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USER EXPERIENCE WITH MEDICATION ADHERENCE TECHNOLOGY: DETERMINING USABILITY BY CAPABILITIES

2024· dissertation· en· W7053520673 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2024
Typedissertation
Langueen
DomaineEngineering
ThématiqueLaser Design and Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésUsabilityAffect (linguistics)Medication adherenceQuality (philosophy)User experience designCognitionQuality of life (healthcare)Web usability
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: As populations worldwide grow older, the prevalence of chronic conditions and the complexity of managing multiple medications significantly increase. This challenge is further complicated by a range of barriers older adults face, including physical limitations, cognitive impairments, sensory issues, motivational challenges, and non-supportive environments. Such barriers can lead to a decline in capacity to self-manage medications, resulting in poor adherence to prescribed medication regimens, which in turn can cause increased hospitalizations and a decrease in quality of life. Medication Adherence Technologies (MATech), which range from simple electronic devices to more complex smart devices with connectivity and real-time monitoring capabilities, are recognized as one of the solutions to these challenges. However, the design and features of these technologies vary significantly, influencing how they are used by different users. Usability varies widely; some older adults may find certain features of these devices challenging to use due to their barriers. Hence, it is crucial to ensure that MATech are accessible and user-friendly for all older adults, regardless of their individual challenges. This study aims to identify the most suitable MATech for older adults with various physical, cognitive, sensory, motivational, and environmental limitations, tailored to their unique needs and abilities. \n \nObjectives: The primary objectives of this study were to evaluate the usability and user experience (UX) of thirteen MATech devices among older adults facing various barriers to medication self-management and to gather comprehensive feedback on the usability and features of these technologies. Secondary objectives included determining how different barriers affect the usability outcomes of these technologies and identifying design features that best meet the needs of this demographic to enhance their independence and well-being. \nMethods: The study used a mixed-methods approach to evaluate the usability of MATech. Eighty older adults, aged 60 and older, were recruited through convenience, purposive, and snowball sampling methods from various settings across Ontario, including academic and residential facilities. Data collection was conducted in three steps after obtaining informed consent from the participants. The first step involved measuring barriers to medication self-management using various scales such as the Self-Medication Assessment Tool (SMAT) for physical, cognitive, and vision barriers; the Whisper Test for hearing barriers; the Self-Efficacy for Medication Adherence Scale (SEAMS) for motivational barriers; and the Martin and Park Environmental Demands (MPED) Questionnaire for environmental barriers. The second step involved usability and user experience testing of three smart devices and ten electronic devices, to measure various performance-based metrics (task success rate, total task completion time, efficiency, error rate) and perception-based usability metrics (System Usability Scale (SUS) score, NASA-TLX workload score, Single Ease of Use Question (SEQ), and Subjective Mental Effort Question (SMEQ)). The third step consisted of in-depth qualitative interviews to explore feedback regarding the features of various MATech tested. Quantitative data were statistically analyzed using descriptive statistics and univariate and multivariate regression to assess usability across various devices, while qualitative responses were analyzed using inductive thematic analysis. \n \nResults: \nQuantitative Results: Cognitive impairments were identified in 20% of participants, physical limitations in 33.75%, hearing impairment (both ears) in 60%, and vision impairments in 11.25%. Backward stepwise multivariate regression analysis identified critical predictors for task success rates, including 'SEAMS score' (p<0.001) which measures motivational barrier positively influencing outcomes, whereas 'Low vision score' negatively affected success rates (p<0.001). Moreover, Old 'age' (p<0.001) and 'number of subtasks for product' (p<0.001) notably extended the total task completion times, and 'physical score' (p<0.001) increased error rates, suggesting necessary improvements in MATech design for better usability. While no predictors significantly impacted the SUS scores, the NASA TLX identified 'old age', 'vision impairment', and the ‘number of products tested’ as significant factors in perceived task load, particularly noting that using multiple products increased task load considerably, underscoring their profound impact on user experience and workload management. Predictive models were also developed to determine each participant's ability to successfully complete subtasks. For example, the model for a participant characterized by significant cognitive, physical, hearing, motivational and environmental impairments, but with high vision capacity, indicated high success probabilities for visually intensive subtasks such as "scroll the screen options" (92%) and "locate and touch an icon on a screen" (87%). Conversely, tasks requiring more physical interaction like "flip device" showed much lower success probabilities (45%). \nQualitative Findings: Five themes were identified: (1) the practicality of device design, (2) the impact of technological complexity, (3) the necessity for inclusivity in device functionality, which includes considerations for impairments, security, and privacy, (4) the influence of socio-economic and environmental factors, and (5) the importance of feedback for iterative design. \n \nDiscussion: The findings from this study underscore the critical importance of designing MATech that are not only functional but also tailored to the unique needs of older adults who face multiple barriers to effective medication management. Key findings from the regression analyses highlighted the importance of addressing physical and sensory impairments in MATech design, as these significantly influence user performance and error rates. Additionally, factors such as age and the complexity of device operations significantly influence usability and workload, suggesting the need for simpler, more intuitive designs that minimize cognitive and physical strain. Overall, the research emphasizes the need for a user-centered design approach in developing MATech, emphasizing simplicity, accessibility, and personalization to better support older adults in managing their medications effectively. This approach not only aids in improving medication adherence but also contributes to the broader goal of facilitating a more independent, quality life for older adults.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,025
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,199
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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