# Episodic Future Thinking to Improve Management of Type 2 Diabetes in Rural and Urban Patients: Remote Delivery and Outcomes Assessment to Increase Reach and Dissemination

> **NIH NIH R01** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2022 · $327,451

## Abstract

PROJECT SUMMARY
Successful management of type 2 diabetes (T2D) requires adherence to a dietary, physical activity, and
medication plan agreed upon between a patient and their healthcare providers. The lifestyle changes involved
in these collaborative care plans often provide little to no short-term benefit and may instead be aversive (e.g.,
caloric restriction and physical activity). However, these changes provide critical health benefits in the future,
allowing patients with T2D to halt or reverse disease progression and avoid T2D-related complications (e.g.,
renal disease or diabetic retinopathy). Thus, successful management of type 2 diabetes requires one's present
behavior to be guided by future outcomes. Unfortunately, accumulating evidence indicates that individuals with
type 2 diabetes and those at risk for this disorder show elevated rates of delay discounting (i.e., devaluation of
delayed consequences), which prior data suggest contribute to development and progression of T2D. Thus,
interventions shown to increase valuation of the future are likely to improve T2D management. One such
intervention is episodic future thinking (EFT), a form of prospection in which participants vividly imagine events
that might occur in their future. Preliminary data from the investigative team suggests that EFT facilitates weight
loss and improves glycemic control in patients with T2D. In the proposed work, we will adapt these methods to
examine the feasibility of both remote delivery of EFT and remote outcomes assessment (e.g., weight loss,
glycemic control, and delay discounting) in geographically distributed urban and rural participants. Because
remote delivery and assessment minimize both participant and experimenter burden, these methods may
increase the reach, dissemination, and impact of the intervention. Specific Aim 1 will examine the 8- and 24-
week efficacy of remotely delivered EFT in patients with T2D on remote measures of weight loss, glycemic
control, and delay discounting. Secondary measures will include dietary recalls, physical activity, and medication
adherence. Participants will generate vivid, episodic events and be prompted via a guided smartphone app for
24 weeks to engage in EFT or a control condition in their daily lives. All participants (EFT and control control)
will receive diet and physical activity support, individualized for patients' collaborative care plans. Sub-aim 1a
will compare outcome measures between urban and rural populations to evaluate whether EFT's efficacy is
robust against rural-urban disparities. Specific Aim 2 will examine the acceptability of remote EFT. For an
intervention to be effective in clinical settings, it should be easy to use and its helpfulness should be apparent to
patients. Thus, participants will rate the EFT or control conditions along two dimensions of acceptability:
perceived helpfulness and ease of use. Participants will also complete brief, semi-structured interviews during a
post-intervent...

## Key facts

- **NIH application ID:** 10488205
- **Project number:** 5R01DK129567-02
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** Jeffrey Scott Stein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $327,451
- **Award type:** 5
- **Project period:** 2021-09-12 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488205

## Citation

> US National Institutes of Health, RePORTER application 10488205, Episodic Future Thinking to Improve Management of Type 2 Diabetes in Rural and Urban Patients: Remote Delivery and Outcomes Assessment to Increase Reach and Dissemination (5R01DK129567-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10488205. Licensed CC0.

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