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Record W4396608823 · doi:10.2196/57699

mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial

2024· article· en· W4396608823 on OpenAlexvenueno aff
Rachel L Carey, Ha Le, Donna L. Coffman, Inbal Nahum‐Shani, Mohanraj Thirumalai, Cole Hagen, Laura Baehr, Mary Schmidt-Read, Marlyn S R Lamboy, Stephanie A. Kolakowsky‐Hayner, Ralph J. Marino, Stephen Intille, Shivayogi V. Hiremath

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug Abuse
KeywordsmHealthSpinal cord injuryRandomized controlled trialProtocol (science)Intervention (counseling)MedicinePhysical therapyeHealthPhysical medicine and rehabilitationPsychologyPsychological interventionSpinal cordAlternative medicineNursingHealth carePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based interventions have been linked with improved outcomes and healthier lifestyles among those with SCI. Providing people with an accurate estimate of their everyday PA level can promote PA. Furthermore, PA tracking can be combined with mobile health technology such as smartphones and smartwatches to provide a just-in-time adaptive intervention (JITAI) for individuals with SCI as they go about everyday life. A JITAI can prompt an individual to set a PA goal or provide feedback about their PA levels. OBJECTIVE: The primary aim of this study is to investigate whether minutes of moderate-intensity PA among individuals with SCI can be increased by integrating a JITAI with a web-based PA intervention (WI) program. The WI program is a 14-week web-based PA program widely recommended for individuals with disabilities. A secondary aim is to investigate the benefit of a JITAI on proximal PA, defined as minutes of moderate-intensity PA within 120 minutes of a PA feedback prompt. METHODS: Individuals with SCI (N=196) will be randomized to a WI arm or a WI+JITAI arm. Within the WI+JITAI arm, a microrandomized trial will be used to randomize participants several times a day to different tailored feedback and PA recommendations. Participants will take part in the 24-week study from their home environment in the community. The study has three phases: (1) baseline, (2) WI program with or without JITAI, and (3) PA sustainability. Participants will provide survey-based information at the initial meeting and at the end of weeks 2, 8, 16, and 24. Participants will be asked to wear a smartwatch every day for ≥12 hours for the duration of the study. RESULTS: Recruitment and enrollment began in May 2023. Data analysis is expected to be completed within 6 months of finishing participant data collection. CONCLUSIONS: The JITAI has the potential to achieve long-term PA performance by delivering tailored, just-in-time feedback based on the person's actual PA behavior rather than a generic PA recommendation. New insights from this study may guide intervention designers to develop engaging PA interventions for individuals with disability. TRIAL REGISTRATION: ClinicalTrials.gov NCT05317832; https://clinicaltrials.gov/study/NCT05317832. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57699.

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)0.023
metaresearch head score (Gemma)0.023
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.082
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.023
Meta-epidemiology (narrow)0.0070.004
Meta-epidemiology (broad)0.0150.007
Bibliometrics0.0030.003
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0040.003
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0820.013

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 head0.272
GPT teacher head0.612
Teacher spread0.340 · 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
GenreProtocol

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

Citations8
Published2024
Admission routes1
Has abstractyes

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