study. Electronic parental informed consent and participant assent were obtained for each potential participant prior to screening. Adolescents 12–17 years of age who were at least moderately active, with a diagnosis of type 1 diabetes for at least 3 months prior who administered insulin through a commercially approved hybrid closed-loop (HCL) pump, conventional pump therapy, or low glucose suspend/predictive low glucose systems (pump), or multiple daily injections (MDI) were eligible. The Physical Activity Questionnaire for Children and Adolescents (PAQ) survey was used to assess activity level for eligibility (minimum PAQ score of 1.5). Inclusion and exclusion criteria are detailed in Supplementary Table 1.

Screening and baseline data collection included self-reported diabetes history and standardized questionnaires. Parental questionnaires included the International Physical Activity Questionnaire (IPAQ) (21) and the Hypoglycemia Fear Scale for Parents (HFS-P) (22). Participant questionnaires included the Barriers to Physical Activity in Type 1 Diabetes (BAPAD-1) (23), Hypoglycemia Fear Scale for Children (HFS-C) (22), Pubertal Development Scale (PDS) (24), and Clarke Hypoglycemia Awareness Questionnaire (25).

Participants completed study training, including training on use of the vívosmart 4 wrist-worn activity tracker (Garmin International, Olathe, KS) and the Bant Diabetes smartphone application (app) modified for study use (University Health Network and the Hospital for Sick Children, Toronto, Ontario, Canada), through a virtual visit with a coordinating center staff member. Following training, participants contributed study data for ∼10 days. Participants were asked to self-report, through the Bant app, any activity lasting for at least 10 min. Meal intake, including food photos, were entered for 3 days. MDI users were asked to enter insulin data through the Bant app; insulin data were downloaded for pump users. Participants used their personal Dexcom G6 continuous glucose monitor (CGM) or a blinded Dexcom G6 Pro (Dexcom, San Diego, CA) if they did not use a personal Dexcom G6 CGM. At the conclusion of follow-up, participants were asked to report severe hypoglycemia, diabetic ketoacidosis, hospitalizations, and device-related safety events via an online questionnaire. A poststudy survey solicited methods that participants used to adjust insulin delivery and/or nutrient intake in the recovery period.

Statistical Methods

A statistical analysis plan was developed prior to study initiation outlining outcome metrics, and analysis was planned a priori. CGM metrics following exercise were assessed. Outcomes included mean glucose during 4-h increments postexercise, nadir glucose postexercise, percentage of time in range (TIR; 70–180 mg/dL [3.89–9.99 mmol/L]), and hypoglycemic events (as defined below) during the overnight period postexercise.

The effects of minutes of exercise per day, exercise intensity, exercise duration, competition status, mean heart rate during exercise, and change in glucose during exercise on postexercise mean glucose and overnight hypoglycemic events were assessed. Postexercise mean glucose was computed for every 4-h time block after exercise ended, and a linear mixed-effects regression model was fitted to explore whether the exercise factors influenced the postexercise mean glucose adjusting for glucose at start of exercise and exercise start time with a random participant effect. The adjusted means and 95% CI were reported for each level in each 4-h period. Overnight hypoglycemic events were defined as at least 15 consecutive min with sensor values <70 mg/dL (<3.89 mmol/L). A repeated-measures logistic regression model was fitted to explore whether the exercise factors influenced overnight hypoglycemic events adjusting for bedtime glucose with an exchangeable covariance structure to handle the repeated nights from each participant.

There was no imputation of missing data. Multiple comparisons were corrected using the two-stage Benjamini-Hochberg adaptive false discovery rate correction procedure. All statistical tests were two-sided. Analyses were performed using SAS 9.4 software (SAS Institute).

Data and Resource Availability

The study data are available on the Vivli platform (https://doi.org/10.25934/PR00008429).

There were 251 participants (42% female) with postexercise CGM data (mean age 14 ± 2 years, mean hemoglobin A1c (HbA1c) 7.1 ± 1.3% [54 ± 14.2 mmol/mol], diabetes duration, 5.3 ± 3.9 years). Thirteen participants (5%) with disease duration of <1 year were considered to be in the new-onset period. Insulin modality use was 15% MDI, 30% insulin pump without automation, and 55% automated insulin delivery (AID). The majority (97%) were current Dexcom G6 users. The Pubertal Development Score showed 62% of boys and 61% of girls fell into the midpubertal or late pubertal categories, with 83% of female respondents indicating they had reached menarche. The mean overall PAQ score was 2.7 ± 0.6, which was consistent with the eligibility requirement for participants to be at least moderately physically active. The parental IPAQ survey was completed by 244 respondents; 62% were categorized as achieving health-enhancing physical activity, with only 9% classified as inactive.

During the 10-day study period, 34% of participants logged between 10 and 14 activities, while 27% reported 15 to 19 activities. In total, 3,319 logged activities were included in the analysis, with 53% occurring in the afternoon, between 12 p.m. and 6 p.m. The median (quartiles) duration of each activity was 40 min (20, 75), with 31% of sessions lasting between 20 and 39 min and 17% >100 min.

Glycemic metrics by time period after exercise are presented in Table 1. During the 24-h postexercise period, a dip in mean glucose was identified 8–12 h after activity, which continued during the 12- to 16-h time block. The lowest mean glucose during the recovery period was 150 ± 48 mg/dL (8.33 ± 2.66 mmol/L) noted at 12–16 h after activity, with the highest mean glucose being 168 ± 58 mg/dL (9.32 ± 3.22 mmol/L) 20–24 h after exercise. In the 4 h immediately after exercise, mean TIR was 65 ± 32%. Lower postexercise mean glucose in the 24-h recovery period was observed in those with shorter diabetes duration (<1 year) and those with the lowest baseline HbA1c; yet, no difference in glucose trends based on insulin delivery modality was appreciated (Fig. 1).

Table 1

Glucose metrics within the 24 h after exercise

Time from end of exercise
0 to <4 h4 to <8 h8 to <12 h12 to <16 h16 to <20 h20 to <24 h
Participants (n251 250 249 247 243 225 
Exercise sessions (n2,352 1,818 1,661 1,462 1,042 575 
Glycemic metrics       
 Mean glucose, mg/dL (mmol/L) 159 ± 53 159 ± 54 150 ± 54 150 ± 48 160 ± 53 168 ± 58 
(8.82 ± 2.94) (8.82 ± 3.00) (8.33 ± 3.00) (8.33 ± 2.66) (8.88 ± 2.94) (9.32 ± 3.22) 
 Glucose SD, mg/dL (mmol/L) 36 ± 20 30 ± 19 24 ± 17 25 ± 19 31 ± 19 34 ± 19 
(2.00 ± 1.11) (1.67 ± 1.05) (1.33 ± 0.94) (1.39 ± 1.05) (1.72 ± 1.05) (1.89 ± 1.05) 
 Glucose CV, % 23 ± 11 19 ± 11 16 ± 10 17 ± 11 19 ± 10 20 ± 10 
 TIR 70–180 mg/dL (3.89–9.99 mmol/L), % 65 ± 32 66 ± 34 73 ± 34 75 ± 32 68 ± 34 62 ± 35 
 TIR 70–140 mg/dL (3.89–7.78 mmol/L), % 44 ± 32 43 ± 35 52 ± 38 52 ± 37 44 ± 35 40 ± 34 
 Time <70 mg/dL (<3.89 mmol/L), %* 1.9 ± 3.6 1.5 ± 3.2 1.1 ± 2.9 1.1 ± 2.8 1.1 ± 2.9 1.2 ± 2.9 
 Time >180 mg/dL (>9.99 mmol/L), % 31.4 ± 33.1 31.6 ± 35.0 24.1 ± 34.3 22.7 ± 32.0 30.1 ± 34.7 36.0 ± 35.5 
 Time >250 mg/dL (>13.88 mmol/L), %* 8.0 ± 14.5 7.2 ± 13.9 5.1 ± 12.4 5.0 ± 12.0 7.5 ± 14.4 9.5 ± 15.6 
Postexercise sessions with hypoglycemic event       
 <70 mg/dL (<3.89 mmol/L), % 368 (16) 215 (12) 147 (9) 130 (9) 90 (9) 56 (10) 
 <54 mg/dL (<3.00 mmol/L), % 71 (3) 35 (2) 35 (2) 18 (1) 19 (2) 8 (1) 
Time from end of exercise
0 to <4 h4 to <8 h8 to <12 h12 to <16 h16 to <20 h20 to <24 h
Participants (n251 250 249 247 243 225 
Exercise sessions (n2,352 1,818 1,661 1,462 1,042 575 
Glycemic metrics       
 Mean glucose, mg/dL (mmol/L) 159 ± 53 159 ± 54 150 ± 54 150 ± 48 160 ± 53 168 ± 58 
(8.82 ± 2.94) (8.82 ± 3.00) (8.33 ± 3.00) (8.33 ± 2.66) (8.88 ± 2.94) (9.32 ± 3.22) 
 Glucose SD, mg/dL (mmol/L) 36 ± 20 30 ± 19 24 ± 17 25 ± 19 31 ± 19 34 ± 19 
(2.00 ± 1.11) (1.67 ± 1.05) (1.33 ± 0.94) (1.39 ± 1.05) (1.72 ± 1.05) (1.89 ± 1.05) 
 Glucose CV, % 23 ± 11 19 ± 11 16 ± 10 17 ± 11 19 ± 10 20 ± 10 
 TIR 70–180 mg/dL (3.89–9.99 mmol/L), % 65 ± 32 66 ± 34 73 ± 34 75 ± 32 68 ± 34 62 ± 35 
 TIR 70–140 mg/dL (3.89–7.78 mmol/L), % 44 ± 32 43 ± 35 52 ± 38 52 ± 37 44 ± 35 40 ± 34 
 Time <70 mg/dL (<3.89 mmol/L), %* 1.9 ± 3.6 1.5 ± 3.2 1.1 ± 2.9 1.1 ± 2.8 1.1 ± 2.9 1.2 ± 2.9 
 Time >180 mg/dL (>9.99 mmol/L), % 31.4 ± 33.1 31.6 ± 35.0 24.1 ± 34.3 22.7 ± 32.0 30.1 ± 34.7 36.0 ± 35.5 
 Time >250 mg/dL (>13.88 mmol/L), %* 8.0 ± 14.5 7.2 ± 13.9 5.1 ± 12.4 5.0 ± 12.0 7.5 ± 14.4 9.5 ± 15.6 
Postexercise sessions with hypoglycemic event       
 <70 mg/dL (<3.89 mmol/L), % 368 (16) 215 (12) 147 (9) 130 (9) 90 (9) 56 (10) 
 <54 mg/dL (<3.00 mmol/L), % 71 (3) 35 (2) 35 (2) 18 (1) 19 (2) 8 (1) 

Data are presented as n (%) or mean ± SD. CV, coefficient of variation.

*

Values are winsorized at the 10th and 90th percentiles to account for skewness. Winsorized mean percentage time <54 mg/dL (<3.00 mmol/L) was 0% for all periods due to the low amount of hypoglycemia in each 4-h interval.

A CGM sensor-defined hypoglycemic event <70 mg/dL (<3.89 mmol/L) is defined as at least 15 consecutive minutes <70 mg/dL (<3.89 mmol/L). The hypoglycemic event ends when there are at least 15 consecutive minutes ≥80 mg/dL (≥4.44 mmol/L), at which point the participant becomes eligible for a new hypoglycemic event. A CGM sensor-defined hypoglycemic event <54 mg/dL (<3.00 mmol/L) is defined as at least 15 consecutive minutes <54 mg/dL (<3.00 mmol/L). The hypoglycemic event ends when there are at least 15 consecutive minutes ≥70 mg/dL (≥3.89 mmol/L), at which point the participant becomes eligible for a new hypoglycemic event.

Figure 1

The effect of diabetes duration (A), self-reported HbA1c (B), and insulin delivery modality (C) on postexercise mean glucose. The line represents the median, and the dot represents the mean.

Figure 1

The effect of diabetes duration (A), self-reported HbA1c (B), and insulin delivery modality (C) on postexercise mean glucose. The line represents the median, and the dot represents the mean.

Close modal

Figure 2 shows the effect of change in glucose during exercise, as measured from pre- to postexercise, on postexercise mean glucose, percentage of time >180 mg/dL (9.99 mmol/L), and percentage of time <70 mg/dL (3.89 mmol/L) by 4-h periods. Exercise sessions with a larger drop were associated with lower postexercise mean glucose levels, with the most pronounced differences occurring immediately after exercise and in the 12 to <16 h after exercise. The adjusted mean glucose in the 4 h postexercise was 133 mg/dL (7.38 mmol/L) for activities with a change in glucose <−40 mg/dL (<−2.22 mmol/L) compared with 188 mg/dL (10.43 mmol/L) for activities with a change of glucose >15 mg/dL (>0.83 mmol/L) (P < 0.001), and the mean glucose in the 12 to <16 h postexercise was 146 mg/dL (8.10 mmol/L) vs. 156 mg/dL (8.66 mmol/L), respectively (P = 0.02). Exercise duration, whether an exercise session was competitive (i.e., game) or usual activity, and mean heart rate during exercise had little or no effect on postexercise mean glucose (Supplementary Fig. 1). Self-reported high-intensity effort during exercise tended to be associated with lower mean glucose levels over the next 24 h, by ∼10 mg/dL (0.56 mmol/L). Participants with at least 60 min of activity per day had higher postexercise mean glucose 0–12 h following exercise (Supplementary Fig. 2).

Figure 2

The effect of change in glucose during exercise on postexercise glycemia. Mean glucose (A), and percentage of time glucose was >180 mg/dL (10.0 mmol/L) (B), and <70 mg/dL (3.9 mmol/L) (C). Line ends are the 95% CI, and the dot represents the adjusted mean.

Figure 2

The effect of change in glucose during exercise on postexercise glycemia. Mean glucose (A), and percentage of time glucose was >180 mg/dL (10.0 mmol/L) (B), and <70 mg/dL (3.9 mmol/L) (C). Line ends are the 95% CI, and the dot represents the adjusted mean.

Close modal

The median (quartiles) overnight nadir glucose following exercise was 96 (74, 118) mg/dL (5.33 [4.11, 6.55] mmol/L), and median overnight percentage TIR following exercise was 90% (58%, 100%). Overnight hypoglycemia following exercise was observed on 14% of nights (Table 2), which was only slightly higher than the 12% rate of nocturnal hypoglycemia observed across 424 nights following a day with no activities logged (i.e., a sedentary day). On exercise days, nocturnal hypoglycemia was more frequent when the participants’ total daily activity was greater, with hypoglycemia occurring on 17% of the nights for participants with a total activity ≥60 min/day compared with 8% for participants with a total activity <60 min/day (P = 0.01). Overnight hypoglycemia was also more frequent following longer individual exercise sessions, with 19% postexercise nights having hypoglycemia when the exercise lasted ≥90 min compared with 12% when exercise duration was 10 to <30 min (P = 0.01). Interestingly, the mean bedtime glucose was similar irrespective of exercise minutes per day and exercise duration (∼160 mg/dL [∼8.88 mmol/L]), and results were similar when not adjusting for bedtime glucose (data not shown). Exercise intensity, competitive status, mean heart rate during exercise, and the change in glucose during exercise did not significantly affect postexercise overnight hypoglycemic events after adjusting for bedtime glucose (Table 2).

Table 2

Factors affecting overnight hypoglycemic events

Postexercise nights (n)Bedtime glucose mean ± SDPostexercise nights with hypoglycemic event, %*Adjusted P value
Overall 1,273 162 ± 67 (8.99 ± 3.72) 14 — 
Minutes of exercise per day    0.01 
 <60 min (n = 75 participants) 389 162 ± 58 (8.99 ± 3.22)  
 ≥60 min (n = 142 participants) 884 161 ± 70 (8.94 ± 3.89) 17  
Exercise duration    0.01 
 10 to <30 min 366 164 ± 65 (9.10 ± 3.61) 12  
 30 to <60 min 369 160 ± 64 (8.88 ± 3.55) 13  
 60 to <90 min 207 159 ± 64 (8.82 ± 3.55) 15  
 ≥90 min 331 162 ± 73 (8.99 ± 4.05) 19  
Perceived exercise intensity    0.58 
 Mild/low 393 166 ± 65 (9.21 ± 3.61) 12  
 Moderate 721 161 ± 67 (8.94 ± 3.72) 15  
 High 159 156 ± 67 (8.66 ± 3.72) 17  
Competition status    0.97 
 Competitive 303 162 ± 70 (8.99 ± 3.89) 15  
 Noncompetitive 970 162 ± 66 (8.99 ± 3.66) 14  
Mean heart rate during exercise    0.91 
 <100 bpm 298 158 ± 74 (8.77 ± 4.11) 18  
 100 to <120 bpm 533 162 ± 63 (8.99 ± 4.11) 11  
 ≥120 bpm 294 163 ± 68 (9.05 ± 3.77) 15  
Change in glucose during exercise    0.98 
 <−40 mg/dL (<−2.22 mmol/L) 356 164 ± 66 (9.10 ± 3.66) 15  
 −40 to <15 mg/dL (−2.22 to <0.83 mmol/L) 551 158 ± 63 (8.77 ± 4.11) 14  
 ≥15 mg/dL (≥0.83 mmol/L) 284 165 ± 72 (9.16 ± 4.00) 14  
Postexercise nights (n)Bedtime glucose mean ± SDPostexercise nights with hypoglycemic event, %*Adjusted P value
Overall 1,273 162 ± 67 (8.99 ± 3.72) 14 — 
Minutes of exercise per day    0.01 
 <60 min (n = 75 participants) 389 162 ± 58 (8.99 ± 3.22)  
 ≥60 min (n = 142 participants) 884 161 ± 70 (8.94 ± 3.89) 17  
Exercise duration    0.01 
 10 to <30 min 366 164 ± 65 (9.10 ± 3.61) 12  
 30 to <60 min 369 160 ± 64 (8.88 ± 3.55) 13  
 60 to <90 min 207 159 ± 64 (8.82 ± 3.55) 15  
 ≥90 min 331 162 ± 73 (8.99 ± 4.05) 19  
Perceived exercise intensity    0.58 
 Mild/low 393 166 ± 65 (9.21 ± 3.61) 12  
 Moderate 721 161 ± 67 (8.94 ± 3.72) 15  
 High 159 156 ± 67 (8.66 ± 3.72) 17  
Competition status    0.97 
 Competitive 303 162 ± 70 (8.99 ± 3.89) 15  
 Noncompetitive 970 162 ± 66 (8.99 ± 3.66) 14  
Mean heart rate during exercise    0.91 
 <100 bpm 298 158 ± 74 (8.77 ± 4.11) 18  
 100 to <120 bpm 533 162 ± 63 (8.99 ± 4.11) 11  
 ≥120 bpm 294 163 ± 68 (9.05 ± 3.77) 15  
Change in glucose during exercise    0.98 
 <−40 mg/dL (<−2.22 mmol/L) 356 164 ± 66 (9.10 ± 3.66) 15  
 −40 to <15 mg/dL (−2.22 to <0.83 mmol/L) 551 158 ± 63 (8.77 ± 4.11) 14  
 ≥15 mg/dL (≥0.83 mmol/L) 284 165 ± 72 (9.16 ± 4.00) 14  

Bedtime glucose values are shown as mg/dL (mmol/L).

*

A CGM-measured overnight hypoglycemic event is defined as at least 15 consecutive minutes <70 mg/dL (<3.89 mmol/L) during the overnight period (midnight and 5:59 a.m.).

P value calculated from a repeated-measures logistic regression model adjusting for bedtime glucose with an exchangeable covariance structure. Multiple comparisons were adjusted using the Benjamini-Hochberg adaptive false discovery rate correction method.

Certain clinical characteristics appeared to influence rate of postexercise nocturnal hypoglycemia. For those with a duration of diabetes of <1 year, categorized as new onset in this study, overnight hypoglycemia rates were threefold lower than those with longer disease duration (Supplementary Table 2). The frequency of postexercise overnight hypoglycemia was 22% for participants with an HbA1c <6.0% (<42 mmol/mol) compared with 13% for participants with an HbA1c ≥8.0% (≥64 mmol/mol). Although insulin delivery method did not impact mean glucose levels in the 24-h recovery period, those on AID devices had 11% of nights with a postexercise hypoglycemia event compared with 15% in those on MDI and 21% in those on conventional pump therapy. Not surprisingly, increased percentage time below range of <70 mg/dL (<3.89 mmol/L) in the 24 h prior to exercise increased the probability of overnight hypoglycemia postexercise; 1% time below range was associated with 9% of nights with hypoglycemia versus ≥4% time below range having 29% of nights with nocturnal hypoglycemia.

One participant reported a severe hypoglycemic event during activity. Glucagon was not administered, but the event required assistance from a parent to treat.

After completing the study, participants were surveyed regarding how they managed glucose levels after exercise. Of 251 participants, 104 (42%) completed the survey, with 12% of respondents on MDI, 30% on pump without automation, and 58% on HCL. Of all potential strategies to manage glucose after exercise, >50% of respondents, regardless of insulin delivery modality, endorsed “consumed carbohydrates after exercise and/or at bedtime without taking insulin to cover the carbohydrates eaten” (Supplementary Table 3). However, those on both pumps and AID endorsed altering insulin doses, whereas those on MDI rarely reported adjusting insulin doses after activity.

With 3,319 exercise sessions, clustered into ∼40 different activity categories (e.g., walking, resistance exercise, organized sports, video games, and recess/playing with friends), collected among 251 youth with type 1 diabetes during a 10-day observational period in which usual physical activity was encouraged, the T1DEXIP study data provide a wealth of information on the real-world impact of physical activity on glycemia in the 24 h following exercise. While studies in carefully regulated research environments with prescribed exercise have aided in understanding physiologic changes with exercise, this data set of actual experience includes a breadth of typical activity types that will be of value as researchers continue to explore strategies to manage exercise for youth with diabetes. Moreover, these analyses help to provide new information on the real-world impact of HCL therapy on protection against postexercise dysglycemia in active youth living with type 1 diabetes.

Frequency of overnight hypoglycemia (<70 mg/dL [<3.89 mmol/L] for 15-min duration) was detected on 14% of nights following exercise. Notably, this rate of nocturnal hypoglycemia was similar, albeit slightly higher, to what was seen in this cohort on days with no logged activity (12%). These rates are considerably lower than in an earlier Diabetes Research In Children Network (DirecNet) Study Group in-clinic study where afternoon exercise resulted in a 48% nocturnal hypoglycemia event rate (defined as a plasma glucose <60 mg/dL [<3.33 mmol/L], or fingerstick glucose <60 mg/dL [<3.33 mmol/L] with treatment when a central laboratory glucose value was not available) in youth who were on either standard insulin pump therapy or on MDI (16). Postexercise/prebedtime carbohydrate feeding was a common strategy used by this cohort, which was not universally implemented in the DirecNet study, unless hypoglycemia developed before bedtime.

Importantly, the likelihood of developing nocturnal hypoglycemia was approximately twofold higher for participants who were achieving consensus guideline targets (average total activity duration >60 min/day) compared with participants with lower daily activity (17% vs. 8%). If a single exercise session was ≥90 min, risk of nocturnal hypoglycemia was 19%. Our findings suggest that during routine follow-up, clinicians should establish current physical activity patterns to best help structure the counseling provided even in those on HCL systems since nocturnal hypoglycemia associated with activity is a common clinical concern. In youth with insufficient physical activity levels, discussion on how to increase daily activity to achieve target levels, while reinforcing the known benefits, would also be warranted (3). In those who are exceeding consensus guideline recommendations, the clinical focus could shift to an assessment of whether nocturnal hypoglycemia is present because of activity patterns and whether longer single bouts of exercise occur for that individual, which does appear to increase risk significantly.

Not surprisingly, time below range ≥4% in the previous 24 h was also associated with a threefold higher risk of overnight hypoglycemia compared with those with time below range of 1% on the day prior (29% vs. 9%). Clinicians may choose to use these data to ground discussions regarding risk of hypoglycemia should exercise occur on a day with excessive time below range. The goal of <4% time below range <70 mg/dL (<3.89 mmol/L) recommended by consensus reports seems appropriate in clinical decision making based upon the 2 weeks of CGM data collected in this study (26,27). Methods to minimize risk of nocturnal hypoglycemia, such as reducing insulin delivery overnight, which could occur through reducing usual injected insulin doses or leveraging temporary basal rates/restrained modes available in HCL systems, or consuming carbohydrates prior to bed, should be recommended (3). It is feasible that in the future, existing algorithms that can jointly track CGM and activity monitor data (28,29) could incorporate additional insulin-dosing constraints if threshold for time below range is breeched. It is worth noting that even in the present analysis, those on current HCL systems had a twofold lower postexercise nocturnal hypoglycemia risk compared with those on conventional pump therapy (11% vs. 21% of nights), suggesting a benefit with advanced insulin delivery technologies even without sophisticated physical activity monitoring integrations.

Our findings demonstrate that mean glucose levels tend to be lowest 8–16 h after exercise. While this effect may be related to heightened insulin sensitivity after exercise, perhaps to help restore the body’s glycogen stores, the nadir in glucose concentration after exercise is likely impacted by additional factors. For example, when change in glucose during exercise was assessed, those with a greater drop during exercise were found to have lower mean glucose in the immediate postrecovery period (0–4 h) and 12–16 h after exercise.

While change in glucose during exercise and activity duration appeared to influence glycemia in the recovery period, other event-level characteristics, such as mean heart rate during exercise, activity duration, and self-perception of competition status did not seem to impact glucose levels significantly during early or late recovery. Activities with greater perceived exertion tended to have lower glucose levels 8–24 h postexercise by ∼10 mg/dL (0.56 mmol/L), on average, compared with self-reported lighter intensity efforts. Competition status does seem to be associated with an attenuated drop in glucose, and sometimes even an acute rise, during the exercise period (20); this effect is presumably tied to the adrenergic state associated with competition leading to increased hepatic glucose production and potential insulin resistance (3).

In addition to the event-level characteristics described above, influences of participant characteristics were also explored for potential impact on postexercise glycemia. Lower mean glucose in the recovery period was noted for those with the lowest baseline HbA1c levels (<6.0% [<42 mmol/mol]). Understandably, these two clinical metrics mirror each other, those with lower mean glucose will tend to have lower HbA1c levels and vice versa. Importantly, in our cohort, those with the lowest HbA1c values (i.e., <6% [<42 mmol/mol]), who tended to have lower mean glucose in the overnight period, did not have increased risk for excessive time below range as identified based on their CGM data in the 24 h prior to exercise nor when time below range was restricted to the overnight period. This suggests that those with near-normal HbA1c values may have intrinsic metabolic characteristics or adaptive strategies they implement when exercising to reduce hypoglycemia risk.

Youth diagnosed within the past year, defined as new onset, accounted for a relatively small proportion of the total cohort (5%). However, this group showed a trend for lower mean glucose concentrations in the entire 24-h recovery period, despite disproportionate use of MDI therapy compared with the larger cohort (61% vs. 13%). The observed lower mean glucose levels postexercise, but without hypoglycemia per se, likely reflects the benefits of residual insulin production often seen in those during the honeymoon phase (30). Rates of nocturnal hypoglycemia were threefold higher in those with disease duration >1 year compared with the new-onset cohort. These findings suggest that exercise, a cornerstone of diabetes care, should be encouraged from disease onset, despite potential concern for hypoglycemia. A recent study leveraging both CGM and activity monitoring technologies within 1 month postdiagnosis showed that on days with vigorous activity, TIR was ∼6% higher than on sedentary days, without increased time below range, a finding that could reduce concerns regarding safety and emphasize the beneficial impact of exercise early in the disease course (31).

The exponential growth of diabetes-related technology use in the pediatric population was reflected in this study cohort: 98% of participants were using CGM at enrollment, and 55% reported using HCL. Surprisingly, insulin delivery modality did not alter glycemic patterns identified in the recovery period. However, it is critical to understand the complex interplay between both insulin delivery and behavioral modifications that individuals may make. Modifications to insulin dosing and carbohydrate consumption were not collected in real time and only collected in a poststudy survey, limiting our ability to assess differences achieved solely through insulin delivery devices. Irrespective of the insulin delivery modality used, nearly 50% of respondents endorsed consuming carbohydrates after exercise or prior to bed without taking insulin.

This observational study of youth with type 1 diabetes examining the impact of exercise conducted in a nonprescriptive fashion has several strengths, including the sheer volume of exercise sessions captured as well as the diversity of activities reported. The study also has limitations. While participants ranged in age from 12 to 17 years old, and 83% reported their race/ethnicity being White non-Hispanic, the generalizability of our findings may still be limited as those who were less than moderately physically active (PAQ score of <1.5) were excluded. This cohort also tended to use real-time CGM more frequently than is typical. Participants in this study had low overall hypoglycemia, so our analysis was limited to hypoglycemic events rather than percentage of time below range. These findings may not be generalizable to youth with higher incidence rates of hypoglycemia associated with exercise.

Additionally, it was not possible to examine how family dynamics, support, and baseline knowledge may affect the individual's approach to self-management around exercise. Among this cohort of relatively active youth, most parents surveyed were classified as being health- enhancing physically active, which may impact both the activity patterns of the child and his/her/their self-management strategies. Moreover, objective data related to real-time behavioral modifications, including carbohydrate intake and insulin dose alterations, cannot be evaluated through this data set. Nor did we use objective means to assess exercise intensity.

Recognizing that many youth, including those who live with type 1 diabetes, do not achieve exercise goals to maintain a healthy lifestyle, the T1DEXIP study data set provides critical information on real-world changes in glycemia during and after acute exercise events (20). The postexercise recovery period showed mean glucose tended to dip between 8 and 16 h after physical activity, with overnight median time in target range 70–180 mg/dL (3.89–9.99 mmol/L) being 90% (interquartile range 58%, 100%). Nocturnal hypoglycemia postexercise was ∼14%, which was similar to the 12% rate noted on nights after sedentary days and vastly reduced from what has been detected in earlier and smaller clinical studies. Yet, risk of overnight hypoglycemia was slightly higher in those who do more daily activity and in those who tend to have more time below range <70 mg/dL (<3.89 mmol/L) prior to exercise. Given the relatively lower incidence of nocturnal hypoglycemia demonstrated in this cohort compared with historic studies, it is feasible that achievement of physical activity targets can be attained through education and development of individualized plans, as recommended by the International Society for Pediatric and Adolescent Diabetes (ISPAD) clinical practice guidelines (3). Ongoing analyses of these data may help researchers incorporate physical activity into decision support tools and algorithmic modulation of insulin delivery to ensure that glycemia does not hinder youth with diabetes from participating fully in a variety of activities.

This article contains supplementary material online at https://doi.org/10.2337/figshare.25199444.

Acknowledgments. We extend our sincere thanks to the participants in this study and their families.

Funding. Research reported in this publication was supported by The Leona M. and Harry. B. Helmsley Charitable Trust. Children’s Mercy Kansas City has received grants or contracts for M.A.C. from the National Institutes of Health, The Leona M. and Harry B. Helmsley Charitable Trust, the JDRF, and the Emily Rosebud Foundation. S.R.P. reports receiving grants from the Leona M. and Harry B. Helmsley Charitable Trust, the National Institutes of Health, and the Jaeb Center for Health Research, and honorarium from the American Diabetes Association, outside the submitted work. Yale School of Medicine has received research support for J.L.S. from the National Institutes of Health, Jaeb Center for Health Research, and JDRF.

Dexcom provided continuous glucose monitors at a discounted rate.

Duality of Interest. J.L.S. serves, or has served, on advisory panels for Bigfoot Biomedical, Cecelia Health, Insulet Corporation, Medtronic Diabetes, StartUp Health Diabetes Moonshot, and Vertex. J.L.S. has served as a consultant to Abbott Diabetes, Bigfoot Biomedical, Insulet, Medtronic Diabetes, and Zealand Pharma A/S. Yale School of Medicine has received research support for J.L.S. from Abbott Diabetes, Insulet, Medtronic, and Prevention Bio. M.A.C. is chief medical officer of Glooko Inc. and has received research support from Dexcom and Abbott Diabetes Care. Children’s Mercy Kansas City has received grants or contracts for M.A.C. from Eli Lilly, Tolerion, and Garmin. M.C.R. serves on advisory panels for Zealand Pharma A/S, Zucara Therapeutics, and Indigo Diabetes, acts as a consultant for the Jaeb Center for Health Research, has given lectures sponsored by Dexcom, Novo Nordisk, and Sanofi, and is a shareholder, or holds stocks in, Supersapiens and Zucara Therapeutics. No other potential conflicts of interest relevant to this article were reported.

Authors Contributions. J.L.S., S.B., R.L.G., and M.C.R. drafted the manuscript. S.B. performed statistical analyses. M.A.C., S.R.P., P.C., and L.C.B. reviewed and edited the manuscript. All authors reviewed and approved the final draft of the manuscript. J.L.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Elizabeth Selvin and Adrian Vella.

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