Assessment of Technology Adoption for Reminding Apps Designed for Persons with Dementia
|
If you are the presenter of this abstract (or if you cite this abstract in a talk or on a poster), please show the QR code in your slide or poster (QR code contains this URL). |
Abstract
It is known that people with dementia are generally reluctant to change their routine. Coupled with this is a fear of using technology based on a perceived embarrassment of not being able to use it or of making mistakes. With every unsuccessful attempt to introduce new technology or new methods aimed at helping to provide support with the symptoms of dementia the level of fear will be subsequently increased.
Taking this into consideration there is merit in assessing user characteristics and user needs as a basis to select which may be the most appropriate form of support to be introduced. To date, research within this area has been found to focus on the issues surrounding general technology adoption and the relevant factors, however, little attention has been directed towards the challenges associated with people with dementia and their carers. In addition, studies have focused on the utility of the technology and have not considered the ability to predict the long term success of adoption.
In our current work we are considering the development of technology adoption models which can be used to determine if persons with dementia and their carers will adopt a mobile app which offers a video based reminding service. The premise of the work is based upon considering both the carer and the person with dementia and whether their user characteristics, needs and perceptions can be used as the basis for input to a prediction model. The overarching goal is to develop a model which can be used as a screening process at the point of prescribing support to determine the likelihood that the dyad is likely to adopt and use the mobile reminding app. Unsuitable candidates will therefore not be burdened with an unsuccessful attempt of introducing a change, in the form of an assistive technology, to the established mechanisms.
In an initial study 11 patient specific parameters were extracted from data collected from 40 persons with dementia who had been involved with the evaluation of the mobile app. Parameters considered included, for example, cognitive ability, age, gender, infrastructure parameters, broadband connection, mobile phone reception and sociologic parameters. Based on this dataset a logistic regression model was developed with an accuracy of 80% to predict the likelihood of technology adoption.
Following this initial success, a further large scale study is now currently underway. Data will be gathered over a period of 12 months from 125 participants recruited from the Cache County Study on Memory in Aging (CCSMA). The CCSMA is a population-based epidemiological study of dementia which enrolled 5092 persons, 90% of the entire county residents age 65 and older. The data from the CCSMA have subsequently been linked to the Utah Population Database which incorporates genealogical, medical, vital and demographic records spanning an entire region. In our current work we propose to validate and extend our previously developed prediction model and explore factors and predictors for successful mobile app uptake in patients suffering from memory impairment. At present the Project is currently preparing the technology to be used during the evaluation phase in addition to recruiting the participants.
Taking this into consideration there is merit in assessing user characteristics and user needs as a basis to select which may be the most appropriate form of support to be introduced. To date, research within this area has been found to focus on the issues surrounding general technology adoption and the relevant factors, however, little attention has been directed towards the challenges associated with people with dementia and their carers. In addition, studies have focused on the utility of the technology and have not considered the ability to predict the long term success of adoption.
In our current work we are considering the development of technology adoption models which can be used to determine if persons with dementia and their carers will adopt a mobile app which offers a video based reminding service. The premise of the work is based upon considering both the carer and the person with dementia and whether their user characteristics, needs and perceptions can be used as the basis for input to a prediction model. The overarching goal is to develop a model which can be used as a screening process at the point of prescribing support to determine the likelihood that the dyad is likely to adopt and use the mobile reminding app. Unsuitable candidates will therefore not be burdened with an unsuccessful attempt of introducing a change, in the form of an assistive technology, to the established mechanisms.
In an initial study 11 patient specific parameters were extracted from data collected from 40 persons with dementia who had been involved with the evaluation of the mobile app. Parameters considered included, for example, cognitive ability, age, gender, infrastructure parameters, broadband connection, mobile phone reception and sociologic parameters. Based on this dataset a logistic regression model was developed with an accuracy of 80% to predict the likelihood of technology adoption.
Following this initial success, a further large scale study is now currently underway. Data will be gathered over a period of 12 months from 125 participants recruited from the Cache County Study on Memory in Aging (CCSMA). The CCSMA is a population-based epidemiological study of dementia which enrolled 5092 persons, 90% of the entire county residents age 65 and older. The data from the CCSMA have subsequently been linked to the Utah Population Database which incorporates genealogical, medical, vital and demographic records spanning an entire region. In our current work we propose to validate and extend our previously developed prediction model and explore factors and predictors for successful mobile app uptake in patients suffering from memory impairment. At present the Project is currently preparing the technology to be used during the evaluation phase in addition to recruiting the participants.
Medicine 2.0® is happy to support and promote other conferences and workshops in this area. Contact us to produce, disseminate and promote your conference or workshop under this label and in this event series. In addition, we are always looking for hosts of future World Congresses. Medicine 2.0® is a registered trademark of JMIR Publications Inc., the leading academic ehealth publisher.
This work is licensed under a Creative Commons Attribution 3.0 License.