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Record W2975155518 · doi:10.2196/15775

A Mobile Health App (Roadmap 2.0) for Patients Undergoing Hematopoietic Stem Cell Transplant: Qualitative Study on Family Caregivers' Perspectives and Design Considerations

2019· article· en· W2975155518 on OpenAlexvenueno aff
Dima Chaar, Ji Youn Shin, Amanda Mazzoli, Rebecca Vue, Jacob Kedroske, Grant Chappell, David A. Hanauer, Debra L. Barton, Afton L. Hassett, Sung Won Choi

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood Institute
KeywordsThematic analysisMedicineQualitative researchPsychological interventionmHealthTransplantationCoding (social sciences)GerontologyPsychologyNursingFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hematopoietic stem cell transplantation (HCT), also referred to as blood and marrow transplantation (BMT), is a high-risk, but potentially curative therapy for a number of cancer and noncancer conditions. BMT Roadmap (Roadmap 1.0) is a mobile health app that was developed as a family caregiver-facing tool to provide informational needs about the health status of patients undergoing inpatient HCT. OBJECTIVE: This study explored the views and perceptions of family caregivers of patients undergoing HCT and their input regarding further technology development and expansion of BMT Roadmap into the outpatient setting (referred to as Roadmap 2.0). METHODS: Semistructured qualitative interviews were conducted among 24 family caregivers. Questions were developed from existing literature coupled with prior in-depth observations and interviews in hospital-based settings to explore the study objectives. Participants were recruited during routine outpatient clinic appointments of HCT patients, and all interviews were conducted in the participants' homes, the setting in which Roadmap 2.0 is intended for use. A thematic analysis was performed using a consistent set of codes derived from our prior research. New emerging codes were also included, and the coding structure was refined with iterative cycles of coding and data collection. RESULTS: Four major themes emerged through our qualitative analysis: (1) stress related to balancing caregiving duties; (2) learning and adapting to new routines (resilience); (3) balancing one's own needs with the patient's needs (insight); and (4) benefits of caregiving. When caregivers were further probed about their views on engagement with positive activity interventions (ie, pleasant activities that promote positive emotions and well-being such as expressing gratitude or engaging in activities that promote positive thoughts, emotions, and behaviors), they preferred a "menu" of positive activities to help support caregiver health and well-being. CONCLUSIONS: This study involved family caregivers as participants in the development of new components for Roadmap 2.0. Our research provided a further understanding of the many priorities that hematopoietic stem cell transplant family caregivers face while maintaining balance in their lives. Their schedules can often be unpredictable, even more so once the patient is discharged from the hospital. Our findings suggest that expanding Roadmap 2.0 into the outpatient setting may provide critical caregiver support and that HCT caregivers are interested in and willing to engage in positive activities that may enhance well-being and attenuate the stress associated with caregiving. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.4918.

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)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.375
Teacher spread0.319 · 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 designQualitative
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".

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Citations36
Published2019
Admission routes1
Has abstractyes

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