Kazi Khalid, Ali Syed Mustafa, Selby David A, McBeth John, van der Veer Sabine, Dixon William G
Northern Care Alliance NHS Foundation Trust, Salford, UK.
Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
J Multimorb Comorb. 2023 Jan 18;13:26335565221150129. doi: 10.1177/26335565221150129. eCollection 2023 Jan-Dec.
People living with multiple long-term conditions (MLTC-M) (multimorbidity) experience a range of inter-related symptoms. These symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices, and then summarised to provide useful clinical insight.
We aimed to perform an exploratory analysis to summarise the extent and trajectory of multiple symptom ratings tracked via a smartwatch, and to investigate the relationship between these symptom ratings and demographic factors in people living with MLTC-M in a feasibility study.
'Watch Your Steps' was a prospective observational feasibility study, administering multiple questions per day over a 90 day period. Adults with more than one clinician-diagnosed long-term condition rated seven core symptoms each day, plus up to eight additional symptoms personalised to their LTCs per day. Symptom ratings were summarised over the study period at the individual and group level. Symptom ratings were also plotted to describe day-to-day symptom trajectories for individuals.
Fifty two participants submitted symptom ratings. Half were male and the majority had LTCs affecting three or more disease areas (N = 33, 64%). The symptom rated as most problematic was fatigue. Patients with increased comorbidity or female sex seemed to be associated with worse experiences of fatigue. Fatigue ratings were strongly correlated with pain and level of dysfunction.
In this study we have shown that it is possible to collect and descriptively analyse self reported symptom data in people living with MLTC-M, collected multiple times per day on a smartwatch, to gain insights that might support future clinical care and research.
患有多种长期病症(MLTC-M)(共病)的人会经历一系列相互关联的症状。这些症状可以通过消费技术(如智能手机和可穿戴设备)进行纵向跟踪,然后进行汇总以提供有用的临床见解。
在一项可行性研究中,我们旨在进行探索性分析,以汇总通过智能手表跟踪的多种症状评分的范围和轨迹,并研究这些症状评分与MLTC-M患者人口统计学因素之间的关系。
“留意你的步伐”是一项前瞻性观察性可行性研究,在90天内每天提出多个问题。患有一种以上经临床医生诊断的长期病症的成年人每天对七种核心症状进行评分,另外每天还可根据其长期病症对多达八种额外症状进行个性化评分。在研究期间,对个体和群体层面的症状评分进行了汇总。还绘制了症状评分图,以描述个体的日常症状轨迹。
52名参与者提交了症状评分。一半为男性,大多数人的长期病症影响三个或更多疾病领域(N = 33,64%)。被认为问题最大的症状是疲劳。合并症增加或女性患者似乎与更严重的疲劳体验有关。疲劳评分与疼痛和功能障碍程度密切相关。
在本研究中,我们表明,对于患有MLTC-M的人,每天多次通过智能手表收集并描述性分析自我报告的症状数据,以获得可能支持未来临床护理和研究的见解是可行的。