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首页> 外文期刊>JMIR mHealth and uHealth >Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation
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Patterns of User Engagement With the Mobile App, Manage My Pain: Results of a Data Mining Investigation

机译：移动应用与用户互动的模式，管理我的痛苦：数据挖掘调查的结果

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Background Pain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. 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.
机译：背景疼痛是与健康相关的最普遍的问题之一，也是寻求医疗帮助的三大最常见原因之一。从疼痛跟踪和监视应用程序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合适的应用程序非常重要。目的本文的主要目的是（1）使用数据挖掘方法发现疼痛管理应用程序“ Manage My Pain”的用户参与模式; （2）确定表征个别用户的几个属性与其参与程度之间的关联。方法用户参与度由应用程序的2个关键功能定义：寿命（第一次和最后一次疼痛记录之间的天数）和记录数量。使用k-means聚类算法将用户分为5个用户参与聚类。每个组的特征在于6个属性：性别，年龄，疼痛状况数量，药物数量，疼痛严重程度和阿片类药物的使用。 Z检验和卡方检验用于分析分类属性。性别和聚类对数值属性的影响使用方差的2通分析（ANOVA）进行分析，然后使用Tukey诚实显着性差异（HSD）进行成对比较。结果集群过程产生了5个集群，分别代表了不同级别的用户参与度。在5个集群中的4个集群中，男性和女性的比例显着不同（所有P≤.03）。在寿命相对较高的使用者中，男性的比例高于女性。 2个寿命长的集群的平均年龄高于其他3个集群的用户（所有P <.001）。总体而言，男性明显大于女性（P <.001）。在整个集群中，女性报告的疼痛状况比男性多（所有P <.001）。参与度较高的人群中的用户报告的服药量高于参与度较低的用户（所有P <.001）。女性报告服用的药物数量多于男性（P = .04）。在5组中的4组中，服用阿片类药物的男性比例明显高于女性（所有P≤0.05）。在3个组中，轻度疼痛的男性比例明显高于女性（均P≤.008）。结论尽管该应用程序的大多数用户报告为女性，但男性用户更可能高度参与该应用程序。参与度最高的人群中的用户自我报告的疼痛状况更高，当前使用的药物数量更多，阿片类药物的使用率更高。男性在这些人群中的高度参与似乎并不是由疼痛的严重程度驱动的，而疼痛的严重程度可能是女性的部分原因。对于高度投入的男性而言，使用移动式疼痛应用程序可能比高度投入的女性相对更具吸引力，而对于那些慢性疼痛问题相对较为复杂的人来说，使用移动疼痛应用程序可能更具吸引力。

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