@Article{信息:doi 10.2196 / /移动医疗。7871，作者=“Rahman, Quazi Abidur和Janmohamed, Tahir和Pirbaglou, Meysam和Ritvo, Paul和Heffernan, Jane M和Clarke, Hance和Katz, Joel”，标题=“用户参与移动应用程序的模式，管理我的疼痛:数据挖掘调查的结果”，期刊=“JMIR移动健康Uhealth”，年=“2017”，月=“7”，日=“12”，卷=“5”，数=“7”，页=“e96”，关键词=“慢性疼痛;移动健康;阿片类药物使用;数据挖掘;聚类分析;管理我的痛苦;疼痛管理;背景:疼痛是最普遍的健康相关问题之一，也是寻求医疗帮助的三大最常见原因之一。从疼痛跟踪和监测应用程序中收集的数据的科学出版物对于帮助消费者和医疗保健专业人员选择适合他们使用的应用程序非常重要。 Objective: The main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. Methods: User engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). Results: The clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P <.001). Overall, males were significantly older than females (P <.001). Across clusters, females reported more pain conditions than males (all P <.001). Users from highly engaged clusters reported taking more medication than less engaged users (all P <.001). Females reported taking a greater number of medications than males (P =.04). In 4 of 5 clusters, the percentage of males taking an opioid was significantly greater (all P ≤.05) than that of females. The proportion of males with mild pain was significantly higher than that of females in 3 clusters (all P ≤.008). Conclusions: Although most users of the app reported being female, male users were more likely to be highly engaged in the app. Users in the most engaged clusters self-reported a higher number of pain conditions, a higher number of current medications, and a higher incidence of opioid usage. The high engagement by males in these clusters does not appear to be driven by pain severity which may, in part, be the case for females. Use of a mobile pain app may be relatively more attractive to highly-engaged males than highly-engaged females, and to those with relatively more complex chronic pain problems. ", issn="2291-5222", doi="10.2196/mhealth.7871", url="http://mhealth.www.mybigtv.com/2017/7/e96/", url="https://doi.org/10.2196/mhealth.7871", url="http://www.ncbi.nlm.nih.gov/pubmed/28701291" }