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Record W2496749922 · doi:10.2196/mhealth.3726

Variations in the Use of mHealth Tools: The VA Mobile Health Study

2016· article· en· W2496749922 on OpenAlexvenueno aff
Kathleen L Frisbee

Bibliographic record

VenueJMIR mhealth and uhealth · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsmHealthSpousePreparednessMental healthFamily caregiversCaregiver burdenMedicineVeterans AffairsGerontologyHealth carePsychological interventionPsychologyFamily medicinePsychiatryDementiaDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Mobile health (mHealth) technologies exhibit promise for offering patients and their caregivers point-of-need tools for health self-management. This research study involved the dissemination of iPads containing a suite of mHealth apps to family caregivers of veterans who receive care from the Veterans Affairs (VA) Health Administration and have serious physical or mental injuries. OBJECTIVE: The goal of the study was to identify factors and characteristics of veterans and their family caregivers that predict the use of mHealth apps. METHODS: Veteran/family caregiver dyads (N=882) enrolled in VA's Comprehensive Assistance for Family Caregivers program were recruited to participate in an mHealth pilot program. Veterans and caregivers who participated and received an iPad agreed to have their use of the apps monitored and were asked to complete a survey assessing Caregiver Preparedness, Caregiver Traits, and Caregiver Zarit Burden Inventory baseline surveys. RESULTS: Of the 882 dyads, 94.9% (837/882) of caregivers were women and 95.7% (844/882) of veteran recipients were men. Mean caregiver age was 40 (SD 10.2) years and mean veteran age was 39 (SD 9.15) years, and 39.8% (351/882) lived in rural locations. Most (89%, 788/882) of the caregivers were spouses. Overall, the most frequently used app was Summary of Care, followed by RX Refill, then Journal, Care4Caregivers, VA Pain Coach, and last, VA PTSD Coach. App use was significantly predicted by the caregiver being a spouse, increased caregiver computer skills, a rural living location, lower levels of caregiver preparedness, veteran mental health diagnosis (other than posttraumatic stress disorder), and veteran age. CONCLUSIONS: This mHealth Family Caregiver pilot project effectively establishes the VA's first patient-facing mHealth apps that are integrated within the VA data system. Use varied considerably, and apps that were most used were those that assisted them in their caregiving responsibilities.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.230
GPT teacher head0.500
Teacher spread0.270 · 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 designObservational
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

Citations26
Published2016
Admission routes1
Has abstractyes

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