Penn Medicine Nudge Unit, University of Pennsylvania, Philadelphia, PA, USA.
Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Sci Rep. 2021 Nov 2;11(1):21501. doi: 10.1038/s41598-021-01021-y.
Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.
智能手机和可穿戴设备可用于远程监测健康行为，但对于个体特征如何影响这些设备的持续使用，人们知之甚少。本研究利用基线活动水平以及人口统计学、行为和心理社会特征的数据，采用潜在类别分析，在参与者中识别出行为表型，这些参与者在出院后 6 个月内使用智能手机或可穿戴设备来跟踪身体活动。确定了 4 种表型：（1）更友善和尽责；（2）更活跃、社交和积极；（3）更冒险和支持度低；（4）不活跃、不社交和冒险。我们发现，可穿戴设备的使用时长和一致性因表型而异，但智能手机则不然。此外，表型 3 和 4 的“高危”人群更有可能停止使用可穿戴设备，而表型 1 和 2 的活动监测则与设备类型无关。这些发现可以帮助更好地针对住院患者的远程监测干预措施。
