Beyond clicks and downloads: a call for a more comprehensive approach to measuring mobile-health app engagement.
Ontology highlight
ABSTRACT:
SUBMITTER: O'Brien HL
PROVIDER: S-EPMC7453800 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Beyond clicks and downloads: a call for a more comprehensive approach to measuring mobile-health app engagement.
O'Brien Heather L HL Morton Emma E Kampen Andrea A Barnes Steven J SJ Michalak Erin E EE
BJPsych open 20200811 5
Downloading a mobile health (m-health) app on your smartphone does not mean you will ever use it. Telling another person about an app does not mean you like it. Using an online intervention does not mean it has had an impact on your well-being. Yet we consistently rely on downloads, clicks, 'likes' and other usage and popularity metrics to measure m-health app engagement. Doing so misses the complexity of how people perceive and use m-health apps in everyday life to manage mental health conditio ...[more]
Similar Datasets
Project description:How and why customers engage with mobile travel apps is vital to mobile marketing of travel-related companies. This paper discusses the antecedents and consequences of mobile travel app engagement. Specifically, this study aims to understand how travel app attributes stimulate mobile travel app engagement and lead to purchase intention. A research model is established based on the Stimulus-Organism-Response (S-O-R) model and the model is tested by Partial Least Squares Path Modeling (PLS-PM). The results show that ease of use, compatibility, and UI attractiveness positively influence mobile travel app engagement, and in turn, affect purchase intention. Furthermore, a multi-group analysis shows that the attributes affecting mobile travel app engagement differ across different customer groups. This paper discusses some theoretical and practical implications.
| S-EPMC7954346 | biostudies-literature
Project description:Software development is an iterative process from designing to implementation, and to testing, in which product development staff should be closely integrated with users. Satisfying user needs effectively is often the pain point for developers. In order to alleviate this, this paper manages to establish the quantitative connection between users' online reviews and APP (Application Program) downloads. By analyzing user online comments, companies can dig out user needs and preferences. This could benefit them by making accurate market positioning of their APP products, and therefore iteratively innovating products based on user needs, which hopefully will increase the volume of APP downloads. This paper regards WeChat APP during 47 updates periods as the research object. Based on Grounded Theory, user needs are extracted after data cleaning. Next, by using semantic analysis and word frequency analysis, we are able to obtain the implicit feedbacks such as emotion tendency, satisfaction and requirements lie under online reviews. Then, we construct a quantile regression model to study the impact of users' online reviews on downloads based on the influencing factors we extracted so as to provide a decision basis for enterprises to iteratively update their products. Results show that: (1) Generally speaking, needs of WeChat users mainly focus on performance, reliability, usability, functional deficiency, functional insufficiency, and system adaptability; (2) For those APP versions with relatively fewer downloads, user needs are mostly about functional deficiency, followed by functional insufficiency, performance, usability, and system adaptability. At this stage, it is found out that users' emotion tendency and user satisfaction significantly affect the volume of downloads; (3) When the volume of APP downloads is moderate, the user needs are functional deficiency, functional insufficiency, and system adaptability. While under this circumstances, users' star ratings have a significant impact on downloads; (4) In addition, when the volume of App downloads is high, user needs are performance, usability, and system adaptability. Our methods effectively extract users' requirements from online reviews and then successfully build up the quantitative connection between the implicit feedbacks from those requirements and APP downloads.
| S-EPMC9237435 | biostudies-literature
Project description:BackgroundThree recent reviews evaluated 19 studies testing the hemoglobin A1c (HbA1c) benefit of 16 diabetes apps, including 5 publicly available apps. Most studies relied on small samples and did not link app engagement with outcomes.ObjectiveThis study assessed both HbA1c change in a large sample of people using the One Drop | Mobile app and associations between app engagement and changes in HbA1c.MethodsThe One Drop | Mobile app for iOS and Android is designed to manually and passively (via Apple HealthKit, Google Fit, and the One Drop | Chrome blood glucose meter) store, track, and share data. Users can schedule medication reminders, view statistics, set goals, track health outcomes, and get data-driven insights. In June 2017, we queried data on people with diabetes using the app who had entered at least 2 HbA1c values in the app >60 and ≤365 days apart. Multiple imputation corrected for missing data. Unadjusted and adjusted mixed effects repeated measures models tested mean HbA1c change by time, diabetes type, and their interaction. Multiple regression models assessed relationships between using the app to track food, activity, blood glucose, and medications and HbA1c change.ResultsThe sample (N=1288) included people with type 1 diabetes (T1D) (n=367) or type 2 diabetes (T2D) (n=921) who were 35% female, diagnosed with diabetes for a mean 9.4 (SD 9.9) years, and tracked an average 1646.1 (SD 3621.9) self-care activities in One Drop | Mobile between their first (mean 8.14% [SD 2.06%]) and second HbA1c entry (mean 6.98% [SD 1.1%]). HbA1c values were significantly associated with user-entered average blood glucose 90 days before the second HbA1c entry (rho=.73 to .75, P<.001). HbA1c decreased by an absolute 1.07% (unadjusted and adjusted F=292.03, P<.001) from first to second HbA1c entry. There was a significant interaction between diabetes type and HbA1c. Both groups significantly improved, but users with T2D had a greater HbA1c decrease over time than users with T1D (F=10.54, P<.001). For users with T2D (n=921), HbA1c decreased by an absolute 1.27% (F=364.50, P<.001) from first to second HbA1c entry. Finally, using One Drop | Mobile to record food was associated with greater HbA1c reductions even after adjusting for covariates and after also adjusting for insulin use for users with T2D (all P<.05).ConclusionsPeople with T1D and T2D reported a 1.07% to 1.27% absolute reduction in HbA1c during a median 4 months of using the One Drop | Mobile app. Using the app to track self-care was associated with improved HbA1c. More research is needed on the health benefits of publicly available diabetes apps, particularly studies associating app engagement with short- and long-term effects.
| S-EPMC6238886 | biostudies-literature
Project description:Mobile phone applications ("apps") are potentially an effective, low-burden method to collect patient-reported outcomes outside the clinical setting. Using such apps consistently and in a timely way is critical for complete and accurate data capture, but no studies of concurrent reporting by cancer patient-caregiver dyads have been published in the peer-reviewed literature. This study assessed app engagement, defined as adherence, timing, and attrition with two smartphone applications, one for adult cancer patients and one for their informal caregivers. This was a single-arm, pilot study in which adult cancer patients undergoing IV chemotherapy or immunotherapy used the DigiBioMarC app, and their caregivers used the TOGETHERCare app, for approximately one month to report weekly on the patients' symptoms and wellbeing. Using app timestamp metadata, we assessed user adherence, overall and by participant characteristics. Fifty patient-caregiver dyads completed the study. Within the one-month study period, both adult cancer patients and their informal caregivers were highly adherent, with app activity completion at 86% for cancer patients and 84% for caregivers. Caregivers completed 86% of symptom reports, while cancer patients completed 89% of symptom reports. Cancer patients and their caregivers completed most activities within 48 h of availability on the app. These results suggest that the DigiBioMarC and TOGETHERCare apps can be used to collect patient- and caregiver-reported outcomes data during intensive treatment. From our research, we conclude that metadata from mobile apps can be used to inform clinical teams about study participants' engagement and wellbeing outside the clinical setting.
| S-EPMC10858186 | biostudies-literature
Project description:BACKGROUND:Diabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6% of the general population, and the prevalence of T2DM is predicted to rise to 45.36% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives. OBJECTIVE:The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps. METHODS:In this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care. RESULTS:In total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275; P<.001). The top 5 features included insulin dose adjustment according to carbohydrate counting and blood glucose readings (5.36), alerting a caregiver of abnormal or critical readings (6.09), nutrition education (12.45), contacts for guidance if required (12.64), and offering patient-specific education tailored to the user's goals, needs, and blood glucose readings (12.90). In total, 21 engagement strategies were generated. Overall, the experts showed a moderate degree of consensus in their strategy rankings (Kendall W=0.454; P<.001). The top 5 engagement strategies included a user-friendly design (educational and age-appropriate design; 2.82), a free app (3.73), allowing the user to communicate or send information/data to a health care provider (HCP; 5.36), HCPs prescribing the mobile app in the clinic and asking about patients' app use compliance during clinical visits (6.91), and flexibility and customization (7.91). CONCLUSIONS:This is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts' opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic.
| S-EPMC7519429 | biostudies-literature
Project description: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.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.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).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).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.
| S-EPMC5529741 | biostudies-other
Project description:IntroductionDigital contact-tracing smartphone apps have the potential to slow the spread of disease but are not widely used. We tested whether messages describing how a COVID-19 digital contact-tracing app protects users' privacy led to increased or decreased intentions to download the app by either calming privacy concerns or increasing their saliency.DesignRandomized controlled trial.Setting/participantsWe recruited adult smartphone owners in the U.S. (oversampled for younger adults aged 18-34 years) in November 2020 through an online panel.InterventionSurvey software randomly assigned 860 participants to 1 of 2 parallel messaging conditions (n=430 privacy assured, n=430 no privacy described).Main outcome measures4-point scale of intention to use the app "if public health officials released a COVID Exposure Notification app in their state" that averaged likelihood to (1) download and install the app on their phone; (2) keep the app active on their phone; and (3) keep Bluetooth active on their phone (needed for the app to work).ResultsAfter removing incompletes, those who failed the manipulation checks, or those who had already downloaded a COVID-19 digital contact-tracing app, we analyzed 671 participants (n=330 privacy, n=341 no privacy) in 2021. There was no relationship between privacy condition and download intention (meanprivacy=2.69, meannoprivacy=2.69, b=0.01, 95% CI= -0.13, 0.15, p=0.922) but also no evidence that describing the app's security increased context-dependent privacy concerns (measured 3 ways). Instead, we found increased endorsement of data security in the privacy condition using a scale of beliefs about the app keeping privacy secure (meanprivacy=2.74, meannoprivacy=2.58, b=0.16, 95% CI=0.04, 0.28, p=0.009, small effect ω2=0.009).ConclusionsThis study provides some evidence that people developing contact-tracing messaging campaigns do not need to worry that describing a digital contact-tracing app's privacy protections will backfire. Future mixed methods testing of messages about who has access to information-and for how long-may uncover new communication strategies to increase public trust in contact-tracing apps.Trial registrationThis study is registered with AsPredicted#51826.
| S-EPMC9771837 | biostudies-literature
Project description:Scholars from different theoretical schools have posited that in recent decades, science and society have moved closer together, and the concept of academic engagement has been proposed to capture one part of this approximation empirically. This study analyzes the academic engagement of individual scholars towards politicians, industry representatives and journalists. It uses comprehensive survey data on Swiss professors from all disciplines, all the country's universities and from associated research institutes. It assesses, firstly, the degree to which these professors have professional contacts to journalists, politicians and industry representatives. Secondly, it explains the extent of these contacts, using multi-level modelling that incorporates individual factors as well as organizational and institutional contexts. Our study shows that academic engagement is quite common with strong differences between disciplines. Furthermore, professors with higher academic productivity, positive personal attitude towards communication activities as well as a leadership position have more outside contacts. The gender and nationality of the professors, however, only play a role for some of the contacts with non-scientific actors.
| S-EPMC8139464 | biostudies-literature
Project description:BackgroundThe assessment of health-related quality of life (HRQoL) is important for health outcomes research, disease modeling studies and comparisons of different healthcare interventions. Yet, only a few tools are available to assess HRQoL in 0-1-year-old infants. Furthermore, there is a need for an instrument able to assess HRQoL with a single, standardized, overall score in the first year of life. Here we described the development of the Infant health-related Quality of life Instrument (IQI), a generic, preference-based instrument that can be administered through a mobile application for assessing HRQoL in 0-1-year-old infants.MethodsA multi-step development process began by extracting candidate health concepts from relevant measures identified by two literature searches. Next, three panels, with experts from Asia, Europe, New Zealand and United States of America, and two surveys, with primary caregivers in New Zealand, Singapore, and the United Kingdom, evaluated the relevance of the candidate health concepts, organized them into attributes based on their similarities, explored alternative attributes and generated response scales. Additional interviews assessed the cross-cultural interpretability, parents' understanding of health attributes, and the usability of the mobile application.ResultsThe final list of 7 health attributes included in the IQI consisted of sleeping, feeding, breathing, stooling/poo, mood, skin, and interaction. The users' experiences with the mobile application were generally positive.ConclusionsThe IQI is the first generic, preference-based, instrument designed to assess overall HRQoL in 0-1-year old infants. It is short and easy-to-administer through a mobile application. Moreover, close attention was paid to the opinions of the infants' primary caregivers during the instrument and mobile application development process.
| S-EPMC6118381 | biostudies-literature
Project description:Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients are being used by different research communities. Scientists use these coefficients to test whether two random vectors are linked. Once it has been ascertained that there is such association through testing, then a next step, often ignored, is to explore and uncover the association's underlying patterns. This article provides a survey of various measures of dependence between random vectors and tests of independence and emphasizes the connections and differences between the various approaches. After providing definitions of the coefficients and associated tests, we present the recent improvements that enhance their statistical properties and ease of interpretation. We summarize multi-table approaches and provide scenarii where the indices can provide useful summaries of heterogeneous multi-block data. We illustrate these different strategies on several examples of real data and suggest directions for future research.
| S-EPMC5658146 | biostudies-literature