Obesity and physical inactivity are major societal challenges and significant contributors to the global burden of disease and health care costs [1-3]. Globally, excess body weight and obesity account for approximately 4 million deaths in a year and 120 million disability-adjusted life years [1]. Effective weight management interventions usually involve a combination of behavioral strategies focusing on dietary changes and physical activity [4]. Notably, physical activity is also independently associated with better health outcomes in a dose-response manner [5], however, more than one-fourth of adults globally are insufficiently active [6]. Promoting physical activity and weight management (ie, preventing weight gain to unhealthy levels and promoting weight loss in individuals with excess weight) are important behavioral strategies for better health outcomes.

Addressing obesity and physical inactivity requires a combination of environmental approaches and effective behavior change interventions that can be easily disseminated. Apps and fitness trackers are becoming pervasive in the daily lives of people, with smartphone ownership surpassing three-fourth of the population and activity trackers being used by one-third of adults in the United States and United Kingdom [7,8]. These mobile technologies enable the automation of effective behavior change techniques—weight and physical activity self-monitoring and feedback [9-12]—showing potential in weight management and physical activity interventions [13,14].

Although mobile technologies can facilitate self-monitoring and feedback, behavior change is also heavily regulated by social processes. Online social networks—platforms that allow individuals to create their own personal profile and build a network of connections with other users—can facilitate behavior change [15-18] and have shown potential in weight management and physical activity interventions [19-22]. Online social networks enable the delivery of several social functions, including support and social comparison, which have been associated with increased physical activity [23-25] and greater weight loss in weight management interventions [26,27]. However, user preferences regarding social features seem to be mixed: for some users, these features promote engagement with the intervention, whereas for other users, they are less enjoyable or even disliked [20,28]. Key questions remain as to which intervention features and social network characteristics are more effective and engaging for users in weight management and physical activity promotion [15,18].

This 6-month pilot study of a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: (1) to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and (2) to assess user perspectives on the intervention, particularly its online social networking component and automated self-monitoring and feedback features.

This was a mixed methods study involving a pre-post quasi-experimental pilot with one arm, where participants used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months [29]. Ethics approval was granted by the Human Research Ethics Committee for Medical Sciences of Macquarie University. Reporting follows the mobile health (mHealth) evidence reporting and assessment (mERA) checklist [30], the transparent reporting of evaluations with nonrandomized designs (TREND) statement for reporting of nonrandomized evaluations of behavioral and public health interventions [31], the COnsolidated Criteria for Reporting Qualitative Research (COREQ) checklist [32], and guidelines for good reporting of mixed methods studies [33] (Multimedia Appendices 1-3).

Eligible study participants were consenting healthy adults who were able to speak, write, and read English, were between 19 and 35 years of age, owned a mobile phone (iOS or Android) with internet access, and had Wi-Fi connection at home. Exclusion criteria were pregnancy or breastfeeding, BMI below 17 kg/m², prior history of eating disorders, and having diabetes or other comorbid conditions that could have impacted study participation.

Students and staff from Macquarie University (Sydney, Australia) were recruited via posters and flyers distributed around campus as well as via a Facebook post on the university’s page (Multimedia Appendix 4). Recruitment was completed in April 2017 and followed a purposive convenience sampling technique to test the intervention in a diverse BMI sample. To enable a comparable number of individuals in lower and higher BMI groups (at least 20 individuals in each of those 2 groups), recruitment of individuals in the normal BMI range was stopped earlier, whereas recruitment for the higher BMI groups continued. A sample size of at least 40 individuals was pragmatically chosen to enable pilot testing of the intervention based on available funding. At baseline, participants were invited to attend the initial study session at a research center on campus, where they signed the consent form and filled in a questionnaire about their demographic characteristics. A complete description of study procedures and interventions can be found in published papers [29,34,35].

The intervention consisted of a mobile app (fit.healthy.me) [29,34,35] designed by the research team, which was integrated with 2 wireless devices (Fitbit Aria weight scale and Fitbit Flex 2 physical activity tracker, connected via the Fitbit app). The goal of the intervention was to promote physical activity and support weight management in users of any physical activity level or BMI group. In fit.healthy.me, the participants could compare their step count and weight with other users, in table and graphical formats (Multimedia Appendix 5). Furthermore, users were able to interact and provide social support to each other through the use of messaging and a social forum, as well as follow particular buddies with whom they identified more closely.

The intervention allowed for the delivery of several behavior change techniques [11]: self-monitoring and feedback on behavior (daily number of steps), self-monitoring and feedback on weight and BMI, instructions on how to perform the behavior, social support, and social comparison. Prompts and cues (text messages and emails every 2 weeks) were used to promote engagement with the intervention. Goal setting was not incorporated in fit.healthy.me but it was a core component of the Fitbit devices (eg, the activity tracker had a predefined daily step goal of 10,000, which could be modified by users); goal setting was neither promoted nor discouraged by researchers.

An interview guide [34] was pilot tested before study commencement. In the final 6-month session, 2 researchers with expertise in qualitative methods conducted individual interviews and focus groups with participants to understand their perspectives on the advantages and disadvantages of the intervention, until data saturation was reached. The interviews allowed us to understand individual perspectives, and the focus groups aimed to explore group differences and similarities. Field notes were taken throughout the interviews and focus groups. Interviews and focus groups were audio-recorded and transcribed verbatim.

All data were imported and managed in Nvivo 11 (QSR International). Data were analyzed using thematic analysis, where transcripts and field notes were read to identify and code common ideas and patterns emerging from the data [39]. Through constant comparison, codes and concepts were clustered together to form subthemes, and further abstracted to originate themes, which were then reviewed and refined [40]. The integration of results was done after quantitative and qualitative analyses were conducted through embedding of the data. Integration is presented throughout the Discussion section.

A total of 55 participants were recruited, with a mean age of 23.6 (SD 4.6) years; 51% (n=28) were female (Table 1). Most (n=24, 44%) participants had a normal BMI, 27% (n=15) were overweight, 24% (n=13) were obese, and 5% (n=3) were underweight. The mean step count per day at baseline was 9937 (SD 3527). Of the 55 recruited participants, 45 returned for the final session (study completers)—the retention rate was 82%. There were no statistically significant differences in baseline characteristics between enrolled participants and study completers.

We conducted 32 individual interviews and 5 focus groups with 13 participants (20-45 min for each interview or focus group). Themes and subthemes did not differ between interviews and focus groups and consisted of (1) social comparison and networking (subthemes: social comparison, digital people watching and data sharing, social interactions, and negative aspects of social interactions), (2) self-monitoring (subthemes: self-monitoring of weight and BMI, self-monitoring of steps, and other fitness-related measures), and (3) Personalization and gamification. Social comparison and self-monitoring were seen as very distinct features by users, each with its positive and negative aspects, which in turn influenced motivation to use the app and engagement with the intervention. Personalization and gamification were commonly mentioned as desirable features to promote long-term engagement.

In this mixed methods study, we found that participants in the overweight-obese group significantly lowered their BMI by almost 1 kg/m² (3%) during the first month of the intervention, showing no statistically significant difference at the end of the intervention. Participants who were overweight-obese used the app significantly less compared with individuals in the underweight-normal BMI group, mentioning negative feelings and demotivation arising from social comparison, particularly upward comparison (with fitter people). Participants in the underweight-normal BMI group were avid users of self-monitoring, feedback, and social features within the app and significantly increased their daily step count over the 6-month study duration (by more than 2000 steps). Most participants mentioned the desire for a more personalized intervention.

In our study, social comparison and competition were preferred by participants in the underweight-normal BMI group (particularly upward comparison). Participants in the overweight-obese group mentioned a sense of vulnerability in sharing their data (eg, weight) in a social network and highlighted negative feelings and demotivation from upward comparison, similar to other studies [42], as well as a preference for face-to-face connections for support and accountability. These findings suggest that this group may benefit from being in a network of people with similar characteristics and goals. Previous research on weight and physical activity interventions has revealed that people seem more willing to participate in an online social network with others having common aspects (eg, real-world connections, similar goals, or fitness levels) [43-48].

This study builds on previous literature showing that online social networks can facilitate behavior change [15]. However, we found that user preferences regarding social features seem to be mixed, which indicates that such features should be available but optional, allowing users to control what information is shared and with whom [43,49,50]. Some people reported that they enjoy sharing their fitness achievements to receive praise and social support, in which case broadcasting to larger social networks such as Facebook seems to be helpful [51]. On the contrary, others did not like to share or participate in a social network but enjoyed lurking, that is, passively looking at content in social media without actively engaging [52]. This digital people watching effect promulgated by social media seemed to be enjoyed by some participants in our study.

Regular self-monitoring of weight and BMI was an activity that most participants in our study were not particularly fond of. Participants in the overweight-obese group recognized the importance of weight monitoring but still preferred focusing on steps, due to an increased sense of perceived control over changes and the desire to avoid negative emotions. Other studies have reported on users’ concerns about the potential for negative emotional and motivational impact when discrepancies between reality and goals are revealed in health apps [43]. Despite these concerns, regular self-monitoring of weight has been associated with weight loss and weight maintenance in individuals who are overweight and obese and is considered a healthy weight control strategy in people with a normal weight, with little evidence of adverse effects [53-58]. Given the importance of self-monitoring in behavior change [10,12], new strategies should be explored to promote engagement, such as decreasing the frequency of weighing to a level that individuals are able to maintain.

Personalization was a recurrent topic endorsed by participants in our interviews and has been emphasized in other studies of digital interventions as a critical aspect for weight management and physical activity [49,50,59]. Nowadays, technology allows for increasing levels of personalization, having the potential to enhance engagement with digital health interventions [60] by molding them to be more relevant to users based on their personal characteristics or their motivation to change lifestyle [43,61]. In our study, people within and between different BMI groups showed different preferences regarding core features such as self-monitoring and social comparison, which, in turn, seemed to influence their engagement with the intervention. In addition, there was higher variability in the rate of BMI change across the overweight-obese group compared with the underweight-normal group over the duration of the intervention, suggesting that the overweight-obese group might benefit from higher degrees of personalization to accommodate the greater variability in this group. Currently, most commercial activity trackers and mobile apps offer one-size-fits-all interventions with minimal personalization, which may be a factor in their high drop-off rates [62,63].

The strengths of this study include the objective measurement of outcomes using digital devices instead of self-reported data, the use of exploratory linear mixed effects analysis to better understand participants’ BMI changes weekly throughout the intervention period and complement pre-post data, and the use of postintervention interviews and focus groups to better understand pilot test results and assess the advantages and disadvantages of the intervention and its components. However, the findings should be interpreted in the context of some limitations. This was a quasi-experimental study with a single-arm pre-post design, and causation cannot be inferred from our results. Posthoc subgroup analyses and linear mixed effects analysis were exploratory and might be subject to type I error. As in other studies [15,18,64], engagement with the intervention decreased over the 6-month period, and there was a high degree of missing data over the duration of the study, which affected the quality of the mixed effects analysis. Baseline daily step count (average for the first week of the study before being able to access the study app) was considerably high in both BMI groups, which might reflect the novelty effect of starting to use an activity tracker. There was a predominance of men in the higher BMI categories at baseline, and a predominance of women in the lower BMI categories, which may explain the observed higher weight loss in men. We only used step count as a measure of physical activity, and other measures (eg, minutes of moderate-to-vigorous physical activity) might have shown different results. The results of our study should be interpreted in the context of concomitant use of the Fitbit app (which provided additional features such as goal setting, not available in the fit.healthy.me app) and the different goals participants may have had for joining the study (eg, increase physical activity, lose weight).

Our study found different effects of the intervention in the underweight-normal BMI group and in the overweight-obese group, with participant perspectives also varying depending on the BMI group. The increase in step counts in the underweight-normal BMI group is promising given that any intensity of physical activity, including light intensity, is associated with a lower risk of premature mortality in a dose-response manner [5,65]. The short-term BMI decrease seen in the overweight-obese group may be due to the novelty factor of the intervention. Future research should explore whether it is possible to promote long-term physical activity and BMI changes in individuals who are overweight and obese with such an intervention and what types of interventions and features are associated with higher effectiveness (eg, interventions focusing on contextual and environmental factors, in addition to individual and social aspects; interventions offering personalization, such as the possibility to turn off social features or only allow for social comparison with similar individuals in terms of physical activity and BMI).

The importance of personalization was highlighted in this study by the heterogeneity of participant perspectives regarding intervention features. Smartphones and wireless trackers enable the collection of large volumes of personal data that can be leveraged to personalize interventions. Recent developments in artificial intelligence have led to the common use of machine learning models to optimize intervention content, timing, and delivery, based on users’ preferences, habits, and other individual and contextual data [66-68]. Future research should explore the impact of personalized features to better match individual preferences, barriers, and needs to promote higher engagement and enhance the effectiveness of interventions.

A social networking mobile app connected to wireless tracking devices had different effects on participants in higher and lower BMI groups. Participants in the overweight-obese group showed a short-term decrease in their BMI that was not sustained after 1 month, and they used the app significantly less than participants in the underweight-normal BMI group, mentioning negative feelings with app use. Participants in the underweight-normal BMI group significantly increased their daily step count over the 6-month study duration. Most participants mentioned the desire for a more personalized intervention. Future research should explore the use of personalized features to better match individual preferences, barriers, and needs.