Wu Chao-Yi, Tibbitts Deanne, Beattie Zachary, Dodge Hiroko, Shannon Jackilen, Kaye Jeffrey, Winters-Stone Kerri
Department of Neurology, Oregon Health & Science University, Portland, OR, United States.
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States.
JMIR Form Res. 2023 Aug 10;7:e45693. doi: 10.2196/45693.
Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults' physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter.
In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis.
Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi-enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys.
Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre- to post-cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22).
A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer.
描述诸如癌症诊断等标志性健康事件前后的健康和行为变化具有挑战性，因为缺乏足够频繁收集的纵向客观测量数据，以捕捉该事件发生前后不同的变化轨迹。一个持续被动评估系统，能够持续监测老年人的身体活动、体重、用药行为、疼痛、健康事件和情绪，这有助于识别出癌症诊断前更具体的健康和行为模式，以及诊断后这些模式是否改变及如何改变。
在本研究中，我们进行了一项概念验证性回顾性分析，识别老年人中的新发癌症诊断，并比较癌症诊断前后健康和行为的变化轨迹。
参与者为10名老年人（平均年龄71.8岁，标准差4.9岁；3/10，30%为女性），来自一项针对老年人的更大规模前瞻性队列研究，患有各种自我报告的癌症类型。一个与技术无关的评估平台使用多种设备，通过可穿戴传感器（活动记录仪）提供每日身体活动的连续数据；通过支持Wi-Fi的数字秤提供体重数据；使用支持蓝牙的电子药盒提供每日用药行为数据；通过在线自我报告调查提供每周的疼痛、健康事件和情绪数据。
纵向线性混合效应模型显示，癌症前后步数轨迹（P<.001）、步数变异性（P=.004）、体重（P<.001）、疼痛严重程度（P<.001）、住院或急诊就诊次数（P=.03）、夜间离家天数（P=.01）以及药盒开门次数（P<.001）存在显著差异。在癌症诊断前的一年中，与癌症诊断后一年相比，步数和体重逐渐减少，疼痛严重程度、步数变异性、住院或急诊就诊次数以及夜间离家天数逐渐增加。在癌症诊断后的一年中，与癌症诊断前一年相比，药盒开门次数逐渐增加。在癌症诊断前后，情绪低落（P=.60）和孤独感（P=.22）方面的轨迹没有显著变化。
一个基于家庭的、与技术无关的多领域评估平台，可以为在改变生活的健康事件前后并行监测不同类型的行为和健康指标提供独特方法。持续被动监测具有生态效度、不易产生偏差且能减轻参与者负担，这可以极大地加强旨在改善癌症患者早期检测、临床护理和治疗结果的研究。
