MétaCan
Menu
Back to cohort
Record W3120825983 · doi:10.1177/0733464820983630

The Effect of Telehealth Interventions on Function and Quality of Life for Older Adults with Pre-Frailty or Frailty: A Systematic Review and Meta-Analysis

2021· review· en· W3120825983 on OpenAlexaff
Elham Esfandiari, William C. Miller, Maureen C. Ashe

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsGF Strong Rehabilitation CentreUniversity of British Columbia
Fundersnot available
KeywordsTelehealthPsychological interventionPsycINFOMedicineRandomized controlled trialMEDLINEMeta-analysisCochrane LibraryCINAHLQuality of life (healthcare)Systematic reviewGerontologyPhysical therapyHealth careTelemedicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Telehealth interventions improve health outcomes by increasing access to care. We conducted a systematic review to synthesize evidence on the effect of telehealth interventions compared with no intervention or usual care for older adults with pre-frailty or frailty for physical function, quality of life (QOL), and frailty. We searched for randomized controlled trials (RCTs) in MEDLINE, PubMed, Embase, CINAHL, Cochrane, PsycINFO, and SPORTDiscus. Two authors reviewed records and assessed risk of bias. A narrative synthesis of findings was conducted. When appropriate, the standard mean difference (SMD) was used to compare telehealth interventions with control conditions. We used GRADE to determine the certainty of the evidence. Twelve RCTs were included. Low certainty evidence highlighted positive effects for the function and mental component of QOL favoring telehealth interventions (SMD = 0.31, 95% CI = [0.15, 0.47]; and SMD = 0.43, 95% CI = [0.22, 0.64], respectively). Despite a small positive effect of telehealth interventions, insufficient, and low certainty evidence precludes making definitive recommendations.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.726
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0150.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.156
GPT teacher head0.434
Teacher spread0.278 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations28
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied GerontologySame topicFrailty in Older AdultsFrench-language works237,207