Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida,USA.
Department of Advertising, College of Journalism and Communications, University of Florida, Gainesville, Florida,USA.
J Gerontol A Biol Sci Med Sci. 2023 May 11;78(5):821-830. doi: 10.1093/gerona/glad046.
Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings.
Data come from a sample of 31 participants (Mage = 74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures.
Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance = 70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r = 0.50-0.51, p < .05) and ecologically measured pain (r = 0.72, p = .01), but others were not (eg, ecologically measured fatigue).
ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.
早期发现活动能力下降对于预防生活质量下降、残疾和死亡至关重要。然而，传统的活动能力评估方法在捕捉与偶发性健康事件相一致的日常波动方面存在局限性。我们旨在描述我们的实时在线评估和活动监测（ROAMM）智能手表应用程序的试点研究结果，该应用程序在生态环境中实时独特地捕获多个数据流。
数据来自 31 名参与者（平均年龄 74.7 岁，51.6%为女性）的样本，他们使用 ROAMM 大约两周。我们描述了 ROAMM 的可用性和可行性，使用描述性指标总结提示数据，并将提示数据与传统的基于调查的问卷或其他既定措施进行比较。
参与者对 ROAMM 的功能感到满意（87.1%），并将可用性评为“高于平均水平”。大多数参与者高度参与（平均调整依从率为 70.7%），大多数参与者表示“可能”参加为期两年的研究（77.4%）。一些智能手表功能与其各自的传统测量值相关（例如，某些 GPS 衍生的生活空间活动能力特征（r = 0.50-0.51，p <.05）和生态测量的疼痛（r = 0.72，p =.01），但其他功能则不然（例如，生态测量的疲劳）。
ROAMM 在我们的试点样本中是可用的、可接受的，并且能够有效地测量活动能力和活动能力下降的风险因素。需要更大和更多样化的样本进行额外的工作，以确认智能手表测量特征与传统测量值之间的关联。通过在生态环境中同时监测多个数据流，这项技术可以独特地为活动能力测量和活动能力丧失的风险因素的演变做出贡献。
