Socioeconomic differences in older adults’ intention to use Mhealth applications

Socioeconomic differences in older adults’ intention to use Mhealth applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic differences in older adults’ intention to use Mhealth applications Floris Elburg, Anna Petra Nieboer, Marjan Askari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5029618/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in BMC Geriatrics → Version 1 posted 8 You are reading this latest preprint version Abstract Background Older adults with lower socioeconomic status (SES) are most affected by chronic diseases and in need of effective interventions to manage their conditions. Despite the proven benefits of mobile health (mHealth) in chronic disease management, the stimulation of mHealth adoption among people with lower SES is challenging. Such socioeconomic differences have not been investigated among older adults. The aims of this study were to identify factors associated with mHealth acceptance among older adults in the Netherlands with low and non-low SES, and to determine whether the influences of these factors differ between socioeconomic groups. Methods This cross-sectional study was performed using a questionnaire based on technology acceptance model (TAM) factors. The participants were aged ≥ 65 years, lived independently or in senior living facilities, and had no cognitive impairment. Associations between average TAM factor scores and respondents’ intention to use mHealth applications were analyzed separately for low- and non–low-SES groups using controlled multivariable logistic regression. Models including interaction terms were then computed to investigate differences between groups. Results The sample comprised 360 respondents (mean age, 74.9 ± 7.0 years). Scores for the eight TAM factors were significantly lower in the low-SES group than in the non–low-SES group, indicating that it is more difficult to motivate the former to use mHealth. All factors except digital health anxiety (anxiety) and social relationships were associated significantly with the use intention in the low-SES group, and all factors showed significant associations in the non–low-SES group. The models including interaction terms showed that perceived usefulness, perceived ease of use, and service availability had significantly stronger relationships with the intention to use mHealth in the non–low-SES group than in the low-SES group. Conclusion This study revealed differences in the associations of TAM factors with the intention to use mHealth applications between older adults with low and non-low SES. A stronger and more comprehensive approach is needed to stimulate mHealth adoption among low-SES older adults. Policy development with the consideration of specific TAM factors will increase mHealth adoption among older adults. To reduce health disparities, policies should be tailored to older adults’ needs. Trial registration Clinical trial number: not applicable. technology acceptance model intention to use older adult socioeconomic status mHealth Introduction The global population of adults aged 65 and older is projected to triple, rising from 562 million in 2012 to 1.6 billion by 2050, and will represent 16.7% of the world’s population [ 1 ]. With populational aging in the Netherlands, the prevalence of people with one or more chronic diseases is increasing; 85% of Dutch older adults have at least one chronic condition [ 2 ]. Socioeconomic status (SES), commonly defined in health-related research as a combination of income and education level, is associated with chronic disease prevalence and mortality [ 3 – 11 ]. The risk of chronic disease development is twice as high for Dutch older adults with lower SES than for those with higher SES [ 12 ]. Moreover, Dutch adults with low SES have less knowledge about their conditions, and have more difficulty managing them and following therapeutic regimens [ 13 ]. To reduce disparities in health among socioeconomic groups, the ability of those who are at a disadvantage to use (digital) health interventions is crucial [ 14 , 15 ]. Mobile health (mHealth) has been proven to contribute to the treatment and management of chronic diseases [ 16 , 17 ]. The World Health Organization has defined mHealth as “the use of mobile wireless technologies for public health,” and stated that “mHealth has been shown to improve the quality and coverage of care, increase access to health information, services and skills, as well as promote positive changes in health behaviours to prevent the onset of acute and chronic diseases” [ 18 , p. 2]. mHealth applications have also been promised to overcome socioeconomic barriers [ 19 , 20 ], and their implementation is believed to improve healthcare quality and accessibility for groups with low SES [ 21 , 22 ]. For this reason, the implementation of digital applications has been an important pillar of the development of policies related to the treatment of chronic diseases in the Netherlands [ 13 , 23 ]. Concordantly, the Dutch government invests in digital health applications to stimulate remote care [ 24 ], and the numbers of digital innovations and services introduced in healthcare are increasing rapidly [ 13 ]. Despite the versatility of mHealth applications and the governmental stimulation of their implementation, the rate of mHealth use among older adults remains low [ 25 – 28 ]. People with low SES are also less likely to use mHealth, although this disparity has not been studied in the older adult population [ 14 , 29 – 36 ]. This research gap is problematic, as older adults with low SES are most vulnerable and are often most in need of effective interventions to manage their chronic diseases [ 21 ]. To develop effective interventions for people with low SES, a better understanding of disparities in mHealth adoption among older adults from different socioeconomic classes is needed. To investigate how to enable the use of mHealth among older adults with low SES, we applied the extended technology acceptance model (TAM) in this study. The TAM, introduced in 1989 by Davis [ 37 ], states that technology adoption can be predicted solely by one’s intention to use that specific technology, which in turn is influenced by the technology’s perceived usefulness and ease of use. It has been modified to better fit the context of health technology adoption, with the addition of other factors that influence the intention to use [ 16 , 38 – 42 ]. The TAM has been used widely to study mHealth adoption in the general population [ 43 – 45 ] and among older adults [ 26 , 28 , 39 , 46 , 47 ]. However, evidence for the intention to use mHealth in low-SES populations is limited. In the general population, higher perceived usefulness has been found to be an important factor for health application use; when present, low- and high-SES groups are equally likely to use health technologies [ 35 ]. Furthermore, the intention to use mHealth among people with low SES was found to be influenced more by social networks than it is that among people with high SES [ 35 ]. Additionally, people with lower socioeconomic backgrounds have been found to feel more anxiety about the use of digital health technology, and to be more reluctant to do so [ 32 ]. Although these studies provide evidence on important factors for technology acceptance among different socioeconomic groups in the general population, no research has explored socioeconomic differences in the relationships between technology acceptance factors and the intention to use mHealth specifically among older adults. Thus, this study was performed to investigate differences between socioeconomic groups in factors influencing the intention to use mHealth applications in a large sample of older adults in the Netherlands. The aims were to identify the most important factors for mHealth use among older adults with low and non-low SES, and differences between these groups in the associations of technology acceptance factors with the intention to use mHealth. METHODS Study design and data collection Data were collected through the distribution of a questionnaire among Dutch older adults between January 1st and June 1st 2020. The study cohort consisted of participants aged 65 and older, free of cognitive impairments and living either independently or in senior living facilities. Participants were recruited through multiple channels, including elderly living facilities, healthcare organizations (a hospital, general practitioners, and a home care organization), leisure activity clubs for older adults, and social media platforms in different regions in the Netherlands. The data have mainly been collected in the regions of Noord-Brabant, Utrecht, and Zuid-Holland. To enhance accessibility and encourage participation, the questionnaire was made available in both paper and digital formats”. The questionnaire was validated by a geriatric nurse, a diversity expert, and two eHealth experts and is available upon request. In addition, we collected feedback from four older adults to improve the questionnaire’s readability. The reporting of the online questionnaire follows the Checklist for Reporting Results of Internet E-Surveys (Multimedia Appendix 1). The participants were informed of the purpose of the study, the names of the main researchers, sources of assistance, how to fill in the questionnaire, what mHealth applications are (with multiple examples), how their data would be managed, and how their privacy would be safeguarded. Data assistants provided any assistance or additional information that participants needed by telephone or email, or in person. The online questionnaires were filled in anonymously. All respondents provided written informed consent, and the study was approved by the Medical Ethics Commission of the Erasmus University Medical Center (MEC-2018-120). The data were entered into an SPSS database (IBM Corporation, Armonk, NY, USA). Their representativeness of the general older adult population in the Netherlands was checked by comparison with data from Statistics Netherlands. To increase external validity, the data assistants collected data in different regions of the Netherlands. Measurement of variables Demographic data, including age and gender, were collected, along with information on health literacy, prior internet use, and prior experience with medical apps. The latter three variables were measured as binary (yes/no) responses. Independent variables The examination of differences between SES groups in the intention to use mHealth applications was based on factors from an adapted version of the TAM, used previously to study the intention to use mHealth in older adult populations [ 26 – 28 , 48 ]. We included the following factors as independent variables: perceived ease of use (four statements, e.g., “It is easy to use medical applications for remote care”), perceived usefulness (three statements, e.g., “I find it useful to use medical applications for remote care”), attitude toward use (four statements, e.g., “Using medical applications for remote care would be a good idea”), and subjective norms (three statements, e.g., “People who are important to me think I should use medical applications for remote care”). We also adopted the following factors that are specific to older adults: sense of control (two statements, e.g., “I have resources, knowledge and ability to use medical applications for remote care”), anxiety toward use (two statements, e.g., “I hesitate to use medical applications for remote care for fear of making mistakes that I cannot correct”), personal innovativeness (four statements, e.g., “I like to experiment with new information technology”), social relationships (three statements, e.g., “I am satisfied with the support of my family and friends”), perceived effectiveness of use (two statements, e.g., “I could perform a task on a medical application if I only had the manual”), service availability (three statements, e.g., “I have the ability to use medical applications for remote care anytime, anywhere”), and facilitating circumstances (five statements, e.g., “I have the knowledge needed to use medical applications”). Full descriptions of the TAM factors can be found in Appendix 2. Responses were provided on a 5-point Likert scale (1 = completely disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = completely agree). For each TAM factor, an average score was calculated and used in the analysis. Dependent variables Given that the intention to use can be seen as the primary determinant of technology use [ 37 ], this intention served as the outcome variable in this study. It was measured using three statements (e.g., “Assuming I have access to medical applications for remote care, I plan to use them”) to which responses were provided on a 5-point Likert scale. The mean score was calculated, and for ease of interpretation the outcome variable was dichotomized, with an average score of 3 or higher coded as 1 (indicating intention to use). The SES factor was composed of respondents’ income and education level. Eight levels of educational attainment were distinguished, ranging from no education to university education. Income was measured as the net monthly household income on a 5-point ordinal scale ranging from “less than 1000” to “more than 3500” euros. Education and income were transformed into binary variables. The threshold for low income was set at 1750 euros based on the national standard [ 48 ]. Of the three educational levels defined by Statistics Netherlands, the middle and high levels were combined, resulting in the categories of lower (up to senior general secondary) education and higher (senior general secondary and/or higher) education [ 50 ]. The low-SES group consisted of respondents assigned to the low-income and low-education groups. Control variables The control variables included in the multivariable regression models were: age, sex, Assessment of Activities of Daily Living, Self-care and Independence (ADL) score [ 51 ], and quality of life. For the ADL measure, participants self-rated the assistance they required for 16 skills essential for the management of basic needs. The total score was the sum of all activities for which no help was needed, with lower scores reflecting greater need for help with self-care. The quality of life was measured as self-reported satisfaction with life on a 1–10 scale. The control variables were selected based on expert opinion and significant associations with the intention to use mHealth applications in a univariable regression analysis. Multicollinearity was examined using a correlation matrix. Items with values representing correlation with other items of > 0.5 or <–0.5 were removed. In the regression analyses, responses with missing values for any TAM or control variable were deleted listwise. Statistical analyses Frequency distributions and descriptive statistics were computed to analyze the population characteristics. Means and standard deviations (SDs) were calculated for continuous variables, and percentages were calculated for categorical variables. To identify important technology acceptance factors for mHealth use in the two SES groups, we examined between-group differences in average factors scores using independent-sample t tests. Additionally, multivariable logistic regression analyses in which the TAM factors and selected control variables served as independent variables and the intention to use served as the dependent variable were conducted separately for the low- and non–low-SES groups. For each factor, the odds radio (OR), β coefficient, P value, and standard error (SE) are reported. To determine whether associations between TAM factors and the intention to use mHealth applications differed between SES groups, an additional regression model that included an interaction term was generated for each TAM factor. The inclusion of the interaction term (a function of the factor and SES) allowed the slopes of the independent variables to change as a function of SES. β coefficients, P values, and SEs for the interaction terms are reported. All statistical analyses were performed using SPSS (version 25; IBM Corporation). Results Population characteristics The population characteristics are shown in Table 1 . The population consisted of 360 respondents with an average age of 74.9 (SD = 7.0) years. More than half (56.9%) of the respondents were female. One hundred six (29.4%) respondents had low SES. Whereas 85.2% of the population had prior internet experience, only 16.2% had prior experience with medical applications. About half (50.3%) of the participants indicated that they had the intention to use medical applications. Table 1 Population characteristics Characteristics Total population (N = 360) Age in years, mean (SD) 74.9 (7.0) Socioeconomic status (low), no. (%) 106 (29.4) Gender (female), no. (%) 205 (56.9) Marital status, no. (%) Married 189 (52.9) Divorced 51 (14.3) Widowed 87 (24.4) Never married 22 (6.2) Sustainable cohabitation 8 (2.2) Living arrangement, no. (%) Living independently, alone 126 (35.5) Living independently, with other 160 (45.1) Senior living facility, alone 34 (9.6) Senior living facility, with other 35 (9.9) Health literacy, no. (%) Adequate 293 (85.7) ADL score, mean (SD) 14.7 (2.3) Quality of life (0–10), mean (SD) 7.6 (1.2) Prior experience with internet, no. (%) 306 (85.2) Prior experience with medical apps, no. (%) 58 (16.2) Intention to use mobile medical apps, no. (%) 181 (50.3) SD, standard deviation. Mean factor scores Mean TAM factor scores are presented in Table 2 . Relative to older adults with non-low SES, those with low SES had less-positive attitudes toward the use of mHealth applications ( P 0.030), less perceived ease of use ( P 0.007) and control over the applications ( P 0.015), and less perceived ability to use them to accomplish particular jobs or tasks (self-perceived effectiveness) ( P < 0.001). They also experienced less application availability and accessibility ( P 0.024) and fewer circumstances facilitating mHealth use ( P 0.049), and had a lesser tendency to innovate ( P 0.001) and more anxiety toward mHealth technology ( P < 0.001). No significant difference was found in the extent to which the respondents perceived mHealth applications to be useful, their satisfaction with support from friends and family, or the subjective norms imposed by their social relationships regarding mHealth application use. Table 2 Average TAM factor scores by SES group Low SES, mean (SD) Not-low SES, mean (SD) P-value Perceived usefulness 3.05 (1.02) 3.25 (0.98) 0.112 Perceived ease of use 2.83 (1.02) 3.15 (0.91) 0.007 Attitude towards use 3.06 (0.96) 3.32 (0.96) 0.030 Subjective norm 2.55 (1.06) 2.38 (0.99) 0.174 Sense of control 2.72 (1.08) 3.05 (1.09) 0.015 Feelings of anxiety (digital health anxiety) 3.29 (0.99) 2.49 (0.98) < 0.001 Personal innovativeness 2.35 (1.04) 2.76 (1.02) 0.001 Social relationships 3.98 (0.62) 4.07 (0.65) 0.242 Self-perceived effectiveness 2.93 (1.04) 3.39 (0.92) < 0.001 Service availability 2.77 (1.02) 3.05 (0.91) 0.024 Facilitating circumstances 2.78 (0.82) 2.97 (0.65) 0.049 TAM, technology acceptance model; SES, socioeconomic status; SD, standard deviation. Regression analysis results Table 3 shows the results of the multivariable logistic regression analyses which was meant to create better understanding of mHealth adoption disparities across socioeconomic groups. As mentioned before, TAM was used to explore factors influencing mHealth use among low- and non-low-SES groups. The intention to use mHealth applications of older adults with low SES was associated significantly with perceived usefulness (OR = 2.91), perceived ease of use (OR = 2.24), attitude toward use (OR = 7.69), subjective norms (OR = 1.93), sense of control (OR = 3.08), personal innovativeness (OR = 1.69), self-perceived effectiveness (OR = 2.12), service availability (OR = 2.23), and facilitating circumstances (OR = 4.01), but not with digital health anxiety or social relationships. In the non–low-SES group, all factors were associated significantly with the intention to use mHealth applications. The models including interaction terms showed that the intention to use mHealth applications of older adults with non-low-SES was associated more strongly than the intention of the low-SES group with the applications’ perceived usefulness (OR = 2.39), perceived ease of use (OR = 2.47), and obtainability and accessibility (service availability; OR = 2.14). The association of digital health anxiety with the intention to use also differed between groups (OR = 0.50, P = 0.017); it was significant in the non–low-SES group (OR = 0.45, P < 0.001), but not in the low-SES group, possibly because the mean score in the low-SES group was very high. Table 3 Associations of acceptance factors with the intention to use mHealth applications according to SES Low SES (N = 106) Not-low SES (N = 254) Interaction between SES and TAM factors OR (95%CI) β P-value SE OR (95%CI) β P-value SE OR (95%CI) β P-value SE Perceived usefulness 2.91 (1.52–5.59) 1.07 0.001 0.333 6.87 (3.94–11.97) 1.93 < 0.001 0.284 2.39 (1.03–5.53) 0.87 0.042 0.428 Perceived ease of use 2.24 (1.26–3.96) 0.81 0.006 0.292 5.55 (3.22–9.56) 1.71 < 0.001 0.277 2.47 (1.14–5.35) 0.91 0.021 0.394 Attitude towards use 7.69 (2.85–20.74) 2.04 < 0.001 0.506 11.59 (5.83–23.03) 2.45 < 0.001 0.351 1.64 (0.54–5.02) 0.5 0.385 0.57 Subjective norm 1.93 (1.14–3.27) 0.66 0.014 0.268 1.41 (1.06–1.89) 0.35 0.020 0.148 0.80 (0.45–1.40) -0.23 0.429 0.288 Sense of control 3.08 (1.68–5.67) 1.13 < 0.001 0.311 3.41 (2.34–5.00) 1.23 < 0.001 0.192 1.11 (0.56–2.22) 0.1 0.771 0.353 Feelings of anxiety (digital health anxiety) 0.97 (0.59–1.60) -0.03 0.901 0.255 0.45 (0.32–0.62) -0.81 < 0.001 0.172 0.50 (0.28–0.88) -0.7 0.017 0.293 Personal innovativeness 1.69 (1.04–2.74) 0.52 0.033 0.246 2.34 (1.66–3.30) 0.85 < 0.001 0.175 1.33 (0.75–2.35) 0.28 0.333 0.291 Social relationships 1.39 (0.63–3.06) 0.33 0.421 0.405 1.73 (1.06–2.85) 0.55 0.030 0.253 1.27 (0.52–3.11) 0.24 0.596 0.456 Self-perceived effectiveness 2.12 (1.18–3.80) 0.75 0.012 0.298 3.06 (2.01–4.67) 1.12 < 0.001 0.216 1.45 (0.74–2.86) 0.37 0.281 0.346 Service availability 2.23 (1.22–4.05) 0.8 0.009 0.306 4.55 (2.74–7.57) 1.52 < 0.001 0.26 2.14 (1.01–4.53) 0.76 0.047 0.383 Facilitating circumstances 4.01 (1.78–9.05) 1.39 0.001 0.415 4.02 (2.25–7.17) 1.39 < 0.001 0.295 1.12 (0.44–2.86) 0.11 0.82 0.479 SES, socioeconomic status; OR, odds ratio; CI, confidence interval; SE, standard error. Control variables were age, sex, Assessment of Activities of Daily Living, Self-care and Independence (ADL) score, and quality of life. Discussion Principal results The findings of this study provide novel insights into important factors influencing mHealth adoption among older adults with low and non-low SES, and differences in adoption factors between these groups. In the low-SES group, most acceptance factors had low perceived importance. This group reported significant anxiety toward mHealth use, a lack of a sense of control over mHealth applications, a lack of perceived innovativeness, perceived limited accessibility of mHealth applications, and few conditions facilitating their use. These results indicate that low SES makes it more challenging to motivate older adults to use mHealth applications, as found previously in the general population [ 14 , 34 – 36 ]. We identified eight factors associated significantly with the intention to use mHealth applications of older adults with low SES: perceived application usefulness and ease of use, the attitude toward application use, the perception that important others favor one’s application use, perceived control of application use, perceived innovativeness, and accessibility and facilitation of application use. These findings suggest that the adapted TAM is applicable to low-SES groups, offering policymakers valuable insights into factors that could be leveraged to promote mHealth adoption. Policies targeting these factors are likely to enhance the use of mHealth applications among older adults with low SES - for example, by fostering positive perceptions of performance expectancy through support during the setup phase and initial use [ 52 ]. Additionally, improving the user-friendliness of mHealth applications—such as incorporating intuitive navigation, pre-typed responses, auto-fill features based on user preferences, and text-to-speech functionality—can further facilitate adoption among this population. The same factors, as well as digital health anxiety and social relationships, were also associated significantly with the intention of older adults with non-low SES to use mHealth applications. Thus, factors that will increase mHealth adoption among individuals with low SES will also do so among those with non-low SES, and possibly to a greater extent. Accordingly, greater effort may need to be directed toward individuals with low SES than toward those with non-low SES to increase mHealth adoption at similar rates. Policies designed with a sole focus on adapted TAM factors, with no consideration of SES differences, may not effectively reduce health disparities. To address this issue, we recommend that policymakers adopt comprehensive approaches to factors influencing the use intentions of older adults with low SES, and exert more effort to reach this group specifically. Policies could be tailored to this group’s needs, and/or initiatives could be implemented primarily in areas with larger populations of older adults with low SES. Our models that included interaction terms indicated that perceived usefulness, perceived ease of use, digital health anxiety, and service availability influenced the use intentions of older adults with low SES significantly less than they did those of older adults with non-low SES. Bao and Lee [ 35 ] reported contrasting results for perceived usefulness and ease of use: they found a stronger association among individuals with low SES than among those with high SES for the former, and no difference in association strength according to SES for the latter. A possible explanation for this contradiction is that Bao and Lee [ 35 ] studied the general population, whereas we studied the older adult population. Thus, factors that emerge as relevant for low-SES groups in the general population may be less relevant for older adults with low SES; in other words, different factors may motivate the latter to use mHealth applications. Our findings suggest that for older individuals with low SES, broader structural or contextual barriers—such as limited digital access or finance, privacy and trust [ 48 ] or lower digital literacy—may overshadow the role of individual perceptions. Given the multitude of factors influencing intention to use, policies are likely to be most effective when adopting a multifactorial approach rather than targeting a single determinant. Consequently, we recommend that policymakers aiming to reduce health disparities among older adults with different SES through the use of mHealth base their policy development on findings from research conducted with older adults. This study showed that older adults with low SES are very anxious about mHealth use. However, digital health anxiety was not associated with the use intention in this group, contrary to previous findings that anxiety is a relevant predictor of mHealth use in the general population [ 14 , 32 ] and among older adults [ 33 ]. One possible explanation for our finding is that the prevalence of digital health anxiety was uniformly high among older adults with low SES, minimizing within-group differences. The reduction of this type of anxiety in this population could positively impact their intention to use mHealth, as suggested in previous research [ 14 , 32 , 33 , 53 ]. The average service availability score was low in the low-SES group in this study. In line with this result, individuals with low SES have been found to use health technology less promptly than those with higher SES due to limited technology availability [ 14 ]. However, our findings indicate that the odds of intending to use mHealth in older adults with low SES more than doubles when they perceive mHealth as readily available. Thus, the improvement of availability likely increases usage, and it has been suggested as a viable strategy to improve mHealth adoption in the general population in recent Dutch policy [ 54 ]. In addition to service availability, social network support and other facilitating conditions have been identified as strong facilitators of mHealth use in the general population [ 34 , 35 , 55 ] and among older adults [ 56 ]. In this study, perceived facilitating circumstances and subjective norms were associated positively with the use intention of older adults with low SES, but their average scores in this group were not high. These results suggest that older adults with low SES do not, on average, feel strongly motivated to use mHealth applications because of the opinions of others or objective environmental factors that can make technology use easy. However, those who perceive themselves as being facilitated and supported well are much more likely to adopt mHealth applications. Thus, opportunities exist to engage the social networks of older adults with low SES as facilitating entities, although such efforts could be hampered by difficulties such as the lack of the digital competency needed to provide effective support [ 34 ] or the lack of robust social networks altogether [ 57 , 58 ]. Studies of mHealth acceptance among older adults have led to suggestions to involve other parties, such as healthcare institutions, in support provision [ 19 , 20 , 53 , 56 , 59 – 61 ]. Such efforts may specifically benefit older adults with low SES, and can be used to enhance policies in this context. Finally, future studies should investigate whether mHealth is independently associated with improved management of chronic diseases, accounting for factors such as socioeconomic status (SES). Strengths and limitations This study has several limitations. First, it had a cross-sectional design, preventing the examination of causality [ 62 ] and entailing susceptibility to self-report bias [ 63 ]. Longitudinal studies are essential to explore causal links between SES and acceptance factors. Second, the measurement and application of the SES construct varies across studies of digital healthcare applications. In their scoping review of innovative technologies and social inequalities in health, Weiss et al. [ 6 ] identified three primary approaches to SES calculation, including that based on income and education levels used in this study. Weiss et al. [ 6 ] stated that the outcomes of research on socioeconomic disparities in health innovations may depend on the approach used to measure and define social groups, and thus that clear and thorough reporting of cutoff values and SES definition is crucial. In this study, we converted SES to a binary variable, defining groups using cutoff values for income and educational levels based on a recent report defining joint net monthly incomes < 1750 euros in the Netherlands as low [ 49 ] and on the educational levels defined by Statistics Netherlands, respectively. Given our focus on older adults, we included only respondents with both low incomes and low educational levels in the low-SES group. This approach allowed us to gain insight into the most disadvantaged group relative to all other older adults. The third study limitation was that the population comprised individuals aged ≥ 65 years. Adults, and particularly women, in this age category may have low educational levels due to the lack of opportunities for education, but high household incomes. The disparity in older men’s and women’s educational levels may decrease in the future [ 64 ], potentially impacting the relevance of our study's outcomes over time. Finally, a limitation of this study is the lack of differentiation between the various purposes of mHealth applications, as users’ intention to adopt may differ depending on the specific type or functionality of the app. Conclusions This study showed that older adults in the Netherlands with low SES are less motivated than those with non-low SES to use mHealth applications. A specific approach is needed to stimulate mHealth adoption in the low-SES group. The adapted TAM provides viable factors to increase the intention to use mHealth applications of older adults with low and non-low SES. However, more effort needs to be focused on those with low SES to increase mHealth adoption at similar rates in both groups. To reduce health disparities, we recommend that policymakers focus on factors that significantly influence the use intention of older adults with low SES and tailor their policies specifically to the needs of this group. Abbreviations SES Socioeconomic Status mHealth Mobile Health TAM Technology Acceptance Model ADL Activities of Daily Living, Self-care and Independence SD Standard Deviation OR Odds Radio SE Standard Error Declarations Ethics approval and consent to participate All respondents provided written informed consent, and the study was approved by the Medical Ethics Commission of the Erasmus University Medical Center (MEC-2018-120). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request Competing interests No competing interests declared. Funding No funding was received for this study. Authors’ contributions MA designed the research project and developed the questionnaire. MA and FvE collected the data with the assistance of data assistants. FvE conducted the analysis under the supervision of MA and APN. FvE wrote the initial version of the manuscript. Interpreting the results and revising the paper critically was done by all the authors. We thank the experts, older adults, participants, and data assistants who helped validate our questionnaire. Acknowledgements Not applicable. References He W, Goodkind D, Kowal P, U.S. Government Publishing Office. An aging world: 2015. International Population Reports . Washington, DC: ; 2016. Available from: https://www.census.gov/content/dam/Census/library/publications/2016/demo/p95-16-1.pdf . Accessed 2025 Jun. Rijksinstituut voor Volksgezondheid en Milieu. Chronische aandoeningen en multimorbiditeit: leeftijd en geslacht [Internet]. 2024 [cited 2024 January 16]. 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Centraal Bureau voor de Statistiek. Opleidingsniveau’s in Nederland [Internet]. 2019 [cited 2024 January 19]. Available from: https://www.cbs.nl/nl-nl/nieuws/2019/33/verschil-levensverwachting-hoog-en-laagopgeleid-groeit/opleidingsniveau#:~:text=Laag%3A Dit omvat onderwijs op,specialistenopleidingen (mbo-4). Mlinac ME, Feng MC. Assessment of activities of daily living, self-care, and independence. Arch Clin Neuropsychol. 2016;31(6):506–16. Brady J, McCloud RF, Higgins E, Mahesh A, LeJeune K, Black J, Singh A. The evolution of barriers and facilitators to using a COPD app among older adults: Results from a pilot study. Front Digit Health. 2025;7:1557590. 10.3389/fdgth.2025.1557590 . Faber JS, Al-Dhahir I, Reijnders T, Chavannes NH, Evers AWM, Kraal JJ, et al. Attitudes toward health, healthcare, and eHealth of people with a low socioeconomic status: a community-based participatory approach. Front Digit Health. 2021;3(July):1–15. Rijksinstituut voor Volksgezondheid en Milieu. E-healthmonitor 2022: stand van zaken digitale zorg. Bilthoven; 2022. Dym B, Fiesler C. Social norm vulnerability and its consequences for privacy and safety in an online community. Proceedings of the ACM on Human-Computer Interaction. 2020;4(CSCW2). Airola E. Learning and use of eHealth among older adults living at home in rural and nonrural settings: systematic review. JMIR. 2021;23(12):e23804. Boekhout JM, Peels DA, Berendsen BAJ, Bolman CAW, Lechner L. An eHealth intervention to promote physical activity and social network of single, chronically impaired older adults: adaptation of an existing intervention using intervention mapping. JMIR Res Protoc. 2017;6(11):1–16. Lakerveld J, Verstrate L, Bot SD, Kroon A, Baan CA, Brug J, et al. Environmental interventions in low-SES neighbourhoods to promote healthy behaviour: enhancing and impeding factors. Eu J Public Health. 2014;24(3):390–5. Eggink E, Hafdi M, Hoevenaar-Blom MP, Richard E, Van Moll EP. Attitudes and views on healthy lifestyle interventions for the prevention of dementia and cardiovascular disease among older people with low socioeconomic status: a qualitative study in the Netherlands. BMJ Open. 2022;12(2):e055984. Al-Dhahir I, Reijnders T, Faber JS, van den Berg-Emons RJ, Janssen VR, Kraaijenhagen RA, et al. The barriers and facilitators of eHealth-based lifestyle intervention programs for people with a low socioeconomic status: scoping review. JMIR. 2022;24(8):e34229. Freeman T, Fisher M, Foley K, Boyd MA, Ward PR, McMichael G, et al. Barriers to digital health services among people living in areas of socioeconomic disadvantage: research from hospital diabetes and antenatal clinics. Health Promot J Austr. 2021;33(3):751–7. Reichenheim ME, Coutinho ES. Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression. BMC Med Res Methodol. 2010;10:66. Rosenman R, Tennekoon V, Hill LG. Measuring bias in self-reported data. Int J Behav Healthc Res. 2011;2(4):320–32. Centraal Bureau voor de Statistiek. Al 23 jaar op rij meer vrouwen dan mannen in hoger onderwijs [Internet]. 2023 [cited 2024 January 27]. Available from: https://www.cbs.nl/nl-nl/nieuws/2023/10/al-23-jaar-op-rij-meer-vrouwen-dan-mannen-in-hoger-onderwijs Additional Declarations No competing interests reported. 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projected to triple, rising from 562\u0026nbsp;million in 2012 to 1.6\u0026nbsp;billion by 2050, and will represent 16.7% of the world\u0026rsquo;s population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With populational aging in the Netherlands, the prevalence of people with one or more chronic diseases is increasing; 85% of Dutch older adults have at least one chronic condition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Socioeconomic status (SES), commonly defined in health-related research as a combination of income and education level, is associated with chronic disease prevalence and mortality [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The risk of chronic disease development is twice as high for Dutch older adults with lower SES than for those with higher SES [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, Dutch adults with low SES have less knowledge about their conditions, and have more difficulty managing them and following therapeutic regimens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To reduce disparities in health among socioeconomic groups, the ability of those who are at a disadvantage to use (digital) health interventions is crucial [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMobile health (mHealth) has been proven to contribute to the treatment and management of chronic diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The World Health Organization has defined mHealth as \u0026ldquo;the use of mobile wireless technologies for public health,\u0026rdquo; and stated that \u0026ldquo;mHealth has been shown to improve the quality and coverage of care, increase access to health information, services and skills, as well as promote positive changes in health behaviours to prevent the onset of acute and chronic diseases\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, p. 2]. mHealth applications have also been promised to overcome socioeconomic barriers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and their implementation is believed to improve healthcare quality and accessibility for groups with low SES [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For this reason, the implementation of digital applications has been an important pillar of the development of policies related to the treatment of chronic diseases in the Netherlands [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Concordantly, the Dutch government invests in digital health applications to stimulate remote care [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the numbers of digital innovations and services introduced in healthcare are increasing rapidly [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the versatility of mHealth applications and the governmental stimulation of their implementation, the rate of mHealth use among older adults remains low [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. People with low SES are also less likely to use mHealth, although this disparity has not been studied in the older adult population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This research gap is problematic, as older adults with low SES are most vulnerable and are often most in need of effective interventions to manage their chronic diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To develop effective interventions for people with low SES, a better understanding of disparities in mHealth adoption among older adults from different socioeconomic classes is needed.\u003c/p\u003e\u003cp\u003eTo investigate how to enable the use of mHealth among older adults with low SES, we applied the extended technology acceptance model (TAM) in this study. The TAM, introduced in 1989 by Davis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], states that technology adoption can be predicted solely by one\u0026rsquo;s intention to use that specific technology, which in turn is influenced by the technology\u0026rsquo;s perceived usefulness and ease of use. It has been modified to better fit the context of health technology adoption, with the addition of other factors that influence the intention to use [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The TAM has been used widely to study mHealth adoption in the general population [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and among older adults [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, evidence for the intention to use mHealth in low-SES populations is limited. In the general population, higher perceived usefulness has been found to be an important factor for health application use; when present, low- and high-SES groups are equally likely to use health technologies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, the intention to use mHealth among people with low SES was found to be influenced more by social networks than it is that among people with high SES [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, people with lower socioeconomic backgrounds have been found to feel more anxiety about the use of digital health technology, and to be more reluctant to do so [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough these studies provide evidence on important factors for technology acceptance among different socioeconomic groups in the general population, no research has explored socioeconomic differences in the relationships between technology acceptance factors and the intention to use mHealth specifically among older adults. Thus, this study was performed to investigate differences between socioeconomic groups in factors influencing the intention to use mHealth applications in a large sample of older adults in the Netherlands. The aims were to identify the most important factors for mHealth use among older adults with low and non-low SES, and differences between these groups in the associations of technology acceptance factors with the intention to use mHealth.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStudy design and data collection\u003c/span\u003e\u003c/p\u003e\u003cp\u003eData were collected through the distribution of a questionnaire among Dutch older adults between January 1st and June 1st 2020. The study cohort consisted of participants aged 65 and older, free of cognitive impairments and living either independently or in senior living facilities. Participants were recruited through multiple channels, including elderly living facilities, healthcare organizations (a hospital, general practitioners, and a home care organization), leisure activity clubs for older adults, and social media platforms in different regions in the Netherlands. The data have mainly been collected in the regions of Noord-Brabant, Utrecht, and Zuid-Holland. To enhance accessibility and encourage participation, the questionnaire was made available in both paper and digital formats\u0026rdquo;. The questionnaire was validated by a geriatric nurse, a diversity expert, and two eHealth experts and is available upon request. In addition, we collected feedback from four older adults to improve the questionnaire\u0026rsquo;s readability. The reporting of the online questionnaire follows the Checklist for Reporting Results of Internet E-Surveys (Multimedia Appendix 1).\u003c/p\u003e\u003cp\u003eThe participants were informed of the purpose of the study, the names of the main researchers, sources of assistance, how to fill in the questionnaire, what mHealth applications are (with multiple examples), how their data would be managed, and how their privacy would be safeguarded. Data assistants provided any assistance or additional information that participants needed by telephone or email, or in person. The online questionnaires were filled in anonymously. All respondents provided written informed consent, and the study was approved by the Medical Ethics Commission of the Erasmus University Medical Center (MEC-2018-120).\u003c/p\u003e\u003cp\u003eThe data were entered into an SPSS database (IBM Corporation, Armonk, NY, USA). Their representativeness of the general older adult population in the Netherlands was checked by comparison with data from Statistics Netherlands. To increase external validity, the data assistants collected data in different regions of the Netherlands.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMeasurement of variables\u003c/span\u003e\u003c/p\u003e\u003cp\u003eDemographic data, including age and gender, were collected, along with information on health literacy, prior internet use, and prior experience with medical apps. The latter three variables were measured as binary (yes/no) responses.\u003c/p\u003e\u003cp\u003eIndependent variables\u003c/p\u003e\u003cp\u003eThe examination of differences between SES groups in the intention to use mHealth applications was based on factors from an adapted version of the TAM, used previously to study the intention to use mHealth in older adult populations [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. We included the following factors as independent variables: perceived ease of use (four statements, e.g., \u0026ldquo;It is easy to use medical applications for remote care\u0026rdquo;), perceived usefulness (three statements, e.g., \u0026ldquo;I find it useful to use medical applications for remote care\u0026rdquo;), attitude toward use (four statements, e.g., \u0026ldquo;Using medical applications for remote care would be a good idea\u0026rdquo;), and subjective norms (three statements, e.g., \u0026ldquo;People who are important to me think I should use medical applications for remote care\u0026rdquo;). We also adopted the following factors that are specific to older adults: sense of control (two statements, e.g., \u0026ldquo;I have resources, knowledge and ability to use medical applications for remote care\u0026rdquo;), anxiety toward use (two statements, e.g., \u0026ldquo;I hesitate to use medical applications for remote care for fear of making mistakes that I cannot correct\u0026rdquo;), personal innovativeness (four statements, e.g., \u0026ldquo;I like to experiment with new information technology\u0026rdquo;), social relationships (three statements, e.g., \u0026ldquo;I am satisfied with the support of my family and friends\u0026rdquo;), perceived effectiveness of use (two statements, e.g., \u0026ldquo;I could perform a task on a medical application if I only had the manual\u0026rdquo;), service availability (three statements, e.g., \u0026ldquo;I have the ability to use medical applications for remote care anytime, anywhere\u0026rdquo;), and facilitating circumstances (five statements, e.g., \u0026ldquo;I have the knowledge needed to use medical applications\u0026rdquo;). Full descriptions of the TAM factors can be found in Appendix 2. Responses were provided on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely disagree, 2\u0026thinsp;=\u0026thinsp;disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 4\u0026thinsp;=\u0026thinsp;agree, 5\u0026thinsp;=\u0026thinsp;completely agree).\u003c/p\u003e\u003cp\u003eFor each TAM factor, an average score was calculated and used in the analysis.\u003c/p\u003e\u003cp\u003eDependent variables\u003c/p\u003e\u003cp\u003eGiven that the intention to use can be seen as the primary determinant of technology use [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], this intention served as the outcome variable in this study. It was measured using three statements (e.g., \u0026ldquo;Assuming I have access to medical applications for remote care, I plan to use them\u0026rdquo;) to which responses were provided on a 5-point Likert scale. The mean score was calculated, and for ease of interpretation the outcome variable was dichotomized, with an average score of 3 or higher coded as 1 (indicating intention to use).\u003c/p\u003e\u003cp\u003eThe SES factor was composed of respondents\u0026rsquo; income and education level. Eight levels of educational attainment were distinguished, ranging from no education to university education. Income was measured as the net monthly household income on a 5-point ordinal scale ranging from \u0026ldquo;less than 1000\u0026rdquo; to \u0026ldquo;more than 3500\u0026rdquo; euros. Education and income were transformed into binary variables. The threshold for low income was set at 1750 euros based on the national standard [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Of the three educational levels defined by Statistics Netherlands, the middle and high levels were combined, resulting in the categories of lower (up to senior general secondary) education and higher (senior general secondary and/or higher) education [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The low-SES group consisted of respondents assigned to the low-income and low-education groups.\u003c/p\u003e\u003cp\u003eControl variables\u003c/p\u003e\u003cp\u003eThe control variables included in the multivariable regression models were: age, sex, Assessment of Activities of Daily Living, Self-care and Independence (ADL) score [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and quality of life. For the ADL measure, participants self-rated the assistance they required for 16 skills essential for the management of basic needs. The total score was the sum of all activities for which no help was needed, with lower scores reflecting greater need for help with self-care. The quality of life was measured as self-reported satisfaction with life on a 1\u0026ndash;10 scale.\u003c/p\u003e\u003cp\u003eThe control variables were selected based on expert opinion and significant associations with the intention to use mHealth applications in a univariable regression analysis. Multicollinearity was examined using a correlation matrix. Items with values representing correlation with other items of \u0026gt;\u0026thinsp;0.5 or \u0026lt;\u0026ndash;0.5 were removed. In the regression analyses, responses with missing values for any TAM or control variable were deleted listwise.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStatistical analyses\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFrequency distributions and descriptive statistics were computed to analyze the population characteristics. Means and standard deviations (SDs) were calculated for continuous variables, and percentages were calculated for categorical variables. To identify important technology acceptance factors for mHealth use in the two SES groups, we examined between-group differences in average factors scores using independent-sample \u003cem\u003et\u003c/em\u003e tests. Additionally, multivariable logistic regression analyses in which the TAM factors and selected control variables served as independent variables and the intention to use served as the dependent variable were conducted separately for the low- and non\u0026ndash;low-SES groups. For each factor, the odds radio (OR), β coefficient, \u003cem\u003eP\u003c/em\u003e value, and standard error (SE) are reported.\u003c/p\u003e\u003cp\u003eTo determine whether associations between TAM factors and the intention to use mHealth applications differed between SES groups, an additional regression model that included an interaction term was generated for each TAM factor. The inclusion of the interaction term (a function of the factor and SES) allowed the slopes of the independent variables to change as a function of SES. β coefficients, \u003cem\u003eP\u003c/em\u003e values, and SEs for the interaction terms are reported. All statistical analyses were performed using SPSS (version 25; IBM Corporation).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePopulation characteristics\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe population characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The population consisted of 360 respondents with an average age of 74.9 (SD\u0026thinsp;=\u0026thinsp;7.0) years. More than half (56.9%) of the respondents were female. One hundred six (29.4%) respondents had low SES. Whereas 85.2% of the population had prior internet experience, only 16.2% had prior experience with medical applications. About half (50.3%) of the participants indicated that they had the intention to use medical applications.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePopulation characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal population (N\u0026thinsp;=\u0026thinsp;360)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge in years, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.9 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocioeconomic status (low), no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106 (29.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female), no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205 (56.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e189 (52.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (14.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87 (24.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (6.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSustainable cohabitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving arrangement, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving independently, alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126 (35.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving independently, with other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160 (45.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior living facility, alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior living facility, with other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35 (9.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth literacy, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdequate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e293 (85.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADL score, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.7 (2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuality of life (0\u0026ndash;10), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.6 (1.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior experience with internet, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e306 (85.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior experience with medical apps, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58 (16.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntention to use mobile medical apps, no. (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e181 (50.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSD, standard deviation.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMean factor scores\u003c/span\u003e\u003c/p\u003e\u003cp\u003eMean TAM factor scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Relative to older adults with non-low SES, those with low SES had less-positive attitudes toward the use of mHealth applications (\u003cem\u003eP\u003c/em\u003e 0.030), less perceived ease of use (\u003cem\u003eP\u003c/em\u003e 0.007) and control over the applications (\u003cem\u003eP\u003c/em\u003e 0.015), and less perceived ability to use them to accomplish particular jobs or tasks (self-perceived effectiveness) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). They also experienced less application availability and accessibility (\u003cem\u003eP\u003c/em\u003e 0.024) and fewer circumstances facilitating mHealth use (\u003cem\u003eP\u003c/em\u003e 0.049), and had a lesser tendency to innovate (\u003cem\u003eP\u003c/em\u003e 0.001) and more anxiety toward mHealth technology (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found in the extent to which the respondents perceived mHealth applications to be useful, their satisfaction with support from friends and family, or the subjective norms imposed by their social relationships regarding mHealth application use.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage TAM factor scores by SES group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow SES,\u003c/p\u003e\u003cp\u003emean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot-low SES,\u003c/p\u003e\u003cp\u003emean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerceived usefulness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.05 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.25 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerceived ease of use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.83 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.15 (0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttitude towards use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.06 (0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.32 (0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubjective norm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.55 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.38 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSense of control\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.72 (1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.05 (1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFeelings of anxiety (digital health anxiety)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.29 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.49 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePersonal innovativeness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.35 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.76 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial relationships\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.98 (0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.07 (0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-perceived effectiveness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.93 (1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.39 (0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eService availability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.77 (1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.05 (0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacilitating circumstances\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.78 (0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.97 (0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTAM, technology acceptance model; SES, socioeconomic status; SD, standard deviation.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eRegression analysis results\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of the multivariable logistic regression analyses which was meant to create better understanding of mHealth adoption disparities across socioeconomic groups. As mentioned before, TAM was used to explore factors influencing mHealth use among low- and non-low-SES groups. The intention to use mHealth applications of older adults with low SES was associated significantly with perceived usefulness (OR\u0026thinsp;=\u0026thinsp;2.91), perceived ease of use (OR\u0026thinsp;=\u0026thinsp;2.24), attitude toward use (OR\u0026thinsp;=\u0026thinsp;7.69), subjective norms (OR\u0026thinsp;=\u0026thinsp;1.93), sense of control (OR\u0026thinsp;=\u0026thinsp;3.08), personal innovativeness (OR\u0026thinsp;=\u0026thinsp;1.69), self-perceived effectiveness (OR\u0026thinsp;=\u0026thinsp;2.12), service availability (OR\u0026thinsp;=\u0026thinsp;2.23), and facilitating circumstances (OR\u0026thinsp;=\u0026thinsp;4.01), but not with digital health anxiety or social relationships. In the non\u0026ndash;low-SES group, all factors were associated significantly with the intention to use mHealth applications.\u003c/p\u003e\u003cp\u003eThe models including interaction terms showed that the intention to use mHealth applications of older adults with non-low-SES was associated more strongly than the intention of the low-SES group with the applications\u0026rsquo; perceived usefulness (OR\u0026thinsp;=\u0026thinsp;2.39), perceived ease of use (OR\u0026thinsp;=\u0026thinsp;2.47), and obtainability and accessibility (service availability; OR\u0026thinsp;=\u0026thinsp;2.14). The association of digital health anxiety with the intention to use also differed between groups (OR\u0026thinsp;=\u0026thinsp;0.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017); it was significant in the non\u0026ndash;low-SES group (OR\u0026thinsp;=\u0026thinsp;0.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not in the low-SES group, possibly because the mean score in the low-SES group was very high.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of acceptance factors with the intention to use mHealth applications according to SES\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eLow SES (N\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eNot-low SES (N\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eInteraction between SES and TAM factors\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eOR (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eOR (95%CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerceived usefulness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.91 (1.52\u0026ndash;5.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.87 (3.94\u0026ndash;11.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.39 (1.03\u0026ndash;5.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePerceived ease of use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24 (1.26\u0026ndash;3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.55 (3.22\u0026ndash;9.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.47 (1.14\u0026ndash;5.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAttitude towards use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.69 (2.85\u0026ndash;20.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.59 (5.83\u0026ndash;23.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.64 (0.54\u0026ndash;5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSubjective norm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.93 (1.14\u0026ndash;3.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.41 (1.06\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.80 (0.45\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.288\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSense of control\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.08 (1.68\u0026ndash;5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.41 (2.34\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.11 (0.56\u0026ndash;2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFeelings of anxiety (digital health anxiety)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97 (0.59\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.45 (0.32\u0026ndash;0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.50 (0.28\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e-0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePersonal innovativeness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69 (1.04\u0026ndash;2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.34 (1.66\u0026ndash;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.33 (0.75\u0026ndash;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial relationships\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39 (0.63\u0026ndash;3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.73 (1.06\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.27 (0.52\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-perceived effectiveness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.12 (1.18\u0026ndash;3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.06 (2.01\u0026ndash;4.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.45 (0.74\u0026ndash;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eService availability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23 (1.22\u0026ndash;4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.55 (2.74\u0026ndash;7.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.14 (1.01\u0026ndash;4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.383\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacilitating circumstances\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.01 (1.78\u0026ndash;9.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.02 (2.25\u0026ndash;7.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.12 (0.44\u0026ndash;2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSES, socioeconomic status; OR, odds ratio; CI, confidence interval; SE, standard error.\u003c/p\u003e\u003cp\u003eControl variables were age, sex, Assessment of Activities of Daily Living, Self-care and Independence (ADL) score, and quality of life.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ePrincipal results\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study provide novel insights into important factors influencing mHealth adoption among older adults with low and non-low SES, and differences in adoption factors between these groups. In the low-SES group, most acceptance factors had low perceived importance. This group reported significant anxiety toward mHealth use, a lack of a sense of control over mHealth applications, a lack of perceived innovativeness, perceived limited accessibility of mHealth applications, and few conditions facilitating their use. These results indicate that low SES makes it more challenging to motivate older adults to use mHealth applications, as found previously in the general population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe identified eight factors associated significantly with the intention to use mHealth applications of older adults with low SES: perceived application usefulness and ease of use, the attitude toward application use, the perception that important others favor one\u0026rsquo;s application use, perceived control of application use, perceived innovativeness, and accessibility and facilitation of application use. These findings suggest that the adapted TAM is applicable to low-SES groups, offering policymakers valuable insights into factors that could be leveraged to promote mHealth adoption. Policies targeting these factors are likely to enhance the use of mHealth applications among older adults with low SES - for example, by fostering positive perceptions of performance expectancy through support during the setup phase and initial use [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Additionally, improving the user-friendliness of mHealth applications\u0026mdash;such as incorporating intuitive navigation, pre-typed responses, auto-fill features based on user preferences, and text-to-speech functionality\u0026mdash;can further facilitate adoption among this population.\u003c/p\u003e\u003cp\u003eThe same factors, as well as digital health anxiety and social relationships, were also associated significantly with the intention of older adults with non-low SES to use mHealth applications. Thus, factors that will increase mHealth adoption among individuals with low SES will also do so among those with non-low SES, and possibly to a greater extent. Accordingly, greater effort may need to be directed toward individuals with low SES than toward those with non-low SES to increase mHealth adoption at similar rates. Policies designed with a sole focus on adapted TAM factors, with no consideration of SES differences, may not effectively reduce health disparities. To address this issue, we recommend that policymakers adopt comprehensive approaches to factors influencing the use intentions of older adults with low SES, and exert more effort to reach this group specifically. Policies could be tailored to this group\u0026rsquo;s needs, and/or initiatives could be implemented primarily in areas with larger populations of older adults with low SES.\u003c/p\u003e\u003cp\u003eOur models that included interaction terms indicated that perceived usefulness, perceived ease of use, digital health anxiety, and service availability influenced the use intentions of older adults with low SES significantly less than they did those of older adults with non-low SES. Bao and Lee [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported contrasting results for perceived usefulness and ease of use: they found a stronger association among individuals with low SES than among those with high SES for the former, and no difference in association strength according to SES for the latter. A possible explanation for this contradiction is that Bao and Lee [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] studied the general population, whereas we studied the older adult population. Thus, factors that emerge as relevant for low-SES groups in the general population may be less relevant for older adults with low SES; in other words, different factors may motivate the latter to use mHealth applications. Our findings suggest that for older individuals with low SES, broader structural or contextual barriers\u0026mdash;such as limited digital access or finance, privacy and trust [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] or lower digital literacy\u0026mdash;may overshadow the role of individual perceptions. Given the multitude of factors influencing intention to use, policies are likely to be most effective when adopting a multifactorial approach rather than targeting a single determinant. Consequently, we recommend that policymakers aiming to reduce health disparities among older adults with different SES through the use of mHealth base their policy development on findings from research conducted with older adults.\u003c/p\u003e\u003cp\u003eThis study showed that older adults with low SES are very anxious about mHealth use. However, digital health anxiety was not associated with the use intention in this group, contrary to previous findings that anxiety is a relevant predictor of mHealth use in the general population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and among older adults [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One possible explanation for our finding is that the prevalence of digital health anxiety was uniformly high among older adults with low SES, minimizing within-group differences. The reduction of this type of anxiety in this population could positively impact their intention to use mHealth, as suggested in previous research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe average service availability score was low in the low-SES group in this study. In line with this result, individuals with low SES have been found to use health technology less promptly than those with higher SES due to limited technology availability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, our findings indicate that the odds of intending to use mHealth in older adults with low SES more than doubles when they perceive mHealth as readily available. Thus, the improvement of availability likely increases usage, and it has been suggested as a viable strategy to improve mHealth adoption in the general population in recent Dutch policy [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to service availability, social network support and other facilitating conditions have been identified as strong facilitators of mHealth use in the general population [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and among older adults [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, perceived facilitating circumstances and subjective norms were associated positively with the use intention of older adults with low SES, but their average scores in this group were not high. These results suggest that older adults with low SES do not, on average, feel strongly motivated to use mHealth applications because of the opinions of others or objective environmental factors that can make technology use easy. However, those who perceive themselves as being facilitated and supported well are much more likely to adopt mHealth applications. Thus, opportunities exist to engage the social networks of older adults with low SES as facilitating entities, although such efforts could be hampered by difficulties such as the lack of the digital competency needed to provide effective support [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or the lack of robust social networks altogether [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Studies of mHealth acceptance among older adults have led to suggestions to involve other parties, such as healthcare institutions, in support provision [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Such efforts may specifically benefit older adults with low SES, and can be used to enhance policies in this context. Finally, future studies should investigate whether mHealth is independently associated with improved management of chronic diseases, accounting for factors such as socioeconomic status (SES).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStrengths and limitations\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, it had a cross-sectional design, preventing the examination of causality [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and entailing susceptibility to self-report bias [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Longitudinal studies are essential to explore causal links between SES and acceptance factors.\u003c/p\u003e\u003cp\u003eSecond, the measurement and application of the SES construct varies across studies of digital healthcare applications. In their scoping review of innovative technologies and social inequalities in health, Weiss et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] identified three primary approaches to SES calculation, including that based on income and education levels used in this study. Weiss et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] stated that the outcomes of research on socioeconomic disparities in health innovations may depend on the approach used to measure and define social groups, and thus that clear and thorough reporting of cutoff values and SES definition is crucial. In this study, we converted SES to a binary variable, defining groups using cutoff values for income and educational levels based on a recent report defining joint net monthly incomes\u0026thinsp;\u0026lt;\u0026thinsp;1750 euros in the Netherlands as low [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and on the educational levels defined by Statistics Netherlands, respectively. Given our focus on older adults, we included only respondents with both low incomes and low educational levels in the low-SES group. This approach allowed us to gain insight into the most disadvantaged group relative to all other older adults.\u003c/p\u003e\u003cp\u003eThe third study limitation was that the population comprised individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. Adults, and particularly women, in this age category may have low educational levels due to the lack of opportunities for education, but high household incomes. The disparity in older men\u0026rsquo;s and women\u0026rsquo;s educational levels may decrease in the future [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], potentially impacting the relevance of our study's outcomes over time. Finally, a limitation of this study is the lack of differentiation between the various purposes of mHealth applications, as users\u0026rsquo; intention to adopt may differ depending on the specific type or functionality of the app.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that older adults in the Netherlands with low SES are less motivated than those with non-low SES to use mHealth applications. A specific approach is needed to stimulate mHealth adoption in the low-SES group. The adapted TAM provides viable factors to increase the intention to use mHealth applications of older adults with low and non-low SES. However, more effort needs to be focused on those with low SES to increase mHealth adoption at similar rates in both groups. To reduce health disparities, we recommend that policymakers focus on factors that significantly influence the use intention of older adults with low SES and tailor their policies specifically to the needs of this group.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSocioeconomic Status\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emHealth\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMobile Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTechnology Acceptance Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eActivities of Daily Living, Self-care and Independence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Radio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eAll respondents provided written informed consent, and the study was approved by the Medical Ethics Commission of the Erasmus University Medical Center (MEC-2018-120).\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eNo competing interests declared.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003ch3\u003eAuthors’ contributions\u003c/h3\u003e\n\u003cp\u003eMA designed the research project and developed the questionnaire. MA and FvE collected the data with the assistance of data assistants. FvE conducted the analysis under the supervision of MA and APN. FvE wrote the initial version of the manuscript. Interpreting the results and revising the paper critically was done by all the authors. We thank the experts, older adults, participants, and data assistants who helped validate our questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHe W, Goodkind D, Kowal P, U.S. Government Publishing Office. \u003cem\u003eAn aging world: 2015. International Population Reports\u003c/em\u003e. Washington, DC: ; 2016. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbs.nl/nl-nl/nieuws/2023/10/al-23-jaar-op-rij-meer-vrouwen-dan-mannen-in-hoger-onderwijs\u003c/span\u003e\u003cspan address=\"https://www.cbs.nl/nl-nl/nieuws/2023/10/al-23-jaar-op-rij-meer-vrouwen-dan-mannen-in-hoger-onderwijs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"technology acceptance model, intention to use, older adult, socioeconomic status, mHealth","lastPublishedDoi":"10.21203/rs.3.rs-5029618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5029618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOlder adults with lower socioeconomic status (SES) are most affected by chronic diseases and in need of effective interventions to manage their conditions. Despite the proven benefits of mobile health (mHealth) in chronic disease management, the stimulation of mHealth adoption among people with lower SES is challenging. Such socioeconomic differences have not been investigated among older adults. The aims of this study were to identify factors associated with mHealth acceptance among older adults in the Netherlands with low and non-low SES, and to determine whether the influences of these factors differ between socioeconomic groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was performed using a questionnaire based on technology acceptance model (TAM) factors. The participants were aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, lived independently or in senior living facilities, and had no cognitive impairment. Associations between average TAM factor scores and respondents\u0026rsquo; intention to use mHealth applications were analyzed separately for low- and non\u0026ndash;low-SES groups using controlled multivariable logistic regression. Models including interaction terms were then computed to investigate differences between groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe sample comprised 360 respondents (mean age, 74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 years). Scores for the eight TAM factors were significantly lower in the low-SES group than in the non\u0026ndash;low-SES group, indicating that it is more difficult to motivate the former to use mHealth. All factors except digital health anxiety (anxiety) and social relationships were associated significantly with the use intention in the low-SES group, and all factors showed significant associations in the non\u0026ndash;low-SES group. The models including interaction terms showed that perceived usefulness, perceived ease of use, and service availability had significantly stronger relationships with the intention to use mHealth in the non\u0026ndash;low-SES group than in the low-SES group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study revealed differences in the associations of TAM factors with the intention to use mHealth applications between older adults with low and non-low SES. A stronger and more comprehensive approach is needed to stimulate mHealth adoption among low-SES older adults. Policy development with the consideration of specific TAM factors will increase mHealth adoption among older adults. To reduce health disparities, policies should be tailored to older adults\u0026rsquo; needs.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Socioeconomic differences in older adults’ intention to use Mhealth applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 17:34:04","doi":"10.21203/rs.3.rs-5029618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-02T09:38:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-22T21:43:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259443068895791237737757286502673924951","date":"2025-10-09T15:10:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T03:47:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330112131888919390576857293463007835045","date":"2025-09-21T12:16:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-19T11:21:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T14:37:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-09-13T10:18:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"230d7eb4-27a8-4bc2-8707-8affbedcd7e8","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:21:01+00:00","versionOfRecord":{"articleIdentity":"rs-5029618","link":"https://doi.org/10.1186/s12877-026-07340-x","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2026-03-25 16:09:34","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2026-01-19 17:34:04","video":"","vorDoi":"10.1186/s12877-026-07340-x","vorDoiUrl":"https://doi.org/10.1186/s12877-026-07340-x","workflowStages":[]},"version":"v1","identity":"rs-5029618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5029618","identity":"rs-5029618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}