Characteristics of Adopters of an Online Social Networking Physical Activity Mobile Phone App: Cluster Analysis

Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining

This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.

Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data

Background

Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management is effective, possibly because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear.

Objective

The purpose of this study was to (1) explore patient multibehavior engagement trails in the pathway-based hypertension self-management, (2) discover patient behavior preference patterns, and (3) identify the characteristics of patients with different behavior preferences.

Methods

This study included 863 hypertensive patients who generated 295,855 use records in the mHealth app from December 28, 2016, to July 2, 2020. Markov chain was used to infer the patient multibehavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability value (TP value) of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analyses of variance, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns.

Results

Markov chain analysis revealed 3 types of behavior transition (1-way transition, cycle transition, and self-transition) and 4 trails of patient multibehavior engagement. In perform task trail (PT-T), patients preferred to start self-management from the states of task blood pressure (BP), task drug, and task weight (TP value 0.29, 0.18, and 0.20, respectively), and spent more time on the task food state (35.87 s). Some patients entered the states of task BP and task drug (TP value 0.20, 0.25) from the reminder item state. In the result-oriented trail (RO-T), patients spent more energy on the ranking state (19.66 s) compared to the health report state (13.25 s). In the knowledge learning trail (KL-T), there was a high probability of cycle transition (TP value 0.47, 0.31) between the states of knowledge list and knowledge content. In the support acquisition trail (SA-T), there was a high probability of self-transition in the questionnaire (TP value 0.29) state. Cluster analysis discovered 3 patient behavior preference patterns: PT-T cluster, PT-T and KL-T cluster, and PT-T and SA-T cluster. There were statistically significant associations between the behavior preference pattern and gender, education level, and BP.

Conclusions

This study identified the dynamic, longitudinal, and multidimensional characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions and paid attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poorly controlled BP were more likely to be highly involved in hypertension health education.

Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis

Background

Hypertension is a long-term medical condition. Electronic and mobile health care services can help patients to self-manage this condition. However, not all management is effective, possibly due to different levels of patient engagement (PE) with health care services. Health care provider follow-up is an intervention to promote PE and blood pressure (BP) control.

Objective

This study aimed to discover and characterize patterns of PE with a hypertension self-management app, investigate the effects of health care provider follow-up on PE, and identify the follow-up effects on BP in each PE pattern.

Methods

PE was represented as the number of days that a patient recorded self-measured BP per week. The study period was the first 4 weeks for a patient to engage in the hypertension management service. K-means algorithm was used to group patients by PE. There was compliance follow-up, regular follow-up, and abnormal follow-up in management. The follow-up effect was calculated by the change in PE (CPE) and the change in systolic blood pressure (CSBP, SBP) before and after each follow-up. Chi-square tests and z scores were used to ascertain the distribution of gender, age, education level, SBP, and the number of follow-ups in each cluster. The follow-up effect was identified by analysis of variances. Once a significant effect was detected, Bonferroni multiple comparisons were further conducted to identify the difference between 2 clusters.

Results

Patients were grouped into 4 clusters according to PE: (1) PE started low and dropped even lower (PELL), (2) PE started high and remained high (PEHH), (3) PE started high and dropped to low (PEHL), and (4) PE started low and rose to high (PELH). Significantly more patients over 60 years old were found in the PEHH cluster (P≤.05). Abnormal follow-up was significantly less frequent (P≤.05) in the PELL cluster. Compliance follow-up and regular follow-up can improve PE. In the clusters of PEHH and PELH, the improvement in PE in the first 3 weeks and the decrease in SBP in all 4 weeks were significant after follow-up. The SBP of the clusters of PELL and PELH decreased more (–6.1 mmHg and –8.4 mmHg) after follow-up in the first week.

Conclusions

Four distinct PE patterns were identified for patients engaging in the hypertension self-management app. Patients aged over 60 years had higher PE in terms of recording self-measured BP using the app. Once SBP reduced, patients with low PE tended to stop using the app, and a continued decline in PE occurred simultaneously with the increase in SBP. The duration and depth of the effect of health care provider follow-up were more significant in patients with high or increased engagement after follow-up.

Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING)

BACKGROUND

In health care, the use of game-based interventions to increase motivation, engagement, and overall sustainability of health behaviors is steadily becoming more common. The most prevalent types of game-based interventions in health care research are gamification and serious games. Various researchers have discussed substantial conceptual differences between these 2 concepts, supported by empirical studies showing differences in the effects on specific health behaviors. However, researchers also frequently report cases in which terms related to these 2 concepts are used ambiguously or even interchangeably. It remains unclear to what extent existing health care research explicitly distinguishes between gamification and serious games and whether it draws on existing conceptual considerations to do so.

OBJECTIVE

This study aims to address this lack of knowledge by capturing the current state of conceptualizations of gamification and serious games in health care research. Furthermore, we aim to provide tools for researchers to disambiguate the reporting of game-based interventions.

METHODS

We used a 2-step research approach. First, we conducted a systematic literature review of 206 studies, published in the Journal of Medical Internet Research and its sister journals, containing terms related to gamification, serious games, or both. We analyzed their conceptualizations of gamification and serious games, as well as the distinctions between the two concepts. Second, based on the literature review findings, we developed a set of guidelines for researchers reporting on game-based interventions and evaluated them with a group of 9 experts from the field.

RESULTS

Our results show that less than half of the concept mentions are accompanied by an explicit definition. To distinguish between the 2 concepts, we identified four common approaches: implicit distinction, synonymous use of terms, serious games as a type of gamified system, and distinction based on the full game dimension. Our Game-Based Intervention Reporting Guidelines (GAMING) consist of 25 items grouped into four topics: conceptual focus, contribution, mindfulness about related concepts, and individual concept definitions.

CONCLUSIONS

Conceptualizations of gamification and serious games in health care literature are strongly heterogeneous, leading to conceptual ambiguity. Following the GAMING can support authors in rigorous reporting on study results of game-based interventions.

Implementation: The Next Giant Hurdle to Clinical Transformation With Digital Health

Clinical implementation of digital health is a major hurdle to overcome in the coming years. Considering the role of the Journal of Medical Internet Research in the past 20 years and looking toward the journal’s future, this viewpoint acknowledges the vision of medicine and the role that digital health plays in that vision. It also highlights barriers to implementation of digital health as an obstacle to achieving that vision. In particular, this paper focuses on how digital health research must start looking toward implementation as an area of inquiry and the role that the Journal of Medical Internet Research and its' sister journals from JMIR Publications can play in this process.