MétaCan
Menu
Back to cohort
Record W4399875019 · doi:10.2196/53513

Medication Management Strategies to Support Medication Adherence: Interview Study With Older Adults

2024· article· en· W4399875019 on OpenAlexvenueno aff
Lisa Gualtieri, Mathilda Rigby, Deelia Wang, Elaine Mann

Bibliographic record

VenueInteractive Journal of Medical Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsThematic analysisMedical prescriptionMedicineQualitative researchPsychological interventionFamily medicineMedication adherenceMedication therapy managementMEDLINEPopulationGerontologyNursingPharmacyPharmacist

Abstract

fetched live from OpenAlex

BACKGROUND: Home medication management has been insufficiently studied, including the factors that impact the development and effectiveness of adherence strategies under both routine and anomalous circumstances. Older adults are a particularly important population to study due to the greater likelihood of taking medication in combination with the desire to "age in place." OBJECTIVE: This interview study aims to understand how older adults develop medication management strategies, identify when and why such strategies succeed or fail, learn more about how older adults think about their medication, and explore interventions that increase medication adherence. METHODS: This study used a qualitative, semistructured interview design to elicit older adults' experiences with home medication management. Overall, 22 participants aged ≥50 years taking 1 to 3 prescription medications were recruited and interviewed. Interview responses were recorded, and thematic, qualitative analysis was performed by reviewing recordings and identifying recurring patterns and themes. Responses were systematically coded, which not only facilitated the identification of these themes but also allowed us to quantify the prevalence of behaviors and perceptions, providing a robust understanding of medication management and medication adherence. RESULTS: Participants reported developing home medication management strategies on their own, with none of the participants receiving guidance from health care providers and 59% (13/22) of the participants using trial and error. The strategies developed by study participants were all unique and generally encompassed prescription medication and vitamins or supplements, with no demarcation between what was prescribed or recommended by a physician and what they selected independently. Participants thought about their medications by their chemical name (10/22, 45%), by the appearance of the pill (8/22, 36%), by the medication's purpose (2/22, 9%), or by the medication's generic name (2/22, 9%). Pill cases (17/22, 77%) were more popular than prescription bottles (5/22, 23%) for storage of daily medication. Most participants (19/22, 86%) stored their pill cases or prescription bottles in visible locations in the home, and those using pill cases varied in their refill routines. Participants used ≥2 routines or objects as triggers to take their medication. Nonadherence was associated with a disruption to their routine. Finally, only 14% (3/22) of the participants used a time-based reminder or alarm, and none of the participants used a medication adherence device or app. CONCLUSIONS: Participants in our study varied considerably in their home medication management strategies and developed unique routines to remember to take their medication as well as to refill their pill cases. To reduce trial and error in establishing a strategy, there are opportunities for physicians and pharmacists to provide adherence guidance to older adults. To minimize the impact of disruptions on adherence, there are opportunities to develop more durable strategies and to design aids to medication adherence that leverage established daily routines.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.131
GPT teacher head0.520
Teacher spread0.390 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations10
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInteractive Journal of Medical ResearchSame topicMedication Adherence and ComplianceFrench-language works237,207