A quantitative study exploring the acceptance of the eHealth model for mental wellness among digital workers [version 2; peer review: 2 approved]
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SUBMITTER: Tan C
PROVIDER: S-EPMC9214265 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDiabetes is a very common chronic disease that exerts massive physiological and psychological burdens on patients. The digitalization of mental health care has generated effective e-mental health approaches, which offer an indubitable practical value for patient treatment. However, before implementing and optimizing e-mental health tools, their acceptance and underlying barriers and resources should be first determined for developing and establishing effective patient-oriented interventions.ObjectiveThis study aims to assess the acceptance of e-mental health interventions among patients with diabetes and explore its underlying barriers and resources.MethodsA cross-sectional study was conducted in Germany from April 9, 2020, to June 15, 2020, through a web-based survey for which patients were recruited via web-based diabetes channels. The eligibility requirements were adult age (18 years or older), a good command of the German language, internet access, and a diagnosis of diabetes. Acceptance was measured using a modified questionnaire, which was based on the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) and assessed health-related internet use, acceptance of e-mental health interventions, and its barriers and resources. Mental health was measured using validated and established instruments, namely the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-2, and Distress Thermometer. In addition, sociodemographic and medical data regarding diabetes were collected.ResultsOf the 340 participants who started the survey, 261 (76.8%) completed it and the final sample comprised 258 participants with complete data sets. The acceptance of e-mental health interventions in patients with diabetes was overall moderate (mean 3.02, SD 1.14). Gender and having a mental disorder had a significant influence on acceptance (P<.001). In an extended UTAUT regression model (UTAUT predictors plus sociodemographics and mental health variables), distress (β=.11; P=.03) as well as the UTAUT predictors performance expectancy (β=.50; P<.001), effort expectancy (β=.15; P=.001), and social influence (β=.28; P<.001) significantly predicted acceptance. The comparison between an extended UTAUT regression model (13 predictors) and the UTAUT-only regression model (performance expectancy, effort expectancy, social influence) revealed no significant difference in explained variance (F10,244=1.567; P=.12).ConclusionsThis study supports the viability of the UTAUT model and its predictors in assessing the acceptance of e-mental health interventions among patients with diabetes. Three UTAUT predictors reached a notable amount of explained variance of 75% in the acceptance, indicating that it is a very useful and efficient method for measuring e-mental health intervention acceptance in patients with diabetes. Owing to the close link between acceptance and use, acceptance-facilitating interventions focusing on these three UTAUT predictors should be fostered to bring forward the highly needed establishment of effective e-mental health interventions in psychodiabetology.
| S-EPMC8367156 | biostudies-literature
Project description:BACKGROUND:Constantly increasing health care costs have led countries and health care providers to the point where health care systems must be reinvented. Consequently, electronic health (eHealth) has recently received a great deal of attention in social sciences in the domain of Internet studies. However, only a fraction of these studies focuses on the acceptability of eHealth, making consumers' subjective evaluation an understudied field. This study will address this gap by focusing on the acceptance of MyData-based preventive eHealth services from the consumer point of view. We are adopting the term "MyData", which according to a White Paper of the Finnish Ministry of Transport and Communication refers to "1) a new approach, a paradigm shift in personal data management and processing that seeks to transform the current organization centric system to a human centric system, 2) to personal data as a resource that the individual can access and control." OBJECTIVE:The aim of this study was to investigate what factors influence consumers' intentions to use a MyData-based preventive eHealth service before use. METHODS:We applied a new adoption model combining Venkatesh's unified theory of acceptance and use of technology 2 (UTAUT2) in a consumer context and three constructs from health behavior theories, namely threat appraisals, self-efficacy, and perceived barriers. To test the research model, we applied structural equation modeling (SEM) with Mplus software, version 7.4. A Web-based survey was administered. We collected 855 responses. RESULTS:We first applied traditional SEM for the research model, which was not statistically significant. We then tested for possible heterogeneity in the data by running a mixture analysis. We found that heterogeneity was not the cause for the poor performance of the research model. Thus, we moved on to model-generating SEM and ended up with a statistically significant empirical model (root mean square error of approximation [RMSEA] 0.051, Tucker-Lewis index [TLI] 0.906, comparative fit index [CFI] 0.915, and standardized root mean square residual 0.062). According to our empirical model, the statistically significant drivers for behavioral intention were effort expectancy (beta=.191, P<.001), self-efficacy (beta=.449, P<.001), threat appraisals (beta=.416, P<.001), and perceived barriers (beta=-.212, P=.009). CONCLUSIONS:Our research highlighted the importance of health-related factors when it comes to eHealth technology adoption in the consumer context. Emphasis should especially be placed on efforts to increase consumers' self-efficacy in eHealth technology use and in supporting healthy behavior.
| S-EPMC5756317 | biostudies-literature
Project description:Rationale, aims and objectivesLittle is known about hesitancy to receive the COVID-19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID-19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.MethodsThis was a cross-sectional study based on electronic survey data that was collected in Iraq during December first-19th, 2020. The electronic survey was designed using Qualtrics. HBM was followed to develop the survey items. A regression analysis was used to identify factors influencing people accepting vaccination. Thematic analysis for participant comments to an open-ended question.ResultsA total of 1680 completed surveys were received. The mean age of 31.2 ± 9.9 years, 53.0% were female and 47.0% were male. The largest group was HCWs (45.7%), followed by general population (37.5%) and health college students (16.8%). Our findings expressed some hesitancy to receive the COVID-19 vaccine with the acceptance rate of 61.7%. The HCWs perceived significantly higher susceptibility and severity of the COVID-19 infection compared to the general population. The HCWs were significantly more likely than the general population to receive COVID-19 vaccine. Concerns with proper storage was the biggest barrier to vaccine receipt. The regression analysis indicated eight factors that were significantly associated with the willingness to receive COVID-19 vaccine: Preventive measures, perceived benefit, perceived barriers, cue to action, subjective norm, supportive of vaccination in general and having received a flu vaccine before.ConclusionsAwareness campaign can focus on enhancing the vaccine perceived benefit, debunking misconceptions, and increasing the disease perceived severity. Additionally, the public health leaders need to minimize the perceived barriers by providing the vaccines and appeasing people concerns about their storage, effectiveness, and adverse events.
| S-EPMC8242385 | biostudies-literature
Project description:BackgroundMedical students show a relatively high prevalence for common mental disorders. Only few of those in need for treatment seek professional help. Therefore, easily accessible interventions are required. While several evidence-based internet- and mobile-based interventions (IMIs) have been proposed, little is known about medical students' attitudes towards using them.ObjectiveWe aimed to explore the views of medical students on IMIs as well as facilitators and barriers to use them and gain first insights into their preferences for tailored IMIs.MethodsWe conducted four focus groups with 26 medical students enrolled at a German medical school in March 2020. Focus groups were audio-recorded, transcribed and analyzed following established approaches for qualitative content analysis.ResultsMedical students valued IMIs for their low-threshold and flexible access, their potential to bridge waiting times and as a first step towards face-to-face-therapy. However, medical students preferred face-to-face interventions in case of severe mental health problems. The main disadvantages named by students included difficulties to find or decide on suitable IMIs based on clear quality criteria, fear of a misdiagnosis and lack of personalisation and human interaction. Some students also questioned the effectiveness of IMIs. Easy handling, flexible use, data safety and easily understandable terms of use were believed to facilitate the uptake of IMIs, whereas technical problems, frequent notifications, required internet access, need to register, lack of anonymity, high time expenditure and costs were reported to hinder their use. Most students did not prefer IMIs tailored to medical students but rather wanted to use IMIs suitable for students of all disciplines.ConclusionOur results suggest overall positive views regarding IMIs for mental health promotion but concerns regarding their use for severe mental disorders and acute crises. Our findings indicate that IMIs may represent promising tools for stress prevention and early interventions for medical students. Students explicitly stated to prefer quality-approved IMIs recommended and provided by their university.
| S-EPMC8122007 | biostudies-literature
Project description:BackgroundDigital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs.ObjectiveThis study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used.MethodsWe conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain.ResultsWe identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy.ConclusionsAlthough many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective.
| S-EPMC8212628 | biostudies-literature
Project description:BACKGROUND:Technology-based physical activity suggests new opportunities for public health initiatives. Yet only 45% of technology interventions are theoretically based, and the acceptability mechanisms have been insufficiently studied. Acceptability and acceptance theories have provided interesting insights, particularly the unified theory of acceptance and use of technology 2 (UTAUT2). In several studies, the psychometric qualities of acceptability scales have not been well demonstrated. OBJECTIVE:The aim of this study was to adapt the UTAUT2 to the electronic health (eHealth) context and provide a preliminary validation of the eHealth acceptability scale in a French sample. METHODS:In line with the reference validation methodologies, we carried out the following stages of validating the scale with a total of 576 volunteers: translation and adaptation, dimensionality tests, reliability tests, and construct validity tests. We used confirmatory factor analysis to validate a 22-item instrument with 7 subscales: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit. RESULTS:The dimensionality tests showed that the bifactor confirmatory model presented the best fit indexes: ?2173=434.86 (P<.001), ?2/df=2.51, comparative fit index=.97, Tucker-Lewis index=.95, and root mean square error of approximation=.053 (90% CI .047-.059). The invariance tests of the eHealth acceptability factor structure by sex demonstrated no significant differences between models, except for the strict model. The partial strict model demonstrated no difference from the strong model. Cronbach alphas ranged from .77 to .95 for the 7 factors. We measured the internal reliability with a 4-week interval. The intraclass correlation coefficients for each subscale ranged from .62 to .88, and there were no significant differences in the t tests from time 1 to time 2. Assessments for convergent validity demonstrated that the eHealth acceptability constructs were significantly and positively related to behavioral intention, usage, and constructs from the technology acceptance model and the theory of planned behavior. CONCLUSIONS:The 22-item French-language eHealth acceptability scale, divided into 7 subscales, showed good psychometric qualities. This scale is thus a valid and reliable tool to assess the acceptability of eHealth technology in French-speaking samples and offers promising avenues in research, clinical practice, and marketing.
| S-EPMC7191343 | biostudies-literature
Project description:Electronic mental health services represent innovative instruments to increase the dissemination of stress programs in primary prevention. However, little is known about facilitators of their uptake. This study aimed to explore determinants of the acceptance of centrally certified digital stress coping programs and preferences for service delivery modes among adult members of German statutory health insurances. Participants completed a multi-construct 45-item questionnaire covering acceptance of digital stress prevention (behavioral use intention) and potential predictors we assessed using hierarchical regression analysis-(1) socio-demographic variables and time spent online, (2) openness to experience, (3) perceived stress, and (4) attitudes toward e-mental health. Preferences in terms of the willingness to use online, face-to-face and blended programs were analyzed using paired t-tests. Participants (N?=?171, 66% female, 18-69?years) reported a moderate acceptance of digital stress management (M?=?2.76, SD?=?1.16, range: 1-5). We identified younger age (ß?=?-0.16, P?=?.009), openness to experience (ß?=?0.17, P?=?.003), and positive attitudes (ß?=?0.61, P?<?.001) as predictors of acceptance (R 2?=?.50, P?<?.001). Face-to-face was preferred over online (d?=?0.40) and blended (d?=?0.33), and blended over stand-alone online delivery mode (d?=?0.19; all P?<?.001). Our findings indicate that promoting favorable attitudes toward digital stress prevention through tailored information may be a starting point to facilitate their adoption.
| S-EPMC7074489 | biostudies-literature
Project description:BackgroundThe rapid increase in the number of people who are overweight and obese is a worldwide health problem. Obesity is often associated with physiological and mental health burdens. Owing to several barriers to face-to-face psychotherapy, a promising approach is to exploit recent developments and implement innovative e-mental health interventions that offer various benefits to patients with obesity and to the health care system.ObjectiveThis study aims to assess the acceptance of e-mental health interventions in patients with obesity and explore its influencing predictors. In addition, the well-established Unified Theory of Acceptance and Use of Technology (UTAUT) model is compared with an extended UTAUT model in terms of variance explanation of acceptance.MethodsA cross-sectional web-based survey study was conducted from July 2020 to January 2021 in Germany. Eligibility requirements were adult age (≥18 years), internet access, good command of the German language, and BMI >30 kg/m2 (obesity). A total of 448 patients with obesity (grades I, II, and III) were recruited via specialized social media platforms. The impact of various sociodemographic, medical, and mental health characteristics was assessed. eHealth-related data and acceptance of e-mental health interventions were examined using a modified questionnaire based on the UTAUT.ResultsOverall, the acceptance of e-mental health interventions in patients with obesity was moderate (mean 3.18, SD 1.11). Significant differences in the acceptance of e-mental health interventions among patients with obesity exist, depending on the grade of obesity, age, sex, occupational status, and mental health status. In an extended UTAUT regression model, acceptance was significantly predicted by the depression score (Patient Health Questionnaire-8; β=.07; P=.03), stress owing to constant availability via mobile phone or email (β=.06; P=.02), and confidence in using digital media (β=-0.058; P=.04) and by the UTAUT core predictors performance expectancy (β=.45; P<.001), effort expectancy (β=.22; P<.001), and social influence (β=.27; P<.001). The comparison between an extended UTAUT model (16 predictors) and the restrictive UTAUT model (performance expectancy, effort expectancy, and social influence) revealed a significant difference in explained variance (F13,431=2.366; P=.005).ConclusionsThe UTAUT model has proven to be a valuable instrument to predict the acceptance of e-mental health interventions in patients with obesity. The extended UTAUT model explained a significantly high percentage of variance in acceptance (in total 73.6%). On the basis of the strong association between acceptance and future use, new interventions should focus on these UTAUT predictors to promote the establishment of effective e-mental health interventions for patients with obesity who experience mental health burdens.
| S-EPMC8972105 | biostudies-literature
Project description:BackgroundDiabetes management is a growing health care challenge worldwide. eHealth can revolutionize diabetes care, the success of which depends on end user acceptance.ObjectiveThis study aims to understand the readiness and acceptance of eHealth services for diabetes care among the general population, perceived advantages and disadvantages of eHealth, and factors associated with eHealth readiness and acceptance in a multiethnic Asian country.MethodsIn this cross-sectional epidemiological study, participants (N=2895) were selected through disproportionate stratified random sampling from a population registry. Citizens or permanent residents of Singapore aged >18 years were recruited. The data were captured through computer-assisted personal interviews. An eHealth questionnaire was administered in one of four local languages (English, Chinese, Malay, or Tamil), as preferred by the participant. Bivariate chi-square analyses were performed to compare the sociodemographic characteristics and perception of advantages and disadvantages of eHealth services between the diabetes and nondiabetes groups. Multivariable logistic regression models were used to determine factors associated with eHealth readiness and acceptance. All analyses were weighted using survey weights to account for the complex survey design.ResultsThe sample comprised participants with (n=436) and without (n=2459) diabetes. eHealth readiness was low, with 47.3% of the overall sample and 75.7% of the diabetes group endorsing that they were not ready for eHealth (P<.001). The most acceptable eHealth service overall was booking appointments (67.4%). There was a significantly higher preference in the diabetes group for face-to-face sessions for consultation with the clinician (nondiabetes: 83.5% vs diabetes: 92.6%; P<.001), receiving prescriptions (61.9% vs 79.3%; P<.001), referrals to other doctors (51.4% vs 72.2%; P<.001), and receiving health information (34% vs 63.4%; P<.001). The majority of both groups felt that eHealth requires users to be computer literate (90.5% vs 94.3%), does not build clinician-patient rapport compared with face-to-face sessions (77.5% vs 81%), and might not be credible (56.8% vs 64.2%; P=.03). Age (≥35 years), ethnicity (Indian), and lower education status had lower odds of eHealth readiness. Age (≥35 years), ethnicity (Indian), lower education status (primary school), BMI (being underweight), and marital status (being single) were associated with a lower likelihood of eHealth acceptance. Among only those with diabetes, a longer duration of diabetes (4-18 years), higher education (degree or above), and younger age (23-49 years) were associated with eHealth readiness, whereas younger age and income (SGD 2000-3999 [US $1481-$2961]) were associated with acceptance.ConclusionsOverall, an unfavorable attitude toward eHealth was observed, with a significantly higher number of participants with diabetes reporting their unwillingness to use these services for their diabetes care. Sociodemographic factors associated with acceptance and readiness identified a group of people who were unlikely to accept the technology and thus need to be targeted for eHealth literacy programs to avoid health care disparity.International registered report identifier (irrid)RR2-10.1136/bmjopen-2020-037125.
| S-EPMC8446836 | biostudies-literature
Project description:BackgroundDepressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture.ObjectiveFirstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data.MethodsWe conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses.ResultsOverall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), and "motor inhibition" (NMI=0.75). EDA was associated with "aggressive behavior" (NMI=1.0) and "psychic anxiety" (NMI=0.52).ConclusionsPhysiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.
| S-EPMC10196899 | biostudies-literature