Mattsson Susanne, Olsson Erik Martin Gustaf, Carlsson Maria, Johansson Birgitta Beda Kristina
Lifestyle and Rehabilitation in long term illness, Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden.
Clinical Psychology in Healthcare, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
J Med Internet Res. 2019 Apr 5;21(4):e11387. doi: 10.2196/11387.
Physicians and nurses in cancer care easily fail to detect symptoms of psychological distress because of barriers such as lack of time, training on screening methods, and knowledge about how to diagnose anxiety and depression. National guidelines in several countries recommend routine screening for emotional distress in patients with cancer, but in many clinics, this is not implemented. By inventing screening methods that are time-efficient, such as digitalized and automatized screenings with short instruments, we can alleviate the burden on patients and staff.
The aim of this study was to compare Web-based versions of the ultrashort electronic Visual Analogue Scale (eVAS) anxiety and eVAS depression and the short Hospital Anxiety and Depression Scale (HADS) with Web-based versions of the longer Montgomery Åsberg Depression Rating Scale-Self-report (MADRS-S) and the State Trait Anxiety Inventory- State (STAI-S) with regard to their ability to identify symptoms of anxiety and depression in patients with cancer.
Data were obtained from a consecutive sample of patients with newly diagnosed (<6 months) breast, prostate, or colorectal cancer or with recurrence of colorectal cancer (N=558). The patients were recruited at 4 hospitals in Sweden between April 2013 and September 2015, as part of an intervention study administered via the internet. All questionnaires were completed on the Web at the baseline assessment in the intervention study.
The ultrashort and short Web-based-delivered eVAS anxiety, eVAS depression and HADS were found to have an excellent ability to discriminate between persons with and without clinical levels of symptoms of anxiety and depression compared with recommended cutoffs of the longer instruments MADRS-S and STAI-S (area under the curve: 0.88-0.94). Cutoffs of >6 on HADS anxiety and >7 hundredths (hs) on eVAS anxiety identified patients with anxiety symptoms with high accuracy. For HADS depression, at a cutoff of >5 and eVAS depression at a cutoff of >7 hs, the accuracy was very high likewise.
The use of the short and ultrashort tools, eVAS and HADS, may be a suitable initial method of Web-based screening in busy clinical settings. However, there are still a proportion of patients who lack access to the internet or the ability to use it. There is a need to find solutions for this group to find all the patients with psychological distress.
癌症护理中的医生和护士由于缺乏时间、筛查方法培训以及如何诊断焦虑和抑郁的知识等障碍，很容易无法察觉心理困扰的症状。几个国家的国家指南建议对癌症患者进行情绪困扰的常规筛查，但在许多诊所，这并未得到实施。通过发明高效省时的筛查方法，如使用简短工具进行数字化和自动化筛查，我们可以减轻患者和工作人员的负担。
本研究的目的是比较基于网络的超短电子视觉模拟量表（eVAS）焦虑版和eVAS抑郁版、简短医院焦虑抑郁量表（HADS）与基于网络的较长的蒙哥马利·阿斯伯格抑郁评定量表-自评版（MADRS-S）和状态-特质焦虑量表-状态版（STAI-S）在识别癌症患者焦虑和抑郁症状方面的能力。
数据来自连续抽样的新诊断（<6个月）乳腺癌、前列腺癌或结直肠癌患者或结直肠癌复发患者（N = 558）。2013年4月至2015年9月期间，这些患者在瑞典的4家医院招募，作为通过互联网进行的一项干预研究的一部分。在干预研究的基线评估中，所有问卷均在网络上完成。
与较长工具MADRS-S和STAI-S的推荐临界值相比，发现基于网络的超短和短版eVAS焦虑、eVAS抑郁和HADS在区分有无临床水平焦虑和抑郁症状的人方面具有出色的能力（曲线下面积：0.88 - 0.94）。HADS焦虑评分>6以及eVAS焦虑评分>7百分位（hs）时，能高精度地识别出有焦虑症状的患者。对于HADS抑郁，临界值>5以及eVAS抑郁临界值>7 hs时，准确性同样很高。
在繁忙的临床环境中，使用简短和超短工具eVAS和HADS可能是基于网络筛查的合适初始方法。然而，仍有一部分患者无法接入互联网或没有使用互联网的能力。需要为这一群体找到解决方案，以便发现所有有心理困扰的患者。
