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Mobile health apps have the potential to motivate people to adopt healthier behavior, but many fail to maintain this behavior over time. However, it has been suggested that long-term adherence can be improved by personalizing the proposed interventions. Based on the literature, we created a conceptual framework for selecting appropriate functionalities according to the user's profile.
This cross-sectional study aims to investigate if the relationships linking functionalities and profiles proposed in our conceptual framework are confirmed by user preferences.
A web-based questionnaire comprising several sections was developed to determine the mobile app functionalities most likely to promote healthier behavior. First, participants completed questionnaires to define the user profile (Big Five Inventory-10, Hexad Scale, and perception of the social norm using dimensions of the Theory of Planned Behavior). Second, participants were asked to select the 5 functionalities they considered to be the most relevant to motivate healthier behavior and to evaluate them on a score ranging from 0 to 100. We will perform logistic regressions with the selected functionalities as dependent variables and with the 3 profile scales as predictors to allow us to understand the effect of the participants’ scores on each of the 3 profile scales on the 5 selected functionalities. In addition, we will perform logistic ordinal regressions with the motivation score of the functionalities chosen as dependent variables and with scores of the 3 profile scales as predictors to determine whether the scores on the different profile scales predict the functionality score.
Data collection was conducted between July and December 2021. Analysis of responses began in January 2022, with the publication of results expected by the end of 2022.
This study will allow us to validate our conceptual model by defining the preferred functionalities according to user profiles.
RR1-10.2196/38603
Healthy lifestyle behaviors have increased the life expectancy of those who adopt them and help individuals to live not only longer but better [
Several scales exist to measure the quality of these health-related mobile apps, such as the Mobile App Rating Scale [
Based on a previous literature review, we identified the personality traits more likely to adopt certain app functionalities [
Our model contains 17 functionalities presented in detail in
One of the most popular scales to measure personality is the Big Five, which defines the user’s personality according to the following 5 dimensions: openness, agreeableness, conscientiousness, neuroticism, and extraversion. Game preference was measured with the Hexad Scale model [
Profiles considered in our conceptual framework.
Profiles | Scale |
Personality | Big Five |
Game’s preferences | Hexad Scale [ |
Perception of social norms | Theory of Planned Behavior [ |
This study aims to validate our conceptual framework by investigating if the proposed relationships between the functionalities and profiles are reflected in the preferences of our target population in an experimental setting.
The University Ethics Commission has approved this study for ethical research at the University of Geneva (CUREG_2021-04-38).
We performed a cross-sectional study to address our aims. Participants responded to a web-based questionnaire to define their profile. Then, they were presented with a series of prototyped functionalities to be ranked according to their preferences to analyze if they corresponded to those defined in our conceptual framework. We chose to contextualize the functionalities of adopting healthy diet and fitness apps as these issues allow to target a generic public. Indeed, the desire to stay fit is a behavior that most adults want to adopt. To ensure data are completely anonymous, participants’ IP addresses were not collected. We tested the questionnaire for usability and technical issues with 5 participants. This web-based survey is in accordance with the Checklist for Reporting Results of Internet E-Surveys [
The primary outcome is the preferred functionalities given the user profile.
Secondary outcomes are the feature preferences related to past or current use of mobile health (mHealth) apps, and the preference of functionalities according to the participant's state of motivation to change behavior.
The target population for this study included all individuals older than 18 years who understood French. We chose to conduct the questionnaire in French as this population was not necessarily fluent in English and comprised mainly native French speakers. An English language questionnaire would have introduced an element of bias as it might not have been correctly understood. Recruitment was conducted by posting messages on social networks (Facebook and Twitter) targeted at students at the University of Geneva, a young student population. The message indicated that we were seeking to recruit participants for a web-based study lasting 12 minutes as part of a research study conducted by the University of Geneva, with a focus on identifying user preferences based on their profile for a mobile app aimed at helping people get in shape. We also stated that the collected data remain completely anonymous.
For the calculation of the sample size, based on the hypothesis that altruistic people according to the Big Five prefer social networks [
Participants were asked to complete the web-based questionnaire developed using Qualtrics software (Qualtrics;
Participants were asked to indicate their gender, age, occupation, and level of education.
Participants were asked if they use mobile apps aiming at behavior change (such as to help them eat healthier or exercise) to find out if they were already familiar with mHealth apps and whether they already like certain functionalities. If so, we asked them to select which functionalities they used most often and which they never used. These questions allowed us to observe whether participants already familiar with mHealth prefer certain features, as well as whether they prefer the same features among the 17 proposed.
To assess participants' personalities, we relied on the Big Five Inventory-10 scale in French, translated and validated by Courtois [
This scale was chosen because it has a factorial structure identical to that of the full version of the Big Five Inventory scale in French [
To identify participants' gamer profiles, we chose the Hexad Scale, created and validated by Tondello [
For the perception of social norm, we chose two items concerning this dimension of the Theory of Planned Behavior questionnaire of Ajzen [
From the literature, we identified 17 functionalities commonly proposed in behavior change apps. We then created a prototype for each of these functionalities. All functionalities and their definition are presented in
Example of screenshots of the prototype app, including the (A) functionality competition, (B) functionality level and progression, and (C) functionality social network.
For each functionality selected, participants were asked to indicate how much that functionality would motivate them to adopt healthier behavior on a scale of 0 to 100. Then, they were asked why they chose these functionalities. Excluded functionalities will default to a score of 0.
We will exclude incomplete questionnaires and analyze only questionnaires that have been completed entirely.
Demographic characteristics of all participants will be presented using descriptive statistics (mean, standard deviations, or frequencies and range) in a table. A table will also provide responses about their use of mobile apps for health.
We will perform logistic regression with the functionalities as dependent variables and with scores of the 3 profile scales as predictors. This analysis will allow us to understand the effect of the participants’ scores on each of the 3 scales (Big Five Inventory-10 scale in French, Hexad Scale, and perception of the social norm) on the 5 selected functionalities. By performing a logistic regression for each feature, it will be possible to determine whether the scores on the different scales predict the selection of the functionality.
In addition, we will perform a logistic ordinal regression with the motivation score of the functionalities chosen as dependent variables and with scores of the 3 profile scales as predictors. By performing this regression for each functionality motivation score, it will be possible to determine whether the scores on the different scales predict the functionality score.
To test whether there is a difference in functionality selection by age or gender, we will run logistic regressions with the choice of the functionality as the dependent variable and age or gender as the independent variables. In addition, we will perform an ordinal regression with the motivation score of the functionalities as the dependent variable and age or gender as the independent variable. There will be one regression per feature.
To test whether participants indicated that they preferred functionalities that are the same as the ones already used in their current mHealth app, we will run simple regressions with the feature they already use as the independent variable and whether this feature was chosen as the dependent variable. There will be one regression per feature.
We will use the Bonferroni correction for all our regressions to avoid a type 1 error.
Qualitative analysis of the free text for the question regarding the explanation of the participants’ choice was performed, and common themes extracted. Response categories will be defined when reading the responses.
Recruitment and testing were conducted during July 2021. The deadline for the completion of the web-based questionnaire by participants was end of December 2021. We began analyzing the responses in January 2022, and the publication of results is expected at the end of 2022.
This study will define the preferences of functionalities of users with a specific profile (eg, what kind of functionalities are preferred by a user according to their personality). This protocol is important as its sample will enable to validate a model built on several previous studies and reviews. In turn, this will allow developers to build mobile apps that will be more efficient as adapted to each user. Thus, with this research, we will be able to better refine our conceptual framework, which will allow the mobile app designer to select features tailored to their users according to their profile and thus increase their involvement in the mHealth app.
The main interest of this research is that it gathers all the user profiles identified in the literature and all the functionalities generally implemented in mHealth. Indeed, we find studies allowing us to link personality and gamification elements [
Our study has some limitations. We designed it to be as neutral as possible to limit preferences linked to the design of one of the prototyped functionalities. However, it is still possible that participants may prefer a certain functionality because they found it more visually attractive. Our results are also possibly not generalizable to the whole population. Indeed, since recruitment was conducted at the university and on social networks, it is expected that most participants were students aged 18-25 years. Finally, as the questionnaire was in French language and only individuals living in the canton of Geneva and the surrounding area were included, it can only be generalized to this population (ie, French-speaking people of Switzerland and France).
It is important to help people adopt better health behaviors. Mobile apps are an interesting channel to support this effort because they integrate functionalities such as goal setting or self-monitoring that have been proven to foster behavior change. However, app efficiency can be improved by responding to user preferences according to their specific profiles. Our study will provide an additional evidence base to propose an accurate personalization conceptual framework for the development of future mHealth apps.
Presentation of the functionalities selected for our conceptual framework with their definitions and description of the screenshot.
Print version of the web-based questionnaire.
mobile health
We thank all participants for their valuable contribution.
The data sets generated during this study are available from the corresponding author upon reasonable request.
LG conceived the study with the involvement and advice of FE and GF. MP is involved in the statistical analysis. All authors participated in the writing and reading of the manuscript and approved the final version.
None declared.