Evidence map›Paper›PMID 41608160›Full record

ArticleFrontiers in digital health2025

Achieving clinically meaningful outcomes in digital health: a six-step, cyclical precision engagement framework (ENGAGE).

Anne-Kathrin Eiselt, Suzanne Kirkendall, Engelina Xiong, David Langner, Micah Goldfarb

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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

5 authors.

Anne-Kathrin EiseltProfessional Psychology Department, George Washington University, Washington, DC, United States.
Suzanne KirkendallIpsos, Behavioral Science Center, New York City, NY, United States.
Engelina XiongBVA Nudge Consulting, North America, New York City, NY, United States.
David LangnerIpsos, Behavioral Science Center, New York City, NY, United States.
Micah GoldfarbIpsos, Behavioral Science Center, New York City, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By leveraging everyday technologies such as mobile apps, wearables, and AI-enabled tools, digital health interventions (DHIs) offer new pathways to integrate self-management and intervention programs into the fabric of daily life, while bridging gaps in care through continuous, context-aware support. Yet many tools underperform clinically because digital engagement ("screen time") is conflated with impact, while behavioral science is retrofitted, if applied at all. We propose the ENGAGE Framework: a cyclical, six-step model of precision engagement that integrates user needs, behavioral science, and adaptive personalization to transform initial curiosity into sustained real-world habits. By leveraging available data, users can be segmented according to their need (Step 1: Enroll & Segment), targeted with the most relevant and engaging message to increase micro-engagement (Step 2: Nudge & Hook), and persuaded to engage in real-world health behavior change (Step 3: Guide Behavior). From this macro-engagement step, additional core behavioral science principles are used to reinforce the real-world behaviors long enough to positively impact health outcomes (Step 4: Anchor Habits), while measuring progress (Step 5: Generate Evidence) to inform adaptive and optimized engagement strategies (Step 6: Expand & Evolve with AI) for tailored interventions and communications based on user characteristics, context, and clinical data for both new and existing users. Each step of the ENGAGE Framework maps to evidence-based techniques, implementation tactics (e.g., integration pathways and operational deployment strategies), and metrics that help translate superficial engagement into long-lasting behavior change and measurable clinical outcomes. We synthesize relevant engagement literature, identify gaps and challenges (e.g., measurement heterogeneity, lack of focus on macro-engagement, product development challenges, ecosystem barriers), and offer a practical checklist for innovators. By focusing on who needs what support, when and why, ENGAGE aims to help DHI developers and researchers design interventions that are effective, equitable, and empirically testable.

Indexed as

behavior changedigital health intervention (DHI)Just-in-Time Adaptive Interventions (JITAI)personalizationprecision-healthreal-world evidenceuser engagement

Identifiers

PMID41608160
PMCPMC12836306

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.