Determinant Factors of Public Acceptance of Stress Management Apps: Survey Study

Apolinário-Hagen, Jennifer; Hennemann, Severin; Fritsche, Lara; Drüge, Marie; Breil, Bernhard

doi:10.2196/15373

Cited by 51 publications

(47 citation statements)

References 116 publications

(205 reference statements)

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“…Little is known about the status and correlates of technological readiness, proficiency, and motivational readiness in the US dialysis population. Studies in the general population have identified the willingness of people to use mobile health in the United States and across the world (17)(18)(19)(20). A US nonnephrology study exploring mobile health readiness in older individuals receiving health care reported high levels of access to mobile health devices and the internet, with 60% of respondents using mobile devices and the internet for health information (21).…”

Section: Introductionmentioning

confidence: 99%

The Mobile Health Readiness of People Receiving In-Center Hemodialysis and Home Dialysis

Hussein

Bennett

Pace

et al. 2020

CJASN

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Background and objectivesMobile health is the health care use of mobile devices, such as smartphones. Mobile health readiness is a prerequisite to successful implementation of mobile health programs. The aim of this study was to examine the status and correlates of mobile health readiness among individuals on dialysis.Design, setting, participants, & measurementsA cross-sectional 30-item questionnaire guided by the Khatun mobile health readiness conceptual model was distributed to individuals on dialysis from 21 in-center hemodialysis facilities and 14 home dialysis centers. The survey assessed the availability of devices and the internet, proficiency, and interest in using mobile health.ResultsIn total, 949 patients (632 hemodialysis and 317 home dialysis) completed the survey. Of those, 81% owned smartphones or other internet-capable devices, and 72% reported using the internet. The majority (70%) reported intermediate or advanced mobile health proficiency. The main reasons for using mobile health were appointments (56%), communication with health care personnel (56%), and laboratory results (55%). The main reported concerns with mobile health were privacy and security (18%). Mobile health proficiency was lower in older patients: compared with the 45- to 60-years group, respondents in age groups <45, 61–70, and >70 years had adjusted odds ratios of 5.04 (95% confidence interval, 2.23 to 11.38), 0.39 (95% confidence interval, 0.24 to 0.62), and 0.22 (95% confidence interval, 0.14 to 0.35), respectively. Proficiency was lower in participants with Hispanic/Latinx ethnicity (adjusted odds ratio, 0.49; 95% confidence interval, 0.31 to 0.75) and with less than college education (adjusted odds ratio for “below high school,” 0.09; 95% confidence interval, 0.05 to 0.16 and adjusted odds ratio for “high school only,” 0.26; 95% confidence interval, 0.18 to 0.39). Employment was associated with higher proficiency (adjusted odds ratio, 2.26; 95% confidence interval, 1.18 to 4.32). Although home dialysis was associated with higher proficiency in the unadjusted analyses, we did not observe this association after adjustment for other factors.ConclusionsThe majority of patients on dialysis surveyed were ready for, and proficient in, mobile health.Clinical Trial registry name and registration number: Dialysis mHealth Survey,NCT04177277

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Section: Introductionmentioning

confidence: 99%

The Mobile Health Readiness of People Receiving In-Center Hemodialysis and Home Dialysis

Hussein

Bennett

Pace

et al. 2020

CJASN

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“…Perceived stress may shift one's attention toward related harms caused by chronic or excessive strain and the need for support. Stress is often assessed as an unspecific indicator of symptom severity and was found to be associated with intentions to use e-mental health services, 31,55 including a higher interest in using mHealth apps for stress management. 51 From a public health perspective, there is a high potential of using digital prevention of mental disorders, especially regarding otherwise than online hard to reach populations like young adults and people perceiving stigma with a preference for online activities.…”

Section: Determinants Of the Acceptance Of Digital Stress Prevention mentioning

confidence: 99%

Exploring User-Related Drivers of the Early Acceptance of Certified Digital Stress Prevention Programs in Germany

Apolinário-Hagen

Hennemann²,

Kück

et al. 2020

Health�Serv�Insights

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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 ( R2 = .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.

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“…Previous studies investigated factors influencing mHealth use in specific target groups (e.g. [54,55]), for specific diseases or health topics [56][57][58][59][60], and for sustained mHealth engagement [61]. This study aims to explicate the effect of social support.…”

Section: Social Support and Technology-supported Diabetes Selfmanagementmentioning

confidence: 99%

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

Brew‐Sam

Chib

Rossmann

2020

BMC Med Inform Decis Mak

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Background: Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as a means for delivering social support, this paper argues that social support has an impact on the use of diabetes mHealth apps. Specifically, we postulate differences between the impact of healthcare professional versus nonprofessional (family/friends) support on mobile app use for diabetes self-management. Methods: This research employed a triangulation of methods including exploratory semi-structured face-to-face interviews (N = 21, Study 1) and an online survey (N = 65, Study 2) with adult type 1 and type 2 diabetes patients. Thematic analysis (Study 1) was used to explore the relevance of social support (by professionals versus nonprofessionals) for diabetes app use. Binary logistic regression (Study 2) was applied to compare healthcare decisionmaking, healthcare-patient communication, and the support by the personal patient network as predictors of diabetes app use, complemented by other predictors from self-management and technology adoption theory. Results: The interviews (Study 1) demonstrated that (technology-supported) shared decision-making and supportive communication by healthcare professionals depended on their medical specialty. The personal patient network was perceived as either facilitating or hindering the use of mHealth for self-management. Binary logistic regression (Study 2) showed that the physician specialty significantly predicted the use of diabetes apps, with supervision by diabetes specialists increasing the likelihood of app use (as opposed to general practitioners). Additionally, specialist care positively related to a higher chance of shared decision-making and better physicianpatient communication. The support by the personal patient network predicted diabetes app use in the opposite direction, with less family/friend support increasing the likelihood of app use. Conclusion: The results emphasize the relevance of support by healthcare professionals and by the patient network for diabetes app use and disclose differences from the existing literature. In particular, the use of diabetes apps may increase in the absence of social support by family or friends (e.g., compensation for lack of support), and may decrease when such support is high (e.g., no perceived need to use technology).

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Determinant Factors of Public Acceptance of Stress Management Apps: Survey Study

Cited by 51 publications

References 116 publications

The Mobile Health Readiness of People Receiving In-Center Hemodialysis and Home Dialysis

The Mobile Health Readiness of People Receiving In-Center Hemodialysis and Home Dialysis

Exploring User-Related Drivers of the Early Acceptance of Certified Digital Stress Prevention Programs in Germany

Differential influences of social support on app use for diabetes self-management – a mixed methods approach

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