Evidence map›Paper›PMID 40729778›Full record

ArticleInternational journal of medical informatics2025

Regression discontinuity in Time: Evaluating the impact of evolving digital health interventions.

Isha Thapa, Pierre-Amaury Laforcade, Franziska K Bishop, Johannes Ferstad, Manisha Desai, David M Maahs, Priya Prahalad, Dessi P Zaharieva, David Scheinker, Ramesh Johari

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Isha ThapaManagement Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address: ishadt@stanford.edu.
Pierre-Amaury LaforcadeManagement Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address: palaforc@stanford.edu.
Franziska K BishopDepartment of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA. Electronic address: fbishop@stanford.edu.
Johannes FerstadManagement Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address: jof@alumni.stanford.edu.
Manisha DesaiDepartment of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA, USA. Electronic address: manisha.desai@stanford.edu.
David M MaahsDepartment of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA; Lucile Packard Children's Hospital, Division of Pediatric Endocrinology, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA. Electronic address: dmaahs@stanford.edu.
Priya PrahaladDepartment of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA. Electronic address: prahalad@stanford.edu.
Dessi P ZaharievaDepartment of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA. Electronic address: dessi@stanford.edu.
David ScheinkerManagement Science and Engineering, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA; Clinical Excellence Research Center, Stanford University, School of Medicine, USA; Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine Stanford, USA. Electronic address: dscheink@stanford.edu.
Ramesh JohariManagement Science and Engineering, Stanford University, Stanford, CA, USA. Electronic address: rjohari@stanford.edu.

Funding

Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Pediatric T1D: 4T StudyR18DK122422 · NIDDK · STANFORD UNIVERSITY · PI MAAHS, DAVID MATTHEW · 2020 to 2024
$3.2M
NCATS NIH HHS UL1 TR003142NIDDK NIH HHS R18 DK122422
6 · The paper itself

Abstract

backgroundClinics continue to adopt digital health interventions (DHIs) in which algorithms analyze data to help direct patient care. Changes to these algorithms are rarely evaluated rigorously, despite their potential to affect patient outcomes. The regression discontinuity in time (RDT) design may be used to estimate the causal effect of such changes but has received little attention in medical literature.

methodsWe conducted a retrospective study of continuous glucose monitor (CGM) data from youth with type 1 diabetes enrolled in the 4T Program from November 2020 to March 2022. In 2020, the clinic used an algorithm that directed patients for provider review based on the number of glucose targets not being met (e.g., time in range (TIR) < 65 %). In September 2021, the clinic adopted a new algorithm that prioritized the review of patients experiencing level 2 hypoglycemia (glucose < 54 mg/dl). We evaluated the validity of the RDT framework in this setting and estimated the impact of this change in directed care on weekly TIR.

resultsThere were 247 patients with 11,297 weekly TIR observations. Robustness checks supported the validity of the RDT design. Patients with level 2 hypoglycemia were prioritized for review, with no significant change to population-level TIR (-0.2% points; 95% CI:[-4.8, 3.5]).

conclusionsWe demonstrate the feasibility of using the RDT framework to estimate the impact of algorithmic changes to a DHI on patient care. As algorithm-directed care increases in clinical practice, this approach can serve as a diagnostic tool to measure how operational changes impact outcomes.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 1TelemedicineAdolescentAlgorithmsBlood GlucoseChildDigital HealthFemaleHumansHypoglycemiaMaleRetrospective StudiesBlood GlucoseAlgorithm EvaluationCausal InferenceContinuous Glucose MonitoringDigital HealthRegression DiscontinuityRemote Patient MonitoringType 1 Diabetes

Identifiers

PMID40729778
PMCPMC12488265

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.