Assessing User Retention of a Mobile App: Survival Analysis

Telehealth Use and Health Equity for Mental Health and Substance Use Disorder During the COVID-19 Pandemic: A Systematic Review

Background: As a result of the COVID-19 public health emergency (PHE), telehealth utilization accelerated to facilitate health care management and minimize risk. However, those with mental health conditions and substance use disorders (SUD)-who represent a vulnerable population, and members of underrepresented minorities (e.g., rural, racial/ethnic minorities, the elderly)-may not benefit from telehealth equally. Objective: To evaluate health equality in clinical effectiveness and utilization measures associated with telehealth for clinical management of mental health disorders and SUD to identify emerging patterns for underrepresented groups stratified by race/ethnicity, gender, age, rural status, insurance, sexual minorities, and social vulnerability. Methods: We performed a systematic review in PubMed, Embase, Cochrane Central Register of Controlled Trials, and CINAHL through November 2022. Studies included those with telehealth, COVID-19, health equity, and mental health or SUD treatment/care concepts. Our outcomes included general clinical measures, mental health or SUD clinical measures, and operational measures. Results: Of the 2,740 studies screened, 25 met eligibility criteria. The majority of studies (n = 20) evaluated telehealth for mental health conditions, while the remaining five studies evaluated telehealth for opioid use disorder/dependence. The most common study outcomes were utilization measures (n = 19) or demographic predictors of telehealth utilization (n = 3). Groups that consistently demonstrated less telehealth utilization during the PHE included rural residents, older populations, and Black/African American minorities. Conclusions: We observed evidence of inequities in telehealth utilization among several underrepresented groups. Future efforts should focus on measuring the contribution of utilization disparities on outcomes and strategies to mitigate disparities in implementation.

The BLV App Arcade: a new curated repository and evaluation rubric for mobile applications supporting blindness and low vision

PURPOSE

Visual impairment-related disabilities have become increasingly pervasive. Current reports estimate a total of 36 million persons with blindness and 217 million persons with moderate to severe visual impairment worldwide. Assistive technologies (AT), including text-to-speech software, navigational/spatial guides, and object recognition tools have the capacity to improve the lives of people with blindness and low vision. However, access to such AT is constrained by high costs and implementation barriers. More recently, expansive growth in mobile computing has enabled many technologies to be translated into mobile applications. As a result, a marketplace of accessibility apps has become available, yet no framework exists to facilitate navigation of this voluminous space.

MATERIALS AND METHODS

We developed the BLV (Blind and Low Vision) App Arcade: a fun, engaging, and searchable curated repository of app AT broken down into 11 categories spanning a wide variety of themes from entertainment to navigation. Additionally, a standardized evaluation metric was formalized to assess each app in five key dimensions: reputability, privacy, data sharing, effectiveness, and ease of use/accessibility. In this paper, we describe the methodological approaches, considerations, and metrics used to find, store and score mobile applications.

CONCLUSION

The development of a comprehensive and standardized database of apps with a scoring rubric has the potential to increase access to reputable tools for the visually impaired community, especially for those in low- and middle-income demographics, who may have access to mobile devices but otherwise have limited access to more expensive technologies or services.

Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study

BACKGROUND

Traditional methods for investigating work hours rely on an employee's physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work.

OBJECTIVE

In this study, we aimed to develop a novel approach called "probability in work mode," which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours.

METHODS

To capture human-smartphone interactions and GPS locations, we used the "Staff Hours" app, developed by our team, to passively and continuously record participants' screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work.

RESULTS

Our study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P<.001). This discrepancy was attributed to the higher remote work time of 111.6 (SD 106.4) minutes compared to the break time of 54.7 (SD 74.5) minutes.

CONCLUSIONS

Our novel approach, the probability in work mode, harnessed human-smartphone interaction patterns and machine learning models to enhance the precision and accuracy of work hour investigation. By integrating human-smartphone interactions and GPS data, our method provides valuable insights into work patterns, including remote work and breaks, offering potential applications in optimizing work productivity and well-being.

Dispel the Clouds and See the Sun: Influencing Factors and Multiple Paths of User Retention Intention Formation

To achieve user retention through multifactor synergy, Internet enterprises must reduce costs and increase efficiency and sustainable development. In response to the dilemma that Internet companies are experiencing increasingly high user acquisition costs and serious user churn, this paper investigates a sample of 46,695 user reviews of nine product series from Xiaomi Ecological Chain. Case studies and qualitative comparative analysis are used to explore the influence mechanisms of quality of experience, brand trust, and brand attachment on users' retention intentions. Our findings are as follows. (1) Brand attachment alone is not necessary for high user retention intention, but user perception, cognition, and brand trust are necessary. (2) Quality of experience positively impacts brand trust, attachment, and user retention intention. Therefore, improving user perception and cognition is critical in generating high user retention intention. (3) Five configuration paths can achieve high user retention intention, while three configuration paths lead to low user retention intention, and there is an asymmetric relationship between these paths. Among them, the role of quality of experience-driven configuration paths in generating user retention intention is the most prominent. (4) User perception and cognition can substitute with brand trust and attachment in the substitution relationship between configuration paths. Our findings have important theoretical and practical implications for revealing the realization paths of high user retention intention in Internet companies and provide a new perspective for future research.

Activity and Profile of the Users of a Novel Mobile Application Supporting Proper Diet Among Maintenance Hemodialysis Patients

OBJECTIVE

Diet plays a key role in the management of chronic kidney disease. The aim of our study was to evaluate the usage of a self-developed mobile application supporting proper dietary choices among maintenance hemodialysis (HD) patients.

METHODS

The primary functions of the application are to provide databases of products and recipes. Data on user activity recorded using Internet solutions were collected for 12 months from April 2021. The application was promoted both via the Internet and directly to patients. Additionally, a questionnaire was employed to evaluate the usage of the software.

RESULTS

The application was downloaded by 841 smartphone users, 44.4% of whom were from 2 regions of Poland with the largest populations of HD patients. Residents of cities with a population above 250,000 accounted for 86.0% of users. Sixty HD patients (32 males, 28 females; age 56.2 ± 14.8 years) filled the questionnaire. All features of the application scored a median of 4.0 points or higher on a 5-point Likert scale; however, 63.3% of respondents indicated the need to improve particular functions of the application. There was a significant difference in dialysis vintage between respondents who used the application for less than 1 month and others (1.0 vs. 3.3 years; P = .02). The positive perception of its influence on diet adherence was significantly higher among younger (<50 years) compared to older users (5.0 vs. 4.0; P = .03) and among women compared to men (5.0 vs. 4.0; P = .01).

CONCLUSION

HD patients showed interest in dietary mobile applications, and Internet channels were effective in promoting the software. Place of residency, age, gender, and dialysis vintage are factors that influence patient satisfaction with and the time of using the mobile application.

Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis

The development of precision psychiatry is largely based on multi-module measurements from the molecular, cellular, and behavioral levels, which are integrated to assess neurocognitive performances and clinically observed psychopathology. Nevertheless, quantifying mental activities and functions accurately and continuously has been a major difficulty within this field. This article reviews the latest efforts that utilize mobile apps to collect human–smartphone interaction data and contribute towards digital biomarkers of mental illnesses. The fundamental principles underlying a behavioral analysis with mobile apps were introduced, such as ways to monitor smartphone use under different circumstances and construct long-term patterns and trend changes. Examples were also provided to illustrate the potential applications of mobile apps that gain further insights into traditional research topics in occupational health and sleep medicine. We suggest that, with an optimized study design and analytical approach that accounts for technical challenges and ethical considerations, mobile apps will enhance the systemic understanding of mental illnesses.

Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis

Background

Osteoporosis is the fourth most common chronic disease worldwide. The adoption of preventative measures and effective self-management interventions can help improve bone health. Mobile health (mHealth) technologies can play a key role in the care and self-management of patients with osteoporosis.

Objective

This study presents a systematic review and meta-analysis of the currently available mHealth apps targeting osteoporosis self-management, aiming to determine the current status, gaps, and challenges that future research could address, as well as propose appropriate recommendations.

Methods

A systematic review of all English articles was conducted, in addition to a survey of all apps available in iOS and Android app stores as of May 2021. A comprehensive literature search (2010 to May 2021) of PubMed, Scopus, EBSCO, Web of Science, and IEEE Xplore was conducted. Articles were included if they described apps dedicated to or useful for osteoporosis (targeting self-management, nutrition, physical activity, and risk assessment) delivered on smartphone devices for adults aged ≥18 years. Of the 32 articles, a random effects meta-analysis was performed on 13 (41%) studies of randomized controlled trials, whereas the 19 (59%) remaining studies were only included in the narrative synthesis as they did not provide enough data.

Results

In total, 3906 unique articles were identified. Of these 3906 articles, 32 (0.81%) articles met the inclusion criteria and were reviewed in depth. The 32 studies comprised 14,235 participants, of whom, on average, 69.5% (n=9893) were female, with a mean age of 49.8 (SD 17.8) years. The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth-supported interventions resulted in a significant reduction in pain (Hedges g −1.09, 95% CI −1.68 to −0.45) and disability (Hedges g −0.77, 95% CI −1.59 to 0.05). The posttreatment effect of the digital intervention was significant for physical function (Hedges g 2.54, 95% CI −4.08 to 4.08) but nonsignificant for well-being (Hedges g 0.17, 95% CI −1.84 to 2.17), physical activity (Hedges g 0.09, 95% CI −0.59 to 0.50), anxiety (Hedges g −0.29, 95% CI −6.11 to 5.53), fatigue (Hedges g −0.34, 95% CI −5.84 to 5.16), calcium (Hedges g −0.05, 95% CI −0.59 to 0.50), vitamin D intake (Hedges g 0.10, 95% CI −4.05 to 4.26), and trabecular score (Hedges g 0.06, 95% CI −1.00 to 1.12).

Conclusions

Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be valuable tools for patients and health professionals. However, most of the apps that are currently available lack clinically validated evidence of their efficacy and focus on a limited number of symptoms. A more holistic and personalized approach within a cocreation design ecosystem is needed.

Trial Registration

PROSPERO 2021 CRD42021269399; https://tinyurl.com/2sw454a9

Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores

Background

Mobile health (mHealth) platforms show promise in the management of mental health conditions such as anxiety and depression. This has resulted in an abundance of mHealth platforms available for research or commercial use.

Objective

The objective of this review is to characterize the current state of mHealth platforms designed for anxiety or depression that are available for research, commercial use, or both.

Methods

A systematic review was conducted using a two-pronged approach: searching relevant literature with prespecified search terms to identify platforms in published research and simultaneously searching 2 major app stores—Google Play Store and Apple App Store—to identify commercially available platforms. Key characteristics of the mHealth platforms were synthesized, such as platform name, targeted condition, targeted group, purpose, technology type, intervention type, commercial availability, and regulatory information.

Results

The literature and app store searches yielded 169 and 179 mHealth platforms, respectively. Most platforms developed for research purposes were designed for depression (116/169, 68.6%), whereas the app store search reported a higher number of platforms developed for anxiety (Android: 58/179, 32.4%; iOS: 27/179, 15.1%). The most common purpose of platforms in both searches was treatment (literature search: 122/169, 72.2%; app store search: 129/179, 72.1%). With regard to the types of intervention, cognitive behavioral therapy and referral to care or counseling emerged as the most popular options offered by the platforms identified in the literature and app store searches, respectively. Most platforms from both searches did not have a specific target age group. In addition, most platforms found in app stores lacked clinical and real-world evidence, and a small number of platforms found in the published research were available commercially.

Conclusions

A considerable number of mHealth platforms designed for anxiety or depression are available for research, commercial use, or both. The characteristics of these mHealth platforms greatly vary. Future efforts should focus on assessing the quality—utility, safety, and effectiveness—of the existing platforms and providing developers, from both commercial and research sectors, a reporting guideline for their platform description and a regulatory framework to facilitate the development, validation, and deployment of effective mHealth platforms.

Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study

BACKGROUND

SIMPLe is an internet-delivered self-management mobile app for bipolar disorder (BD) designed to combine technology with evidence-based interventions and facilitate access to psychoeducational content. The SIMPLe app was launched to the real world to make it available worldwide within the context of BD treatment.

OBJECTIVE

The main aims of this study are as follows: to describe app use, engagement, and retention rates based on server data; to identify patterns of user retention over the first 6-month follow-up of use; and to explore potential factors contributing to discontinuation of app use.

METHODS

This was an observational ecological study in which we pooled available data from a real-world implementation of the SIMPLe app. Participation was open on the project website, and the data-collection sources were a web-based questionnaire on clinical data and treatment history administered at inclusion and at 6 months, subjective data gathered through continuous app use, and the use patterns captured by the app server. Characteristics and engagement of regular users, occasional users, and no users were compared using 2-tailed t tests or analysis of variance or their nonparametric equivalent. Survival analysis and risk functions were applied to regular users' data to examine and compare use and user retention. In addition, a user evaluation analysis was performed based on satisfaction, perceived usefulness, and reasons to discontinue app use.

RESULTS

We included 503 participants with data collected between 2016 and 2018, of whom 77.5% (n=390) used the app. Among the app users, 44.4% (173/390) completed the follow-up assessment, and data from these participants were used in our analyses. Engagement declined gradually over the first 6 months of use. The probability of retention of the regular users after 1 month of app use was 67.4% (263/390; 95% CI 62.7%-72.4%). Age (P=.002), time passed since illness onset (P<.001), and years since diagnosis of BD (P=.048) correlate with retention duration. In addition, participants who had been diagnosed with BD for longer used the app on more days (mean 97.73, SD 69.15 days; P=.002) than those who had had a more recent onset (mean 66.49, SD 66.18 days; P=.002) or those who had been diagnosed more recently (mean 73.45, SD 66 days; P=.01).

CONCLUSIONS

The user retention rate of the app decreased rapidly after each month until reaching only one-third of the users at 6 months. There exists a strong association between age and app engagement of individuals with BD. Other variables such as years lived with BD, diagnosis of an anxiety disorder, and taking antipsychotics seem relevant as well. Understanding these associations can help in the definition of the most suitable user profiles for predicting trends of engagement, optimization of app prescription, and management.

Investigation of the Role of Anxiety and Depression on the Formation of Phantom Vibration and Ringing Syndrome Caused by Working Stress during Medical Internship

Phantom vibration syndrome (PVS) and phantom ringing syndrome (PRS) are prevalent hallucinations during medical internship. Depression and anxiety are probably understudied risk factors of PVS and PRS. The aim was to evaluate the role of anxiety and depression on the relationship between working stress during medical internship and PVS and PRS. A prospective longitudinal study, consisted of 74 medical interns, was carried out. The severity of phantom vibrations and ringing, as well as anxiety and depression as measured before, at the third, sixth, and 12th month during internship, and two weeks after internship. We conducted a causal mediation analysis to quantify the role of depression and in the mechanism of working stress during medical internship inducing PVS and PRS. The results showed that depression explained 21.9% and 8.4% for stress-induced PRS and PVS, respectively. In addition, anxiety explained 15.0% and 7.8% for stress-induced PRS and PVS, respectively. Our findings showed both depression and anxiety can explain a portion of stress-induced PVS and PRS during medical internship and might be more important in clinical practice and benefit to prevention of work-related burnout.