Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (UMRS 1136), 75013 Paris, France.
APHP, Rheumatology Department, Pitié Salpêtrière Hospital, 75013 Paris, France.
Sensors (Basel). 2020 Aug 25;20(17):4797. doi: 10.3390/s20174797.
In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.
在医疗保健领域，身体活动可以通过两种方式进行监测：患者自身的自我监测或健康专业人员的外部监测。关于自我监测，可穿戴活动追踪器允许自动被动数据收集，从而教育和激励患者。佩戴活动追踪器可以每天增加约 1500 步的步行时间。然而，人们对测量准确性存在一些担忧（例如，缺乏通用的验证协议或不同设备之间的测量差异）。对于外部监测，许多创新的电子工具目前在风湿病学中用于帮助支持医生的时间管理，减少诊所时间的负担，并优先考虑可能需要进一步关注的患者。在炎症性关节炎（如类风湿关节炎）中，定期监测患者以发现疾病发作可改善预后。在一项应用机器学习分析活动追踪器步数的试点研究中，我们表明身体活动与疾病发作密切相关，身体活动模式可以非常准确地预测发作，灵敏度和特异性均高于 95%。因此，通过潜在地早期识别疾病发作，自动监测步数可能会导致疾病控制得到改善。然而，活动追踪器在应用于风湿患者时存在一些局限性，例如追踪器的依从性、长期有效性不明确或追踪器的潜在多样性。
