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Acceptance of Mobile Health Apps for Disease Management Among People With Multiple Sclerosis: Web-Based Survey Study

机译:基于Web的调查研究对多发性硬化症患者进行疾病管理的移动医疗应用程序的接受度

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Background: Mobile health (mHealth) apps might have the potential to promote self-management of people with multiple sclerosis (MS) in everyday life. However, the uptake of MS apps remains poor, and little is known about the facilitators and barriers for their efficient utilization, such as technology acceptance. Objective: The aim of this study was to examine the acceptance of mHealth apps for disease management in the sense of behavioral intentions to use and explore determinants of utilization among people with MS based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Methods: Participants for this Web-based cross-sectional study were recruited throughout Germany with the support of regional MS associations and self-help groups. To identify determinants of intention to use MS apps, a measure based on the UTAUT was adapted with 4 key determinants (performance expectancy, effort expectancy, social influence, and facilitating conditions) and extended by Intolerance of Uncertainty (IU) and electronic health literacy. Potential influencing effects of both MS and computer self-efficacy (C-SE) as mediators and fatigue as a moderator were analyzed using Hayes’s PROCESS macro (SPSS version 3.0) for IBM SPSS version 24.0. Results: A total of 98 participants (mean age 47.03 years, SD 10.17; 66/98, 67% female) with moderate fatigue levels completed the survey. Although most participants (91/98, 92%) were daily smartphone users, almost two-thirds (62/98, 63%) reported no experience with MS apps. Overall, the acceptance was moderate on average (mean 3.11, SD 1.31, minimum=1 and maximum=5), with lower scores among persons with no experience ( P =.04) and higher scores among current users ( P .001). In multiple regression analysis ( R 2 =63% variance explained), performance expectancy (beta=.41) and social influence (beta=.33) were identified as significant predictors of acceptance (all P .001). C-SE was confirmed as a partial mediator in the relationship between IU and acceptance (indirect effect: B=?.095, 95% CI ?0.227 to ?0.01). Furthermore, a moderated mediation by C-SE was shown in the relationship between IU and behavioral intentions to use MS apps for low (95% CI ?0.42 to ?0.01) and moderate levels (95% CI ?0.27 to ?0.01) of fatigue. Conclusions: Overall, this exploratory pilot study indicates for the first time that positive expectations about the helpfulness for self-management purposes and social support might be important factors to be considered for improving the acceptance of MS apps among smartphone users with MS. However, given some inconsistent findings, especially regarding the role of effort expectancy and IU and self-efficacy, the conceptual model needs replication with a larger sample of people with MS, varying more in fatigue levels, and a longitudinal assessment of the actual usage of MS apps predicted by acceptance in the sense of behavioral intentions to use.
机译:背景:移动健康(mHealth)应用可能会促进日常生活中多发性硬化症(MS)患者的自我管理。但是,MS应用程序的使用率仍然很低,并且对于促进程序和有效利用它们的障碍(例如技术接受度)知之甚少。目的:本研究的目的是从行为意图的意义上检验mHealth应用程序对疾病管理的接受程度,并基于统一的接受和技术使用理论(UTAUT)探索MS患者之间的利用决定因素。方法:在整个地区的MS协会和自助小组的支持下,在整个德国招募了这个基于Web的横断面研究的参与者。为了确定使用MS应用程序的意愿的决定因素,基于UTAUT的衡量标准采用了4个关键决定因素(预期绩效,预期工作量,社会影响力和促进条件)进行了调整,并通过不确定性(IU)和电子健康素养进行了扩展。使用Hayes针对IBM SPSS 24.0版的PROCESS宏(SPSS 3.0版),分析了MS和计算机自我效能感(C-SE)作为中介者以及疲劳作为主持人的潜在影响。结果:共有98位参与者(平均年龄47.03岁,SD 10.17; 66/98,女性67%)完成了调查。尽管大多数参与者(91 / 98,92%)是每天使用智能手机的用户,但近三分之二(62 / 98,63%)的用户没有使用MS应用程序的经验。总体而言,接受程度平均为中等(平均3.11,SD 1.31,最小值= 1,最大值= 5),无经验者的得分较低(P = .04),当前用户的得分较高(P <.001) 。在多元回归分析中(解释了R 2 = 63%的方差),绩效预期(beta = .41)和社会影响力(beta = .33)被确定为接受程度的重要预测因子(所有P <.001)。 C-SE被确认为IU和接受之间的部分中介(间接作用:B =?095,95%CI≤0.227至≤0.01)。此外,在低疲劳(95%CI≤0.42至≤0.01)和中等水平(95%CI≤0.27至≤0.01)和使用中等水平(95%CI≤0.27至≤0.01)的IU与使用MS应用程序的行为意图之间的关系中显示了C-SE的适度调解。 。结论:总体而言,这项探索性先导研究首次表明,对于自我管理和社会支持的帮助的积极期望可能是提高MS智能手机用户对MS应用程序接受度的重要因素。但是,鉴于一些不一致的发现,尤其是在预期工作量,IU和自我效能方面,研究人员使用了概念模型,需要复制大量的MS患者,并在疲劳水平上有更多差异,并纵向评估其实际使用情况。通过接受使用行为意图来预测的MS应用程序。

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