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Record W4407823854 · doi:10.1101/2025.02.18.25322482

A technology-based randomized controlled trial of self-affirmation and gain-framed health messaging to reduce sedentary behavior in older adults

2025· preprint· en· W4407823854 on OpenAlexafffund
Meishan Ai, Nagashree Thovinakere, Caitlin S. Walker, Cora Ordway, Elizabeth Quinoñez, Emily Melsky, Frank D’Agostino, Susan Whitfield‐Gabrieli, Siobhan M. Phillips, Dominika M. Pindus, Charles Hilman, Timothy P. Morris, Arthur F. Kramer, Maiya R. Geddes

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsDouglas Mental Health University InstituteMcGill UniversityMcGill University Health CentreUniversity of TorontoMontreal Neurological Institute and Hospital
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesCanadian Institutes of Health ResearchHealth CanadaNational Institutes of HealthCanada First Research Excellence FundAlzheimer SocietyGovernment of CanadaHarvard CatalystMcGill UniversityCanadian Bee Research FundNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaAlzheimer Society Research ProgramHarvard University
KeywordsRandomized controlled trialPsychologyText messagingSedentary behaviorApplied psychologyPhysical activityPhysical therapyMedicineComputer scienceInternet privacy

Abstract

fetched live from OpenAlex

Objective: Sedentary behavior significantly increases the risk for chronic diseases and cognitive decline in aging, underscoring the need for effective interventions. Older adults exhibit a 'positivity effect', whereby processing of positive information is prioritized over negative information. In addition, self-affirmation was shown to reduce sedentary behavior in younger adults, but its effects in older adults remain unknown. This study tested a novel, technology-based intervention combining daily self-affirmation and gain-framed health messages to reduce sedentary behavior in older adults. Methods: In a 6-week randomized controlled trial (NCT0431536), 48 cognitively unimpaired, sedentary older adults were randomized into two groups: The intervention group (mean age=70.0±5.4years) completed daily self-affirmation based on their highest-ranked value, followed by gain-framed health messages. The active control group (mean age=68.4±5.0years) performed self-affirmation on their lowest-ranked value, followed by loss-framed messages. This was a single-blinded clinical trial that incorporated a hybrid efficacy and implementation design. Thus, information about intervention feasibility was examined. In addition, baseline motivational traits, including reward sensitivity, were assessed as moderators of behavior change. The neural basis of self-affirmation and gain-framed health messaging was examined at baseline using a task-based, event-related fMRI paradigm across groups, after randomization at the outset of the intervention. Results: The intervention showed high adherence (0.92±0.08) and positive ease-of-use ratings. While the intervention did not significantly reduce sedentary behavior compared to the active control condition, increased reward sensitivity predicted reduced sedentary behavior across all participants. FMRI results showed increased ventral striatal activation in the intervention group, compared to the active control group during reading of gain-framed compared to neutral messages. Conclusions: This study supports the feasibility of technology-based sedentary beahvior reduction interventions for older adults. While self-affirmation combined with gain-framed messaging did not significantly reduce sedentary behavior, gain-framed messages engaged the reward network, and reward sensitivity predicted future reduction in sedentary behavior.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.334
Teacher spread0.318 · 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 designRandomized 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 routes2
Has abstractyes

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