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Record W3100062556 · doi:10.2196/25351

Live Video Adaptations to a Mind-Body Activity Program for Chronic Pain and Cognitive Decline: Protocol for the Virtual Active Brains Study

2020· article· en· W3100062556 on OpenAlexvenueaboutno aff
Ryan A. Mace, James Doorley, Paula Popok, Ana‐Maria Vranceanu

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative Health
KeywordsRandomized controlled trialPhysical therapyCognitionMedicineProtocol (science)Physical medicine and rehabilitationPsychologyPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic pain (CP) and cognitive decline (CD) are costly, challenging to treat, prevalent among older adults, and worsen each other over time. We are iteratively developing Active Brains-Fitbit (AB-F), a live video program for older adults with CP and CD that teaches mind-body skills and gradual increases in step count. AB-F has demonstrated feasibility; acceptability; and signs of improvement in emotional, physical, and cognitive functions when delivered in person to older adults. OBJECTIVE: We are conducting a feasibility randomized controlled trial (RCT) of AB-F versus a time- and dose-matched educational control (health enhancement program [HEP]) in older adults with CP and CD. Here, we describe virtual adaptions to our study protocol, manualized treatments, evaluation plan, and study design in response to feedback from former participants and COVID-19. We will evaluate the feasibility benchmarks and the potential of AB-F to improve physical, emotional, and cognitive functions. METHODS: This is a single-blind pilot RCT. Participants are randomized to AB-F or HEP. Patients are recruited through pain clinic referrals, institutional registries, and flyers. Interested participants are screened for eligibility via telephone and provide electronic informed consent. After randomization, participants are mailed all study documents, including their treatment manual, an ActiGraph accelerometer, and a Fitbit (separate envelope for AB-F only). Both conditions are manualized and delivered over 8 weekly sessions via Zoom. Participants complete self-report and performance-based (6-min walk test and Montreal Cognitive Assessment) outcome measures via Zoom at baseline and post intervention. Primary outcomes are a priori set feasibility (recruitment, quantitative measures, and adherence), acceptability, credibility, expectancy, and satisfaction benchmarks. Secondary outcomes are physical, cognitive, and emotional functions as well as intervention targets (social function, pain intensity, pain-specific coping, and mindfulness). RESULTS: The trial is ongoing. We have recruited 21 participants (10 AB-F and 11 HEP) across 2 rounds. Only 2 participants have withdrawn (1 before baseline and 1 before the first session). All 19 remaining participants have completed the baseline assessment. In the first round, attendance is high (11 out of 12 participants completed all 4 sessions so far), and AB-F participants are adherent to their Fitbit and step goals (5 out of 6 participants). CONCLUSIONS: Preliminary findings are promising for the feasibility of our completely virtual AB-F intervention. However, these findings need to be confirmed at the trial conclusion. This study will answer important questions about the feasibility of delivering a completely virtual mind-body activity program to older adults with comorbid CP and CD, which, to our knowledge, is unprecedented. Details on integrating multiple digital platforms for virtual assessments and intervention delivery will inform treatment development for older adults and those with comorbid CP and CD, which is crucial during the COVID-19 pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT04044183; https://clinicaltrials.gov/ct2/show/NCT04044183. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25351.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.443
GPT teacher head0.614
Teacher spread0.171 · 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 teacher head, not a consensus.

Study designOther design
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

Citations19
Published2020
Admission routes2
Has abstractyes

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