Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Health Promotion and Behavioral Science, University of Texas School of Public Health, Houston, TX 77030, USA.
Transl Behav Med. 2021 Aug 13;11(8):1537-1547. doi: 10.1093/tbm/ibab015.
Mobile applications and paired devices allow individuals to self-monitor physical activity, dietary intake, and weight fluctuation concurrently. However, little is known regarding patterns of use of these self-monitoring technologies over time and their implications for weight loss. The objectives of this study were to identify distinct patterns of self-monitoring technology use and to investigate the associations between these patterns and weight change. We analyzed data from a 6-month weight loss intervention for school district employees with overweight or obesity (N = 225). We performed repeated measures latent profile analysis (RMLPA) to identify common patterns of self-monitoring technology use and used multiple linear regression to evaluate the relationship between self-monitoring technology use and weight change. RMLPA revealed four distinct profiles: minimal users (n = 65, 29% of sample), activity trackers (n = 124, 55%), dedicated all-around users (n = 25, 11%), and dedicated all-around users with exceptional food logging (n = 11, 5%). The dedicated all-around users with exceptional food logging lost the most weight (X2[1,225] = 5.27, p = .0217). Multiple linear regression revealed that, adjusting for covariates, only percentage of days of wireless weight scale use (B = -0.05, t(212) = -3.79, p < .001) was independently associated with weight loss. We identified distinct patterns in mHealth self-monitoring technology use for tracking weight loss behaviors. Self-monitoring of weight was most consistently linked to weight loss, while exceptional food logging characterized the group with the greatest weight loss. Weight loss interventions should promote self-monitoring of weight and consider encouraging food logging to individuals who have demonstrated consistent use of self-monitoring technologies.
移动应用程序和配套设备允许个人同时自我监测身体活动、饮食摄入和体重波动。然而，对于这些自我监测技术随时间的使用模式及其对减肥的影响知之甚少。本研究的目的是确定自我监测技术使用的不同模式，并研究这些模式与体重变化之间的关系。我们分析了一项针对超重或肥胖的学区员工的为期 6 个月的减肥干预研究的数据（N = 225）。我们进行了重复测量潜在剖面分析（RMLPA），以确定自我监测技术使用的常见模式，并使用多元线性回归来评估自我监测技术使用与体重变化之间的关系。RMLPA 揭示了四个不同的模式：最少使用者（n = 65，占样本的 29%）、活动追踪器（n = 124，占 55%）、专用全方位使用者（n = 25，占 11%）和专用全方位使用者，具有出色的食物记录（n = 11，占 5%）。具有出色食物记录的专用全方位使用者体重减轻最多（X2[1,225] = 5.27，p =.0217）。多元线性回归显示，调整协变量后，只有无线体重秤使用天数的百分比（B = -0.05，t(212) = -3.79，p <.001）与体重减轻独立相关。我们确定了用于跟踪减肥行为的移动健康自我监测技术使用的不同模式。自我监测体重与体重减轻最密切相关，而出色的食物记录则是体重减轻最大的人群的特征。减肥干预措施应促进体重自我监测，并考虑鼓励有自我监测技术使用记录的个体进行食物记录。
