Measuring technology self efficacy: reliability and construct validity of a modified computer self efficacy scale in a clinical rehabilitation setting

Author version made available in accordance with the Publisher's policy.Purpose: To describe a modification of the Computer Self Efficacy Scale for use in clinical settings and to report on the modified scale’s reliability and construct validity. Methods: The Computer Self Efficacy Scale was modified to make it applicable for clinical settings (for use with older people or people with disabilities using everyday technologies). The modified scale was piloted, then tested with patients in an Australian inpatient rehabilitation setting (n=88) to determine the internal consistency using Cronbach’s alpha coefficient. Construct validity was assessed by correlation of the scale with age and technology use. Factor analysis using principal components analysis was undertaken to identify important constructs within the scale. Results: The modified Computer Self Efficacy scale demonstrated high internal consistency with a standardised alpha coefficient of 0.94. Two constructs within the scale were apparent; using the technology alone, and using the technology with the support of others. Scores on the scale were correlated with age and frequency of use of some technologies thereby supporting construct validity. Conclusions: The modified Computer Self Efficacy Scale has demonstrated reliability and construct validity for measuring the self efficacy of older people or people with disabilities when using everyday technologies. This tool has the potential to assist clinicians in identifying older patients who may be more open to using new technologies to maintain independence

Sex and gender differences in technology needs and preferences among informal caregivers of persons with dementia

Background: Dementia is a major public health concern associated with significant caregiver demands and there are technologies available to assist with caregiving. However, there is a paucity of information on caregiver needs and preferences for these technologies, particularly from a sex and gender perspective. To address this gap in research, the objectives of this study are to examine (1) the knowledge of technology, (2) perceived usefulness of technology, (3) feature preferences when installing and using technology and (4) sex and gender influences on technology needs and preferences among family caregivers of persons with dementia (PWD) across North America. Methods: A secondary analysis was conducted on an existing cross-sectional survey with family caregivers of PWDs. Respondents were recruited through the Alzheimer Society of Canada, the Victorian Order of Nurses and Adult Day Programs and other Canadian health care provision institutes. Descriptive statistics, bivariate and multivariate analyses were used to describe the study sample, uncover differences between male and female caregivers and examine sex and gender influences on caregivers’ technology needs and preferences. Results: A total of 381 eligible responses were received over a nine-month data collection period. The majority of respondents did not know much about and never used any technologies to assist with caregiving. “Being easy to install”, “easy to learn how to use” and “cost” were identified as the most important features when purchasing and setting up technology, while “reliability” was identified as the most important feature when using technology. Most respondents were willing to pay up to $500 to acquire individual technologies. Controlling for other socio-demographic variables, female respondents were more likely to have some or more knowledge about technology for caregiving while male respondents were more willing to pay higher amounts for these technologies compared to their female counterparts

Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AIpublishedVersio

Evaluating Evidence-Based Content, Features of Exercise Instruction, and Expert Involvement in Physical Activity Apps for Pregnant Women: Systematic Search and Content Analysis

Background: Guidelines for physical activity and exercise during pregnancy recommend that all women without contraindications engage in regular physical activity to improve both their own health and the health of their baby. Many women are uncertain how to safely engage in physical activity and exercise during this life stage and are increasingly using mobile apps to access health-relatedinformation. However, the extent to which apps that provide physical activity and exercise advice align with current evidence-based pregnancy recommendations is unclear. Objective: This study aims to conduct a systematic search and content analysis of apps that promote physical activity and exercise in pregnancy to examine the alignment of the content with current evidence-based recommendations; delivery, format, and features of physical activity and exercise instruction; and credentials of the app developers. Methods: Systematic searches were conducted in the Australian App Store and Google Play Store in October 2020. Apps were identified using combinations of search terms relevant to pregnancy and exercise or physical activity and screened for inclusion (with a primary focus on physical activity and exercise during pregnancy, free to download or did not require immediate paid subscription, and an average user rating of ≥4 out of 5). Apps were then independently reviewed using an author-designed extraction tool. Results: Overall, 27 apps were included in this review (Google Play Store: 16/27, 59%, and App Store: 11/27, 41%). Two-thirds of the apps provided some information relating to the frequency, intensity, time, and type principles of exercise; only 11% (3/27) provided this information in line with current evidence-based guidelines. Approximately one-third of the apps provided information about contraindications to exercise during pregnancy and referenced the supporting evidence. None of the apps actively engaged in screening for potential contraindications. Only 15% (4/27) of the apps collected information about the user\u27s current exercise behaviors, 11% (3/27) allowed users to personalize features relating to their exercise preferences, and a little more than one-third provided information about developer credentials. Conclusions: Few exercise apps designed for pregnancy aligned with current evidence-based physical activity guidelines. None of the apps screened users for contraindications to physical activity and exercise during pregnancy, and most lacked appropriate personalization features to account for an individual\u27s characteristics. Few involved qualified experts during the development of the app. There is a need to improve the quality of apps that promote exercise in pregnancy to ensure that women are appropriately supported to engage in exercise and the potential risk of injury, complications, and adverse pregnancy outcomes for both mother and child is minimized. This could be done by providing expert guidance that aligns with current recommendations, introducing screening measures and features that enable personalization and tailoring to individual users, or by developing a recognized system for regulating apps

Speech analysis for Ambient Assisted Living : technical and user design of a vocal order system

International audienceEvolution of ICT led to the emergence of smart home. A Smart Home consists in a home equipped with data-processing technology which anticipates the needs of its inhabitant while trying to maintain their comfort and their safety by action on the house and by implementing connections with the outside world. Therefore, smart homes equipped with ambient intelligence technology constitute a promising direction to enable the growing number of elderly to continue to live in their own homes as long as possible. However, the technological solutions requested by this part of the population have to suit their specific needs and capabilities. It is obvious that these Smart Houses tend to be equipped with devices whose interfaces are increasingly complex and become difficult to control by the user. The people the most likely to benefit from these new technologies are the people in loss of autonomy such as the disabled people or the elderly which cognitive deficiencies (Alzheimer). Moreover, these people are the less capable of using the complex interfaces due to their handicap or their lack ICT understanding. Thus, it becomes essential to facilitate the daily life and the access to the whole home automation system through the smart home. The usual tactile interfaces should be supplemented by accessible interfaces, in particular, thanks to a system reactive to the voice ; these interfaces are also useful when the person cannot move easily. Vocal orders will allow the following functionality: - To ensure an assistance by a traditional or vocal order. - To set up a indirect order regulation for a better energy management. - To reinforce the link with the relatives by the integration of interfaces dedicated and adapted to the person in loss of autonomy. - To ensure more safety by detection of distress situations and when someone is breaking in the house. This chapter will describe the different steps which are needed for the conception of an audio ambient system. The first step is related to the acceptability and the objection aspects by the end users and we will report a user evaluation assessing the acceptance and the fear of this new technology. The experience aimed at testing three important aspects of speech interaction: voice command, communication with the outside world, home automation system interrupting a person's activity. The experiment was conducted in a smart home with a voice command using a Wizard of OZ technique and gave information of great interest. The second step is related to a general presentation of the audio sensing technology for ambient assisted living. Different aspect of sound and speech processing will be developed. The applications and challenges will be presented. The third step is related to speech recognition in the home environment. Automatic Speech Recognition systems (ASR) have reached good performances with close talking microphones (e.g., head-set), but the performances decrease significantly as soon as the microphone is moved away from the mouth of the speaker (e.g., when the microphone is set in the ceiling). This deterioration is due to a broad variety of effects including reverberation and presence of undetermined background noise such as TV radio and, devices. This part will present a system of vocal order recognition in distant speech context. This system was evaluated in a dedicated flat thanks to some experiments. This chapter will then conclude with a discussion on the interest of the speech modality concerning the Ambient Assisted Living

Evaluation of a context-aware voice interface for Ambient Assisted Living: qualitative user study vs. quantitative system evaluation

International audienceThis paper presents an experiment with seniors and people with visual impairment in a voice-controlled smart home using the SWEET-HOME system. The experiment shows some weaknesses in automatic speech recognition which must be addressed, as well as the need of better adaptation to the user and the environment. Indeed, users were disturbed by the rigid structure of the grammar and were eager to adapt it to their own preferences. Surprisingly, while no humanoid aspect was introduced in the system, the senior participants were inclined to embody the system. Despite these aspects to improve, the system has been favourably assessed as diminishing most participant fears related to the loss of autonomy

Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals’ beliefs about AI’s influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale’s structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users’ general attitudes toward AI

Diseño de una plataforma web para el seguimiento de pacientes a través de datos recogidos con pulseras de actividad

La mHealth está emergiendo rápidamente como un medio importante para el control de enfermedades crónicas, que son la principal causa de muerte e incapacidad del mundo. Además, la necesidad de repensar las estructuras tradicionales de los servicios de salud y las posibilidades del uso mixto de los wereables y las apps para ayudar a mejorar la calidad de vida de la gente, han motivado la realización de este estudio piloto. En este contexto, el proyecto actual presenta el diseño de la interfaz, diseño gráfico y testeo del prototipo funcional de una nueva aplicación móvil Android de mHealth para el seguimiento integral y monitorización de pacientes crónicos llamada Tuende. Esta monitorización se lleva a cabo a través de la combinación de datos recogidos con pulseras de actividad compatibles con Google Fit e información subjetiva de la percepción de la enfermedad aportada por el paciente. Este rasgo diferenciador con el resto de aplicaciones del mercado posibilita que el paciente tome una parte activa en su cuidado, involucrándose en su tratamiento, lo que favorece una mejor adaptación a este. Además, toda esta información permite a los profesionales sanitarios poder ofrecer una atención personalizada, ajustando los tratamientos según las necesidades de cada paciente. Algunas de las funciones más relevantes que la plataforma incorpora son: la interacción social entre usuarios de la aplicación que, junto con el uso de las mecánicas de gamificación por desafíos, ayudará al paciente a estar motivado en el uso de la plataforma, sin percibir las tareas como una obligación, y la comunicación telemática entre el paciente y el personal sanitario, ofreciendo una alternativa más eficiente y asequible de los servicios de atención sanitaria. El proceso de diseño centrado en el usuario a través de la evaluación de las iteraciones del prototipo ha permitido desarrollar una plataforma intuitiva, fácil de usar y accesible a cualquier usuario que sepa manejar un dispositivo móvil con cierta soltura, como indican los resultados de los test con usuarios.<br /