Published on 21.12.18 in Vol 20, No 12 (2018): December
Using a Mobile Social Networking App to Promote Physical Activity: A Qualitative Study of Users’ Perspectives
Background: Despite many health benefits of physical activity, nearly a third of the world’s adult population is insufficiently active. Technological interventions, such as mobile apps, wearable trackers, and Web-based social networks, offer great promise in promoting physical activity, but little is known about users’ acceptability and long-term engagement with these interventions.
Objective: The aim of this study was to understand users’ perspectives regarding a mobile social networking intervention to promote physical activity.
Methods: Participants, mostly university students and staff, were recruited using purposive sampling techniques. Participants were enrolled in a 6-month feasibility study where they were provided with a wearable physical activity tracker (Fitbit Flex 2) and a wireless scale (Fitbit Aria) integrated with a social networking mobile app (named “fit.healthy.me”). We conducted semistructured, in-depth qualitative interviews and focus groups pre- and postintervention, which were recorded and transcribed verbatim. The data were analyzed in Nvivo 11 using thematic analysis techniques.
Results: In this study, 55 participants were enrolled; 51% (28/55) were females, and the mean age was 23.6 (SD 4.6) years. The following 3 types of factors emerged from the data as influencing engagement with the intervention and physical activity: individual (self-monitoring of behavior, goal setting, and feedback on behavior), social (social comparison, similarity and familiarity between users, and participation from other users in the network), and technological. In addition, automation and personalization were observed as enhancing the delivery of both individual and social aspects. Technological limitations were mentioned as potential barriers to long-term usage.
Conclusions: Self-regulatory techniques and social factors are important to consider when designing a physical activity intervention, but a one-size-fits-all approach is unlikely to satisfy different users’ preferences. Future research should adopt innovative research designs to test interventions that can adapt and respond to users’ needs and preferences throughout time.
J Med Internet Res 2018;20(12):e11439
Physical inactivity has been identified by the World Health Organization as a global public health problem, emerging as the fourth leading risk factor for global mortality . Research has shown that physical inactivity increases the risk of many chronic diseases—most notably, type 2 diabetes, coronary heart disease, and colon cancer [ ]. Nearly a third of adults worldwide are insufficiently active [ ], highlighting the need for effective health interventions to change behavior and promote physical activity.
It is widely acknowledged that behavior change is a challenging process. The success of behavior change depends not only on an individual but also on social and environmental factors [, ]. Behavior change interventions are usually complex (ie, involving several interacting components), which makes it hard to identify what is effective in changing a particular behavior, for whom, and in what context [ - ]. Several taxonomies for behavior change techniques (ie, the active components in health behavior change interventions) have been developed [ , ] in an attempt to isolate and identify the most effective components of interventions. For physical activity promotion, some behavior change techniques seem to be particularly relevant such as self-monitoring of behavior, goal setting, and social support [ , ]. In addition, the mode of delivery of the intervention is equally important, as it can influence its acceptance, dissemination, and long-term use [ , ].
The use of technology in the delivery of behavior change interventions has potential in promoting their success and diffusion. Notably, mobile health (mHealth) interventions, involving mobile apps and wearable devices, can reach individuals continuously, enabling the self-monitoring of health and physical activity data  and the tailoring of intervention components in real time [ ]. In addition, Web-based social networks seem to hold great promise, as they can help address social processes related to behavior change such as social support and social comparison [ , ]. Given their potential, interventions combining mHealth technologies and Web-based social networks might be particularly effective in promoting physical activity.
To date, a few qualitative studies have sought users’ attitudes and views on the use of mHealth technologies and Web-based social networks for physical activity promotion [- ], with most focusing on just one of these technologies. This limits the ability of researchers and developers to assess whether these 2 technologies can work in synergy. In addition, it remains unclear which behavior change components are most effective and which are considered more engaging by consumers [ ]. The aim of this study was to explore individuals’ perspectives before and after using a mobile social networking app for physical activity promotion. Specifically, we were interested in exploring potential barriers and facilitators to engagement with the intervention, as well as the behavior change techniques and delivery features considered important by users to promote physical activity. This research will help guide the future development of interventions and public health initiatives that could be more effective in influencing physical activity.
This study is part of a larger mixed-methods feasibility study on the use of a social networking mobile app to promote physical activity and weight management . Given the importance of physical activity and its impact on weight management [ - ], this paper focused specifically on factors influencing physical activity. This study adheres to the COnsolidated criteria for REporting Qualitative research checklist for reporting qualitative research ( ) [ ]. This study protocol was approved by the Macquarie University’s Human Research Ethics Committee for Medical Sciences (reference number: 5201600716). The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files.
Study Setting and Participants
This study was conducted at Macquarie University (Sydney, Australia). We recruited 55 participants, mostly university staff and students, using purposive sampling techniques through several channels, including posters around campus, website information, social media, and an email newsletter. Eligible participants were healthy adults with sufficient English to understand and participate in the study; aged between 19 and 35 years; who planned to be living in Sydney for the duration of the study; and owned a mobile phone (iOS or Android) with internet access. The exclusion criteria included pregnancy; body mass index (BMI) <17; prior history of eating disorders; or having diabetes or other comorbid conditions that could impact the study participation (eg, severe mental illness and end-stage disease).
For a 6-month period, participants were asked to use an intervention bundle (detailed below). Interviews were conducted pre- and postintervention, with the aim of assessing participants’ perspectives on the use of social networking and mHealth interventions to promote physical activity. Of 55 initial participants, 45 returned for the final interviews.
The intervention bundle was composed of a mobile social networking app (named “fit.healthy.me”), a fitness tracker (Fitbit Flex 2), and short message service text messages and emails . The mobile app “fit.healthy.me” consisted of several features—“My measures,” “My team,” “Social forum,” and “Private message”—which directly supported different behavior changes techniques (self-monitoring, social support, and social comparison). Specifically, “My measures” provided a summary of the number of steps, weight, and BMI. “My team” was a platform for participants to visualize and compare their steps with others. “Social forum” and “Private message” were designed for individuals to network with other users and provide and receive social support.
To enable the automation of self-monitoring, the app was integrated with the Fitbit Flex 2 fitness tracker, through the Fitbit Application Programming Interface. Reminders to wear the trackers and check the app were sent to participants every 2 weeks in the form of short message service text messages and emails.provides a detailed description of the modes of delivery and features of the intervention, and shows the screenshots of the “fit.healthy.me” app.
|Modes of delivery||Features||Behavior change techniquesa|
|fit.healthy.me app||My measures||Self-monitoring of behavior (ie, physical activity)|
|My team||Social comparison|
|Social forum||Social support|
|Private message||Social support|
|My journey||Instruction on how to perform the behavior|
|Fitbit Flex 2||Fitness tracker||Self-monitoring of behavior (ie, physical activity)|
|Texts and emails||Reminders||Prompts or cues|
aClassified according to the behavior change technique taxonomy developed by Michie et al .
Prior to study commencement, an interview guide () was developed and pilot-tested. Participants were invited to attend the initial study session at the research center, where they received information about the purpose of the study, signed the consent form, and filled in a questionnaire about their demographic characteristics and smartphone usage (eg, the type of smartphone used and hours per day spent using the smartphone).
In the preintervention session, 55 participants attended a brief individual interview (10-15 minutes) in which they were asked about perceived facilitators and barriers to physical activity and their views on the potential advantages and disadvantages of the mobile app and wireless devices (fitness tracker and scale). The content of the preintervention interviews was summarized and used as prompts for discussion in the postintervention sessions.
In the postintervention session, we conducted 32 individual interviews and 5 focus groups with 13 participants (20-45 minutes); data saturation was reached. While the interviews allowed us to understand individual perspectives, the focus groups enabled us to explore group differences and similarities [, ].
At the postintervention sessions, participants talked about their experiences regarding the use of the intervention and provided suggestions on the devices and the intervention. Furthermore, semistructured interviews were conducted by 2 researchers with expertise in qualitative methods. Field notes were taken throughout the interviews.
Data Management and Analysis
With participants’ consent, the interviews were recorded and transcribed verbatim, and transcripts were analyzed in Nvivo 11 (QRS International Pty Ltd., Melbourne, Australia). The data were analyzed using thematic analysis techniques . Specifically, the transcripts were explored using the inductive analysis to identify themes and patterns [ ]. First, we open-coded the transcripts to identify all important aspects related to the research questions. Subsequently, by scrutinizing and comparing different data and codes (ie, constant comparison), we pinpointed concepts that seemed to cluster together [ ]. Informed by engagement with the literature, we identified the similarities, differences, and general patterns in the open codes, to fill in underdeveloped categories, narrow excess ones, and organize them into major themes [ , ].
summarizes participants’ demographic characteristics. At baseline, 51% (28/55) participants were females; the mean age was 23.6 years. On average, participants spent 5.6 hours daily using smartphones, and 89% (49/55) participants stated that they frequently used social media. Of all, 76% (42/55) participants were university students.
Summary of Results
We found the following 3 types of factors emerging from the data as influencing user engagement with the intervention and physical activity levels: individual, social, and technological. At the individual level, participants mentioned that goal setting, self-monitoring, and feedback were important for their physical activity. At the social level, social comparison and the connection with other users in terms of familiarity and similarity were considered motivating. Finally, at the technological level, automation and personalization were considered to be facilitators, while technological limitations were observed as reducing user engagement. The following sections discuss each of these themes in detail, with illustrative quotations (- ).
|Age, mean (SD)||23.6 (4.6)|
|Female gender, n (%)||28 (51)|
|Weight, mean (SD)||78.1 (22.3)|
|BMIa (kg/m2), mean (SD)||26.5 (6.8)|
|BMI categoriesb, n (%)|
|Steps/day, mean (SD)||9937 (3527)|
|Marital status, n (%)|
|In a relationship||22 (40)|
|Married or de facto||6 (11)|
|Daily smartphone use (hours), mean (SD)||5.6 (3.4)|
|Most used appsc, n (%)|
|Social media||49 (89)|
|Fitness apps||6 (10)|
|Occupation, n (%)|
|Smartphone, n (%)|
aBMI: body mass index.
bAccording to the World Health Organization, a BMI of <18.5 is classified as underweight, 18.5-24.9 as normal, 25-29.9 as preobese, and ≥30 as obese .
cMost used apps—options are not mutually exclusive.
Individual-Level Factors Influencing Physical Activity
Self-monitoring was deemed important by many users, as it increased their awareness of activity levels and performance, as well as enabled them to review their progress over time and better plan their exercise (, quotes 1 and 2). Some users indicated that even though self-monitoring was important, knowing the daily number of steps was not sufficient, as they were doing other types of exercise. Thus, they would prefer to measure parameters that were relevant to the type of activity they did ( , quotes 3 and 4).
Other than physical activity, users also expressed the desire to monitor a wide range of health-related information (eg, sleep). By having multiple types of information about themselves, users felt they could get an overall view of their daily patterns, and how external factors (eg, family, jobs, and study) affected their health and well-being (, quote 5).
Many participants expressed that they benefited from goal setting. They believed that setting a goal (eg, 10,000 steps daily) kept them accountable for their physical activity performance and motivated them to reach that goal. Participants indicated that goal setting and self-monitoring complemented each other because, without self-monitoring, they would have no way of knowing whether their goals had been achieved (, quote 6). In addition, many participants expressed the desire to be able to personalize their goals to fit with their ability and daily routines, rather than having a standard goal ( , quote 7).
Feedback on Behavior
For many users, the feedback on progress toward goals was particularly encouraging; knowing that they were close to reaching their goals would motivate users to do more physical activity, while being notified of goal achievement gave them positive emotions (, quotes 8 and 9). Nevertheless, some participants mentioned that knowing they had not achieved their goals also brought on some negative feelings such as disappointment or guilt ( , quote 10).
Social-Level Factors Influencing Physical Activity
Participants mentioned that comparing themselves with other users encouraged them to be more engaged with the intervention, as well as to be more physically active (, quotes 1 and 2). One interesting aspect was that comparisons with higher, lower, or similar standards of physical activity (upward, downward, and lateral comparisons in accordance to [ ]) had different effects on performance, according to participants. Most users said that they preferred to compare themselves against higher performers because that motivated them to try to learn their strategies and be more physically active, to beat the top level ( , quote 3). Other users mentioned that they would like to compare themselves to both similar and higher standards ( , quotes 4 and 5). On the other hand, some participants mentioned that comparison to higher standards could be rather demotivating and confronting, especially when they failed to achieve as many steps as others. Instead, those users preferred comparing themselves with lower standards, which gave them a sense of confidence and assurance that they were on the right track ( , quotes 6 and 7).
Familiarity With Other Users
For many participants, social comparison and providing social support did not hold much meaning if they did not personally know other users. Many suggested that they were more likely to be engaged if they were “familiar” with other users (eg, if other users were their real-life social connections;, quotes 8 and 9). On the other hand, some participants mentioned that they did not necessarily need to know other users in real life; however, they needed to have some information about other users such as their lifestyle, fitness goals, or the types of activity they did, which could form the basis for social comparison ( , quotes 10 and 11).
Similarity With Other Users (Homophily)
Other users did not stress the importance of “familiarity”; instead, they described a preference to share data within a social network of people who shared similar attributes or goals to them (a phenomenon known as “homophily” ). Particularly, some participants preferred to connect with users who had similar BMI or were doing the same type of physical activities ( , quotes 12 and 13). In addition, a lot of participants emphasized the importance of having a similar goal, as it might facilitate more meaningful comparison and discussion on PA strategies ( , quotes 14 and 15).
Participation From Other Users
Participation from other users was important for people to engage with the social network component of the intervention. Many users described attrition as a “domino effect”—once a certain number of people stopped using the app or the wearable tracker, other users subsequently felt less motivated to use the technology (, quotes 16 and 17).
Technology-Level Factors Influencing Physical Activity
Technological Facilitators of Engagement and Behavior Change
Many participants found that using the wireless tracker and scale in combination with a mobile app offered many advantages. Specifically, wireless devices provided an automatic way for users to collect and self-monitor personal measurements, and their integration with the mobile app provided a user interface platform for participants to visualize those data and to review progress (, quotes 1 and 2).
Many users mentioned that having personalized information and services would also support long-term engagement, as they could offer the advantage of providing relevant information tailored to each specific user, thus eliminating the cognitive burden of dealing with information overload. Many users described that personalization should go beyond the content generated by the system and extend to the provision of relevant services (eg, suggestion of exercise routines;, quotes 3-5).
Technological Barriers to Continued Usage
As time went on, many users described the feeling that the novelty of the technology had worn off, and they started to think of it as a chore. Even apparently simple tasks like charging the devices were seen by participants as an extra burden in their already busy daily routines (, quotes 6 and 7).
Technical Problems and User Experience
Technical problems were often described as a common cause for attrition (, quote 8). In addition, user experience factors, such as the design aspects of the interface and its usability, were reported as important aspects of engagement and continued use ( , quotes 9 and 10).
This study explored users’ perspectives regarding facilitators and barriers in using mobile social networking interventions to promote physical activity. The following 3 categories of influencing factors emerged: individual, social, and technological. At the individual level, behavior change techniques, such as goal setting, self-monitoring, and feedback, were suggested as important for user engagement in physical activity. At the social level, social comparison, familiarity, and similarity with other users were mentioned as motivating aspects. Finally, automation and personalization were highlighted as technological facilitators, enhancing the delivery of both individual and social aspects of the intervention. However, some technological limitations were also found to be barriers to user engagement.
Comparison With Previous Literature
Our findings suggest that the success of a behavior change depends on a range of factors, including both individual and social aspects. These findings are in line with other behavior change theories, namely the social cognitive theory , and the Capability Opportunity Motivation—Behavior model [ ]. Both theories suggest that even though several behavioral factors (eg, self-regulation [ ], capability, and motivation [ ]) are largely dependent on individuals, external factors (eg, peer modeling [ ] and environmental structure [ ]) can arise from the physical or social environments to prompt behavior. Hence, it seems sensible to integrate both individual and social aspects of behavior change in physical activity interventions to increase their long-term success.
In line with our results, behavioral informatics interventions (eg, a mobile social networking app, connected with a fitness tracker) can facilitate the delivery of both individual and social aspects in physical activity interventions . Specifically, fitness trackers can automate the self-monitoring of behavior and connect to mobile apps with social features, allowing users to not only view their progress but also continuously benefit from social support [ , ]. To date, one qualitative study has examined how wearable trackers, mobile apps, and Web-based social networks may interact, finding that social support from Web-based networks can be effective in increasing users’ adherence and engagement with the wearable trackers [ ]. However, this study had a couple of limitations—it included a small number of users, as well as nonusers of wearable trackers; and it examined Web-based social networks as a stand-alone feature, not integrated with the trackers. In contrast, our study provided participants with an integrated intervention, including mHealth and social networking components, which allowed us to explore the informed perspectives of participants who used these technologies for 6 months.
Individual-Level Behavior Change Techniques
Our users indicated that goal setting, self-monitoring of behavior, and feedback on behavior could encourage them to engage in physical activity, which is in line with previous qualitative studies [, ]. Indeed, these 3 self-regulatory techniques have demonstrated the effectiveness in physical activity interventions [ ] and may work in synergy—to maximize the effects of goal setting, people may need to self-monitor and receive feedback, which allows them to see their progress in relation to their goals and change their strategies if necessary [ ].
In addition, previous research has suggested the need to examine which type of goal is best for motivating individuals to be more active and how technologies can best support monitoring those goals and providing feedback. The literature seems to suggest that small goals (described as “graded tasks” in the Coventry, Aberdeen, and London—Refined taxonomy ) are more effective for long-term engagement compared with larger and harder to achieve goals [ ]. For example, Fitbit provides users with small goals of taking 250 steps per hour, which then facilitates the achievement of the daily goal of 10,000 steps [ ]. It is worth noting the importance of real-time self-monitoring and consistent feedback for the success of this “small goals” approach [ ], underlining implications for the design of mobile apps and wearable trackers.
Social Networks and Social Features
This study emphasized the role of social comparison, familiarity, and similarity with other users in a social networking intervention. First, our participants revealed different preferences regarding social comparison. This finding is in line with previous research, where it has been demonstrated that individual preferences might depend on their tendency to make upward or downward comparisons . Specifically, previous studies have illustrated that some people seek social comparison to self-improve [ ], and thus, upward comparison may reinforce positive fitness behavior by making it seem normative or even rewarding [ , ]. For others, instead of seeking feedback about themselves, they want to create and maintain a positive self-image, and thus, prefer to make a downward comparison [ , ]. Taken as a whole, this finding suggests that a one-size-fits-all approach to social comparison is unlikely to suit all users, and thus, social comparison needs to be tailored to each individual.
Second, familiarity and similarity were found to be important factors in a social networking intervention for physical activity. The importance of familiarity seems to be in line with previous literature, where researchers have demonstrated that existing social networks can greatly influence individual health behaviors [, ], leveraging social support and potentially increasing the intervention effectiveness [ , , - ]. Research has shown that strategies involving new networks might not be as effective as ones capitalizing on existing connections [ , ], which suggests that fitness technology may be most effective when groups of people who know one another have access to the same device or app [ ]. Thus, allowing study participants to invite friends and family to join an app may increase the real-world effectiveness of these interventions [ ], despite potential problems of contamination.
Furthermore, this study showed that similarity is important for motivation and engagement, highlighting the role of homophily (ie, the tendency of people to bond with alike individuals) . Notably, previous research has indicated that social networks structured on the basis of homophily lead to higher adoption of healthy behaviors [ ]. Moreover, it has been suggested that when people with similar interests interact to achieve a shared goal, they can provide each other with support and companionship in the activity, and thus, reduce the perceived costs of adopting a new exercise routine [ , ]. Taken together, these findings highlight the benefits of leveraging homophily to foster collective efficacy and improve physical activity.
Technology As a Platform to Bring Together Individual and Social Levels
Through automation and personalization, multiple modes and features of technology can work synergistically to deliver a physical activity intervention with both individual and social factors [, , ]. Thus, the integration of multiple mHealth technologies can automate several aspects of health management, reducing the burden on users. Furthermore, many users suggested the importance of personalized features within the intervention. Indeed, a one-size-fits-all approach is unlikely to satisfy many needs and wants of users [ ], which emphasizes the need to consider individual lifestyles and preferences when designing interventions.
Strengths and Limitations
This study has several strengths. We interviewed users after 6 months of experiencing the intervention, ensuring that our sample had an informed perspective. The combination of individual interviews and focus groups enabled us to capture both individual perspectives and social dynamics in a group setting, which are essential aspects to understand in a social networking intervention. The findings of this paper must be interpreted in light of some limitations. First, study recruitment was limited to a university setting with a young age group. Though the main purpose of qualitative studies is not to make generalizable claims , future research with a diverse sample could explore other contextual factors related to behavioral informatics interventions (eg, an older age group might encounter different barriers and facilitators of a mobile social networking app). Second, as this was part of a feasibility study, the technology used was at a prototype stage and not yet extensively tested. Finally, despite our engagement efforts, we were not able to interview participants who dropped out of the study—they might have different perspectives on the facilitators and barriers of the intervention.
Implications for Future Research
This study highlights several important implications, including suggestions on the intervention design and new research avenues. Interventions for physical activity promotion should consider offering goal setting, self-monitoring, and feedback as a bundle, as these techniques have been shown to be both effective and acceptable to end users. Consequently, the design of mobile apps and wearable trackers need to effectively assist with real-time self-monitoring and provide consistent feedback to enable the achievement of goals . In addition, the potential of social behavior change techniques (eg, social comparison) should be further explored, and aspects of leveraging existing social ties and homophily could be considered in constructing a social network intervention for physical activity. Questions remain about the cost-effectiveness of wearable trackers and mobile apps as a public health initiative, opening up new possibilities for future health economics research and public health programs [ , ].
Furthermore, this study highlights the importance of personalization. By identifying users’ behavioral patterns and preferences, researchers can design and deliver interventions at the right time, using the right channel and tone, and the most relevant content or services [, ]. Future studies should use innovative study designs to determine which intervention components are effective, what is the optimal sequence for delivering these components, and which tailoring variables should be used [ , ].
This study provides insights into the individual, social, and technological factors that influence user engagement with a mobile social networking app for physical activity promotion. Our findings reveal that self-regulatory behavior change techniques seem to be a necessary element in these interventions, and that aspects related to social comparison, existing social ties, and homophily should be considered in the development of the social network component. Future research should adopt innovative research designs to evaluate the effectiveness of these different components, as well as investigate the delivery of personalized interventions.
The authors would like to thank Ms Paige Martin for her contribution to the mobile app design and development and data collection. HLT is supported by the International Macquarie University Research Training Pathway Master of Research (iMQRTPMRES) Scholarship (2016302). This research is supported by a grant received from the National Health and Medical Research Council Centre of Research Excellence in Informatics and E-Health (1032664). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
HLT, EC, and LL conceptualized the study. HLT developed and pilot-tested the interview guide, conducted the interviews and focus groups, performed data analysis, and wrote the first draft of the manuscript. LL pilot-tested the interview guide, conducted some data collection and analysis, and provided guidance on data analysis and critical feedback on the manuscript. EC critically revised the manuscript.
Conflicts of Interest
EC could benefit from commercialization of fit.healthy.me.
Multimedia Appendix 1
COnsolidated criteria for REporting Qualitative research checklist.PDF File (Adobe PDF File), 132KB
Multimedia Appendix 2
Screenshots of the fit.healthy.me mobile app.PDF File (Adobe PDF File), 177KB
Multimedia Appendix 3
Interview guides.PDF File (Adobe PDF File), 66KB
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|BMI: body mass index|
|mHealth: mobile health|
Edited by G Eysenbach; submitted 28.06.18; peer-reviewed by A Sullivan, A Henriksen; comments to author 29.08.18; revised version received 06.09.18; accepted 08.09.18; published 21.12.18
©Huong Ly Tong, Enrico Coiera, Liliana Laranjo. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.12.2018.
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