Factors influencing behavioural intention to use a smart shoe insole in regionally based adults with diabetes: a mixed methods study
| Autor: | Michael Kingsley, Nerida Hyett, Emma M. Macdonald, Byron Perrin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Gerontology
Male Telemedicine lcsh:Diseases of the musculoskeletal system Peripheral neuropathy Smart insole medicine.medical_treatment Health Behavior Foot ulceration Foot Orthoses Monitoring Ambulatory Intention Unified theory of acceptance and use of technology 03 medical and health sciences Wearable Electronic Devices 0302 clinical medicine Diabetes mellitus Medicine Humans Orthopedics and Sports Medicine Aged 030203 arthritis & rheumatology Expectancy theory Rehabilitation business.industry Research Wearable technology Australia Regression analysis Biofeedback Psychology 030229 sport sciences Focus Groups Middle Aged Focus group Diabetic Foot Shoes Smart Materials Patient Compliance Female lcsh:RC925-935 Thematic analysis business Psychosocial Attitude to Health |
| Zdroj: | Journal of Foot and Ankle Research Journal of Foot and Ankle Research, Vol 12, Iss 1, Pp 1-9 (2019) |
| ISSN: | 1757-1146 |
| Popis: | Background Smart insole technologies that provide biofeedback on foot health can support foot-care in adults with diabetes. However, the factors that influence patient uptake and acceptance of this technology are unclear. Therefore, the aim of this mixed-methods study was to use an established theoretical framework to determine a model of psychosocial factors that best predicts participant intention to use smart insoles. Methods Fifty-three adults with diabetes from regional Australia completed the validated Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Multiple regression analysis was used to determine the psychosocial factors that best predict behavioural intention to adopt a smart insole. Additionally, a focus group was conducted and thematic analysis was performed to explore barriers and enablers to adopting this technology. Results The multiple regression model that best predicted intention to adopt the smart insole (adjusted R2 = 0.51, p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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