The facilitators and barriers of mHealth adoption and use among people with a low socio-economic position: A scoping review

Background Despite the fact that 95% of the global population has a mobile phone, the adoption of mHealth lags among people with a low socio-economic position (SEP). As they face health risks and many barriers in the traditional offline healthcare system, mHealth has an important role. Therefore, it is important to understand the factors that promote and impede mHealth adoption among people with a lower SEP. Objective The current study aims to provide an overview of what is known about the facilitators and barriers to the adoption and use of autonomous mHealth applications among people with low SEP. Methods A PRISMA scoping review in which the scientific databases PubMed, Web of Science, PsychInfo and SocINDEX were searched in the period of March 2017 to March 2022. Results Of the 1827 indexed papers, 13 papers were included in the review. In these papers, 30 factors have been identified as promoting or hindering the adoption of autonomous mHealth applications among low SEP people. Conclusions Thirty factors were found to facilitate or impede mHealth adoption among people with a low SEP, categorised into intrapersonal, interpersonal, community, ecological and app specific levels. Factors are assumed to be interrelated. The relationship between traditional (offline) care and digital care appeared to be of particular interest as the current study revealed that face-to-face contact is a prerequisite of mHealth adoption among people with low SEP. Therefore, a well-structured cosmopolitan system of stakeholders has been recommended. Trial registration This study was registered in OSF (https://doi.org/10.17605/OSF.IO/ATU9D)

How to persuade more primary care professionals to adopt a valued smoking cessation referral aid:a cross-sectional study of facilitators and barriers

BACKGROUND: To study the factors associated with the intention of primary care professionals (PCPs) to use or not use a referral aid (RA) for selecting an evidence-based smoking cessation intervention (EBSCI). METHODS: Participants (n = 85) were recruited from June to September 2020 to complete an online questionnaire based on the I-Change Model to assess the factors associated with the adoption of RA. The differences between PCPs with (n = 37) and without (n = 48) the intention to adopt in terms of demographics, motivational factors, and post-motivational factors were subsequently assessed. Correlation and logistic regression analyses were conducted to investigate the factors associated with the intention to adopt. RESULTS: Both groups indicated that they highly appreciated the RA. However, PCPs without the intention to adopt expressed a more negative attitude towards the RA, experienced less social support, showed low self-efficacy, and encountered barriers such as lack of time and skills. The factors most strongly associated with the intention to adopt were advantages, disadvantages, self-efficacy, less barriers, working in a solo practice and age. CONCLUSIONS: The adoption of RA can be facilitated in two ways. The first one is by increasing the added value of the tool through a second round of co-creation focusing on the adoptability of the RA in practice. The second approach is by communicating the added value of referring to EBSCIS and thereby using the RA by implementing it in smoking cessation training for PCPs, which could also help to improve the attitude, social support, self-efficacy, and perceived skills in terms of RA usage among PCPs. IMPACT: This study is the first work in the Netherlands to investigate the willingness of PCPs to actively refer patients to other EBSCIs in addition to providing face-to-face counseling themselves. TRIAL REGISTRATION: The study was registered at the Netherlands Trial Register (NL7020, https://www.trialregister.nl/trial/7020)

An eHealth Intervention to Promote Physical Activity and Social Network of Single, Chronically Impaired Older Adults: Adaptation of an Existing Intervention Using Intervention Mapping

BACKGROUND: Especially for single older adults with chronic diseases, physical inactivity and a poor social network are regarded as serious threats to their health and independence. The Active Plus intervention is an automated computer-tailored eHealth intervention that has been proven effective to promote physical activity (PA) in the general population of adults older than 50 years. OBJECTIVE: The aim of this study was to report on the methods and results of the systematic adaptation of Active Plus to the wishes and needs of the subgroup of single people older than 65 years who have one or more chronic diseases, as this specific target population may encounter specific challenges regarding PA and social network. METHODS: The Intervention Mapping (IM) protocol was used to systematically adapt the existing intervention to optimally suit this specific target population. A literature study was performed, and quantitative as well as qualitative data were derived from health care professionals (by questionnaires, n=10) and the target population (by focus group interviews, n=14), which were then systematically integrated into the adapted intervention. RESULTS: As the health problems and the targeted behavior are largely the same in the original and adapted intervention, the outcome of the needs assessment was that the performance objectives remained the same. As found in the literature study and in data derived from health professionals and focus groups, the relative importance and operationalization of the relevant psychosocial determinants related to these objectives are different from the original intervention, resulting in a refinement of the change objectives to optimally fit the specific target population. This refinement also resulted in changes in the practical applications, program components, intervention materials, and the evaluation and implementation strategy for the subgroup of single, chronically impaired older adults. CONCLUSIONS: This study demonstrates that the adaptation of an existing intervention is an intensive process in which adopting the IM protocol is an invaluable tool. The study provides a broad insight in adapting interventions aimed at single older adults with a chronic disease. It is concluded that even when the new target population is a sizable segment of the original target population, the adapted intervention still needs considerable changes to optimally fit the needs and situational differences of the narrower target population

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply gen-eral machine learning methods to this application domain, and Bayesian networks in particular. We investigate several suitable methods for deal-ing with missing data, and determine which method obtains good results in terms of fitting the data. Furthermore, we present the learnt Bayesian network model for this e-health intervention case study, and conclusions are drawn about determinants of physical activity behaviour change and how the intervention affects physical activity behaviour and its determi-nants. We also evaluate the contributions of Bayesian network analysis compared to traditional statistical analyses in this field. Finally, possible extensions on the performed analyses are proposed