Zhang Anao, Kamat Aarti, Acquati Chiara, Aratow Michael, Kim Johnny S, DuVall Adam S, Walling Emily
School of Social Work, University of Michigan, Ann Arbor, MI 48109, USA.
Adolescent and Young Adult Oncology Program, University of Michigan Health, Ann Arbor, MI 48109, USA.
Cancers (Basel). 2022 Feb 12;14(4):914. doi: 10.3390/cancers14040914.
Adolescents and young adults (AYAs) diagnosed with cancer are an age-defined population, with studies reporting up to 45% of the population experiencing psychological distress. Although it is essential to screen and monitor for psychological distress throughout AYAs' cancer journeys, many cancer centers fail to effectively implement distress screening protocols largely due to busy clinical workflow and survey fatigue. Recent advances in mobile technology and speech science have enabled flexible and engaging methods to monitor psychological distress. However, patient-centered research focusing on these methods' feasibility and acceptability remains lacking. Therefore, in this project, we aim to evaluate the feasibility and acceptability of an artificial intelligence (AI)-enabled and speech-based mobile application to monitor psychological distress among AYAs diagnosed with cancer. We use a single-arm prospective cohort design with a stratified sampling strategy. We aim to recruit 60 AYAs diagnosed with cancer and to monitor their psychological distress using an AI-enabled speech-based distress monitoring tool over a 6 month period. The primary feasibility endpoint of this study is defined by the number of participants completing four out of six monthly distress assessments, and the acceptability endpoint is defined both quantitatively using the acceptability of intervention measure and qualitatively using semi-structured interviews.
被诊断患有癌症的青少年和青年（AYAs）是一个按年龄界定的人群，研究报告称该人群中高达45%的人经历过心理困扰。尽管在AYAs的癌症治疗过程中筛查和监测心理困扰至关重要，但许多癌症中心由于繁忙的临床工作流程和调查疲劳，未能有效实施困扰筛查方案。移动技术和语音科学的最新进展使监测心理困扰的方法更加灵活且引人入胜。然而，针对这些方法的可行性和可接受性的以患者为中心的研究仍然匮乏。因此，在本项目中，我们旨在评估一款基于人工智能（AI）和语音的移动应用程序用于监测被诊断患有癌症的AYAs心理困扰的可行性和可接受性。我们采用单臂前瞻性队列设计和分层抽样策略。我们的目标是招募60名被诊断患有癌症的AYAs，并在6个月的时间内使用一款基于人工智能语音的困扰监测工具来监测他们的心理困扰。本研究的主要可行性终点由完成六个月中四个月困扰评估的参与者数量来定义，可接受性终点则通过使用干预措施可接受性进行定量定义，并通过半结构化访谈进行定性定义。
