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Record W4417441036 · doi:10.2196/73769

Evaluation of a Tablet-Based Emotion Regulation Intervention for Surrogate Decision-Makers of Patients With Critical Illness: Pilot Nonrandomized Trial

2025· article· en· W4417441036 on OpenAlexvenueno aff
Grant A. Pignatiello, Paul Tuschman, Stephanie Griggs, Nicholas K. Schiltz, Heath A. Demaree, Alex Klinck, Ronald L. Hickman

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingIntervention (counseling)AnxietyCognitionCognitive reappraisalDepression (economics)Randomized controlled trial

Abstract

fetched live from OpenAlex

BACKGROUND: Psychological distress among surrogate decision-makers (surrogates) for patients with critical illness is well documented. Existing interventions for supporting surrogates in their role often target surrogates' informational needs without directly addressing surrogates' acute emotional burden. Therefore, we developed the Reappraisal-Enhanced Foundation for Regulating Affect and Managing Emotions (REFRAME) intervention, a tablet-based app that empowers surrogates to manage their psychological distress with cognitive reappraisal. OBJECTIVE: We sought to (1) determine the feasibility, acceptability, and appropriateness of implementing REFRAME and (2) examine its preliminary effects on surrogates' psychological distress. METHODS: We conducted a pilot nonrandomized trial at a tertiary medical center in northeast Ohio. We recruited adult surrogates for incapacitated intensive care unit (ICU) patients (≥48 hours). The first 20 participants received usual care (UC); the next 28 received UC and REFRAME, consisting of 3 sequential 10- to 15-minute modules administered every 24 to 48 hours (T1-T3) post enrollment (T0). We evaluated implementation outcomes both quantitatively and qualitatively by describing enrollment and completion rates, surrogates' scores on the Acceptability of Intervention Measure and the Intervention Appropriateness Measure, and thematically analyzing feedback from each interventional module. We measured psychological distress with the Patient-Reported Outcomes Measurement Information System Anxiety and Depression short forms at enrollment (T0) and approximately 1-week post enrollment (T3). We used linear mixed-effects models to assess changes in anxiety and depression severity between groups from T0 to T3, adjusting for the surrogate's gender, patient relationship, prior decision-making experience, and perceived stress. RESULTS: Our analytic sample included 48 surrogates (UC=20; REFRAME=28). Two-thirds (19/28, 67.9%) of those assigned to REFRAME completed all 3 modules, with over 70% finding it acceptable and appropriate. Qualitative feedback indicated that surrogates appreciated the intervention's normalization of their emotions and provision of practical reappraisal strategies. Both groups showed reductions in psychological distress severity, with greater reductions in depressive symptoms reported by surrogates in the REFRAME group (d=0.68). CONCLUSIONS: REFRAME was feasible to implement, well-received by users, and considered relevant in the ICU setting. We observed preliminary improvement in depressive symptoms, though the effects on anxiety are less certain. Our findings indicate that incorporating brief cognitive reappraisal tools into routine ICU practice may support surrogates' psychological well-being. Larger, more diverse trials with longer follow-up are necessary to confirm these initial findings and assess their impact on shared decision-making.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.449
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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