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Smartphone Applications for Educating and Helping Non-motivating Patients Adhere to Medication That Treats Mental Health Conditions: Aims and Functioning

机译:用于教育和帮助没有动力的患者的智能手机应用程序坚持治疗精神健康状况的药物:目标和功能

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>Background: Patients prescribed with medication that treats mental health conditions benefit the most compared to those prescribed with other types of medication. However, they are also the most difficult to adhere. The development of mobile health (mHealth) applications (“apps”) to help patients monitor their adherence is fast growing but with limited evidence on their efficacy. There is no evidence on the content of these apps for patients taking psychotropic medication. The aim of this study is to identify and evaluate the aims and functioning of available apps that are aiming to help and educate patients to adhere to medication that treats mental health conditions.>Method: Three platform descriptions (Apple, Google, and Microsoft) were searched between October 2015 and February 2016. Included apps need to focus on adherence to medication that treats mental health conditions and use at least a reinforcement strategy. Descriptive information was extracted and apps evaluated on a number of assessment criteria using content analysis.>Results: Sixteen apps were identified. All apps included self-monitoring properties like reminders and psycho-educational properties like mood logs. It was unclear how the latter were used or how adherence was measured. Major barriers to medication adherence like patients' illness and medication beliefs and attitudes were not considered nor where information to patients about mediation side effects. Very few apps were tailored and none was developed based on established theories explaining the processes for successful medication adherence like cognitions and beliefs. Reported information on app development and validation was poor.>Discussion: A variety of apps with different properties that tackle both intentional and unintentional non-adherence from a different perspective are identified. An evidence-based approach and co-creation with patients is needed. This will ensure that the apps increase the possibility to impact on non-adherence. Theories like social cognition models can be useful in ensuring that patients' education, motivation, skills, beliefs, and type of adherence are taken into consideration when developing the apps. Findings from this study can help clinicians and patients make informed choices and pursue policy-makers to integrate evidence when developing future apps. Quality-assurance tools are needed to ensure the apps are systematically evaluated.
机译:>背景:与其他类型的药物相比,处方了可治疗精神疾病的药物的患者受益最大。但是,它们也是最难坚持的。帮助患者监测依从性的移动健康(mHealth)应用程序(“ apps”)的开发正在迅速发展,但是其功效的证据有限。对于服用精神药物的患者,这些应用程序的内容没有任何证据。这项研究的目的是确定和评估可用应用程序的目标和功能,这些应用程序旨在帮助和教育患者坚持治疗精神疾病的药物。>方法:三个平台描述(Apple,在2015年10月至2016年2月期间对Google和Microsoft进行了搜索。所包含的应用需要重点关注对治疗精神健康状况的药物的依从性,并至少使用强化策略。提取了描述性信息,并使用内容分析根据许多评估标准对应用程序进行了评估。>结果:确定了16个应用程序。所有应用程序都包含提醒等自我监控属性,以及心情记录等心理教育属性。尚不清楚如何使用后者或如何测量依从性。没有考虑药物依从性的主要障碍,例如患者的疾病以及药物的信仰和态度,也没有考虑患者对调解副作用的了解。很少量身定制的应用程序,并且都没有基于已建立的理论来开发任何应用程序,这些理论解释了成功遵循药物的过程,例如认知和信念。报告的有关应用程序开发和验证的信息很差。>讨论:确定了具有不同属性的各种应用程序,这些应用程序从不同的角度解决了有意和无意的不遵守。需要基于证据的方法并与患者共同创造。这将确保应用增加了影响不遵守的可能性。诸如社会认知模型之类的理论可用于确保在开发应用程序时考虑患者的教育程度,动机,技能,信念和依从性类型。这项研究的结果可以帮助临床医生和患者做出明智的选择,并在制定未来的应用程序时寻求政策制定者整合证据。需要质量保证工具来确保对应用程序进行系统评估。

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