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Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study
被引:0
|作者:
Wu, Chao-Yi
[1
,2
,5
]
Tibbitts, Deanne
[3
,4
]
Beattie, Zachary
[1
]
Dodge, Hiroko
[1
,2
]
Shannon, Jackilen
[4
]
Kaye, Jeffrey
[1
]
Winters-Stone, Kerri
[3
,4
]
机构:
[1] Oregon Hlth & Sci Univ, Dept Neurol, Portland, OR USA
[2] Harvard Med Sch, Massachusetts Gen Hosp, Dept Neurol, Charlestown, MA USA
[3] Oregon Hlth & Sci Univ, Div Oncol Sci, Portland, OR USA
[4] Oregon Hlth & Sci Univ, Knight Canc Inst, Portland, OR USA
[5] Harvard Med Sch, Massachusetts Gen Hosp, Dept Neurol, 149 13th St,10-003C, Charlestown, MA 02129 USA
基金:
美国国家卫生研究院;
关键词:
sensor;
quality of life;
physical activity;
medication;
monitoring;
function;
mobile phone;
DAILY COMPUTER USE;
PREVALENCE;
COGNITION;
BURDEN;
D O I:
10.2196/45693
中图分类号:
R19 [保健组织与事业(卫生事业管理)];
学科分类号:
摘要:
Background: 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. Objective: 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.Methods: 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.Results: 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).Conclusions: 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. (JMIR Form Res 2023;7:e45693) doi: 10.2196/45693
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