Detect Rebound Hypoglycemia and Hyperglycemia

Detects rebound hypoglycemia (Rhypo) and rebound hyperglycemia (Rhyper) as derived events built from cgmguru Level 1 event starts and a later opposite-threshold crossing within a configurable time window.

Rhypo is a Level 1 hyperglycemic event (\(>\)180 mg/dL for at least 15 minutes) followed by any glucose value \(<\)70 mg/dL within rebound_minutes. Rhyper is a Level 1 hypoglycemic event (\(<\)70 mg/dL for at least 15 minutes) followed by any glucose value \(>\)180 mg/dL within rebound_minutes. The later rebound side only needs a single qualifying threshold crossing.

These rebound hypoglycemia and hyperglycemia definitions are documented by Hansen and Bibby (2024); cgmguru applies them on its own event-preprocessed CGM grid.

Usage

rebound_events(df, type = c("all", "hypo", "hyper"),
 data_source = c("raw", "preprocessed"), reading_minutes = NULL,
 sort_time = FALSE, inter_gap = 45, rebound_minutes = 120,
 return_interpolated = TRUE)

Arguments

df

A dataframe containing continuous glucose monitoring (CGM) data with columns id, time, and gl.

type

Rebound direction to return. "hypo" returns Rhypo, "hyper" returns Rhyper, and "all" returns both.

data_source

Source interpretation for df. "raw" applies cgmguru event preprocessing first. "preprocessed" treats df as an already-preprocessed event grid and does not interpolate again.

reading_minutes

Time interval between readings in minutes. Can be a scalar, a vector matching nrow(df), or NULL. If omitted, it is inferred per id from positive timestamp differences.

sort_time

Logical. If TRUE, sort rows within each id by time. Defaults to FALSE.

inter_gap

Maximum gap in minutes to interpolate across when data_source = "raw". Defaults to 45.

rebound_minutes

Maximum bridge interval in minutes from the initial Level 1 event end to the later rebound threshold crossing. Defaults to 120.

return_interpolated

Logical. If TRUE, include the preprocessed event grid used for rebound detection as interpolated_data. Defaults to TRUE, so rebound_events() returns the preprocessed data by default.

Value

A list containing:

• events_total: Tibble with id, type, total_episodes, and avg_ep_per_day.

• events_detailed: Tibble with bridge boundaries (start_time, end_time, indices, and glucose values), the initial Level 1 event boundaries, rebound threshold crossing fields, and minutes_to_rebound.

• interpolated_data: The preprocessed event grid used for rebound detection, included by default when return_interpolated = TRUE, with columns id, time, and gl.

References

Hansen, K. W., and Bibby, B. M. (2024). Rebound hypoglycemia and hyperglycemia in type 1 diabetes. Journal of Diabetes Science and Technology, 18(6), 1392-1398.

See also

detect_all_events, detect_hyperglycemic_events, detect_hypoglycemic_events, interpolate_cgm

Examples

df <- data.frame(
  id = "A",
  time = as.POSIXct("2026-01-01 00:00:00", tz = "UTC") + 0:8 * 5 * 60,
  gl = c(190, 195, 200, 170, 165, 160, 65, 100, 110)
)
rebound_events(df, type = "hypo", reading_minutes = 5)
#> $events_total
#> # A tibble: 1 × 4
#>   id    type  total_episodes avg_ep_per_day
#>   <chr> <chr>          <int>          <dbl>
#> 1 A     hypo               0              0
#> 
#> $events_detailed
#> # A tibble: 0 × 18
#> # ℹ 18 variables: id <chr>, type <chr>, start_time <dttm>, start_glucose <dbl>,
#> #   end_time <dttm>, end_glucose <dbl>, start_index <int>, end_index <int>,
#> #   initial_start_time <dttm>, initial_start_glucose <dbl>,
#> #   initial_end_time <dttm>, initial_end_glucose <dbl>,
#> #   initial_start_index <int>, initial_end_index <int>, rebound_time <dttm>,
#> #   rebound_glucose <dbl>, rebound_index <int>, minutes_to_rebound <dbl>
#> 
#> $interpolated_data
#> # A tibble: 8 × 3
#>   id    time                   gl
#>   <chr> <dttm>              <dbl>
#> 1 A     2026-01-01 00:05:00   195
#> 2 A     2026-01-01 00:10:00   200
#> 3 A     2026-01-01 00:15:00   170
#> 4 A     2026-01-01 00:20:00   165
#> 5 A     2026-01-01 00:25:00   160
#> 6 A     2026-01-01 00:30:00    65
#> 7 A     2026-01-01 00:35:00   100
#> 8 A     2026-01-01 00:40:00   110
#>