Healthy lifestyle behaviors have increased the life expectancy of those who adopt them and help individuals to live not only longer but better [1]. More specifically, adopting a healthy diet, maintaining a healthy weight, quitting smoking, drinking alcohol in moderation, and regular exercise are 5 behaviors associated with lower mortality. An increasing number of health apps aiming to help people adopt better health behaviors are reaching the market annually, with over 35,000 health apps available in 2018 [2]. Smartphone apps offer new opportunities to adopt health-related behaviors by providing immediate access to information about one's health, reminders to take medication, or help track one's progress [3].

Several scales exist to measure the quality of these health-related mobile apps, such as the Mobile App Rating Scale [4] and the App Behavior Change Scale [5]. A common feature of these scales is to consider a mobile app's personalization as a quality factor. Indeed, personalization is an important aspect to consider when creating an app that enables behavior change. For example, it has been shown that messages tailored to the user tend to be read more, recalled more, attract more attention, be better remembered, be a topic of discussion with others, and be perceived as personally relevant compared to untailored messages [6].

Based on a previous literature review, we identified the personality traits more likely to adopt certain app functionalities [7]. These findings led to the development of a model indicating the type of features preferred according to a user profile. When designing a mobile app aiming at behavior change for health, designers can refer to our model as a guideline to know what functionalities they should privilege for their apps, given the profile of the intended users. For example, if a person is extroverted according to the Big Five, it will be relevant to privilege functionalities allowing comparison and cooperation between users [8].

Our model contains 17 functionalities presented in detail in Multimedia Appendix 1. For the user profile, we relied on the most common classification dimensions found in the literature, as follows: personality profiles [8-16], game preference [11,17,18], and perception of social norm [19] (Table 1). Gender and age are also important, and a recent review showed a difference in the type of functionality preferred according to gender, although no study in the review included individuals older than 31 years [17].

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 [18], which defines the user's gamer profile according to the following 6 dimensions: disruptor, achievers, free spirit, player, socializer, and philanthropist. For example, participants with the profile “player” are motivated by extrinsic rewards and will do anything to earn a reward within a system. This type of profile is interesting to consider for apps that use gamification, which is also a concept widely used nowadays to incite behavior change. We can define gamification as “the use of game design elements in non-game contexts” [21]. Indeed, gamification positively affects motivation, engagement, and enjoyment [22]. Finally, the perception of social norm is the “individual's perception that other individuals important to the respondent believe that the respondent should perform the behavior of interest” [23]. This perception can help or hinder the performance of the behavior, depending on how the user's entourage perceives it. Therefore, it is important to consider this factor, and, depending on this perception, different functionalities can be included.

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 [24].

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 [11,17], we used the multiple regression power calculation on R (R foundation for Statistical Computing), with the following measures: u=3; f²=0.07; P=.05; power=0.9; and variance=202.403. To estimate variance, we relied on a previous study [25] investigating the preference of users classified according to the Big Five on posters. More specifically, we looked at the variance of altruistic participants (n=46) according to the Big Five on the average ratings of a poster representing a social network promoting blood donation (score from 0 to 100). Thus, we obtained a sample size of 206.

Participants were asked to complete the web-based questionnaire developed using Qualtrics software (Qualtrics; Multimedia Appendix 2). First, they completed the consent form describing the purpose of the study and the procedure and informing them of their right to withdraw from the study. They were asked to confirm that they have read and understood the consent form and agree to have their responses used in our research and scientific publications. They can then access the rest of the questionnaire if they accepted these clauses. If not, they were informed that without their consent, we cannot collect their data and must terminate the survey. Next, participants were asked to answer demographic questions. In the case of a participant younger than 18 years, we explain that only those aged >18 years can participate and therefore we cannot continue with the questionnaire. Eligible participants continued to answer the questionnaire online where they had to (1) respond to scales to measure their profile, and then (2) look at the 17 features, select 5, and indicate on a score from 0 to 100 how much these features would motivate them to get back in shape.

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 [8,9,14], personality, gamer profile, and gamification elements [11], personality and sensitivity to persuasion strategies [10,27], or personality and need for cognition [28]. Moreover, these studies are not necessarily specific to the field of mobile apps for behavior change. Some studies are more focused on preferences related to video games [9,14,29] and others on the type of messages and feedback [19,30]. Therefore, our research allows to combine what has been done previously in different studies and to corroborate their findings for mHealth apps regarding user preferences according to their specific profile.

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.