Effectiveness of an achievement goal theory-based mobile intervention for increasing physical activity among college students: A randomised controlled trial

Effectiveness of an achievement goal theory-based mobile intervention for increasing physical activity among college students: A randomised controlled trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effectiveness of an achievement goal theory-based mobile intervention for increasing physical activity among college students: A randomised controlled trial Ye Hoon Lee, Hyungsook Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7189266/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although it is well known that regular physical activity is beneficial to physical and psychological health, the physical activity levels of adults and college students worldwide remain insufficient, and few studies have applied the achievement goal theory to a mobile health environment to verify its effectiveness. Therefore, this study examined the effects of a mobile intervention based on the achievement goal theory to improve college students’ physical activity. Specifically, this study compared the effects of mastery- and performance-oriented messages on participants’ walking behaviour. A total of 87 South Korean university students (mean age = 22.59 ± 2.00 years; 56.3% men) were randomly assigned to one of three groups: mastery-oriented (n = 29), performance-oriented (n = 29), or control (n = 29). During the eight-week intervention period, the mastery-oriented group received nudging messages emphasising self-referential goals and personal growth, whereas the performance-oriented group received messages emphasising social comparison and competition. The control group did not receive any motivational messages. Walking duration, distance, and frequency were objectively measured using the Nike Run Club mobile application. The results showed significant differences between groups in walking duration (p = .006), distance (p = .021), and frequency (p = .021). More specifically, the mastery-oriented group showed significantly better performance on all three measurement indicators than the control group, but there was no significant difference between the performance-oriented and control groups. These results emphasise the potential of mobile interventions to promote physical activity by promoting intrinsic motivation and self-referential growth with mastery-based motivational messages. Therefore, practitioners and health experts should integrate mastery-oriented nudge strategies into mobile health platforms to promote sustainable physical activity participation among young adults. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Introduction Although it is well known that regular physical activity (PA), even low PA, is beneficial to physical (Penedo & Dahn, 2005; Spartano et al., 2019; Warburton et al., 2006) and psychological (Margulis et al., 2023; Paluska & Schwenk, 2000; Ranjbar et al., 2015) health, the PA level of adults and college students worldwide remains insufficient (Piercy et al., 2018; World Health Organization, 2024). In Particular, according to a recent nationwide survey in South Korea, less than 25% of young Korean adults regularly participate in structured leisure sports, and more than half of men and two-thirds of women in their 20s are completely inactive (Ministry of Culture, Sports, & Tourism, 2022, 2023). Given that sedentary lifestyles can lead to serious health risks such as cardiovascular disease, metabolic disorders, and psychological distress (Department of Health and Human Services, 1996; Guthold et al., 2018; Uddin et al., 2020), there is an urgent need to develop effective, scalable, and cost-effective intervention programmes to promote sustainable PA participation among young adults, especially college students. Mobile health (mHealth) interventions are emerging as promising alternatives because they are more scalable and economical than traditional face-to-face PA promotion programmes and can provide personalised real-time feedback and motivational prompts (Buckingham et al., 2019; Direito et al., 2017; Sequi-Dominguez et al., 2020). Recent meta-analytical evidence supports the overall efficacy of mHealth interventions, which have been shown to improve PA levels. However, the magnitude of the effect varies greatly, depending on the intervention design, context, and cultural suitability (Tong et al., 2024; Mönninghoff et al., 2021). More specifically, Tong et al. (2024) found that mHealth interventions that combine culturally suitable and contextually customised messages with active participation strategies, such as regular notification and goal-setting functions, are particularly effective in promoting sustainable PA. In addition, a systematic literature review and meta-analysis by Mönninghoff et al. (2021) reported that mHealth interventions showed significant improvement in various outcome indicators such as walking, moderate-to-high-intensity PA, and overall PA, and this effect continued in short-term (up to six months) and long-term (six months or longer) follow-up observations. However, despite these positive results, there is a lack of research on how to optimally apply specific behavioural theories in a digital environment to strengthen message design and delivery and increase the persistence of changes in PA-related behaviour. Therefore, to maximise the potential of mobile-based PA interventions, an in-depth and systematic study of theory-based message framing and intervention delivery methods is required. Goal-setting is widely recognised as a key strategy for promoting PA participation with consistently proven effectiveness in various population groups and contexts (McEwan et al., 2016; Howlett et al., 2019). Traditionally, specific, measurable, achievable, realistic, and time-limited (SMART) goals have been mainstream in PA intervention. However, recent studies have pointed out limitations such as SMART goals increasing psychological pressure and anxiety and reducing internal motivation, especially for PA or for novice athletes (Swann & Rosenbaum, 2018; Swann et al., 2023). The empirical evidence accumulated through recent experimental studies shows that open goals such as ‘see how much you can walk’ and non-specific goals such as ‘do your best’ not only have the same effect as SMART goals in increasing PA but also greatly promote psychological benefits such as enjoyment, autonomy, and continuous participation (Hawkins et al., 2023, 2024; Swann et al., 2020; Goddard et al., 2025). In fact, recent feasibility studies show that open goals can be successfully integrated into longer-term intervention programmes, effectively reducing negative emotions such as pressure, anxiety, and failure and increasing the continuity of participants’ PA (Goddard et al., 2025). Despite the accumulated evidence on the goal-setting method, there is still a remarkable research gap. In the context of promoting PA, there is a relative lack of empirical investigations of mastery-oriented (learning-oriented) goals (Swann et al., 2021). Mastery goals, central to Achievement Goal Theory (AGT), emphasise personal improvement, skill acquisition, intrinsic motivation, and enjoyment, aligning theoretically with the psychological benefits identified for open goals. Although AGT has extensively validated mastery goals as effective for promoting sustained motivation, self-efficacy, and positive psychological outcomes (Ames, 1992; Dweck, 1999; Nicholls, 1989; Standage et al., 2003; Wang et al., 2016), few studies have explicitly tested their effectiveness within mobile-based PA promotion interventions. The theoretical and practical alignment between mastery goals and beneficial psychological outcomes for individuals suggests an urgent need to empirically explore their effectiveness in mobile health (mHealth) contexts, providing clearer guidance for PA interventions. AGT provides a convincing theoretical framework for understanding the motivational process in the context of achievement, such as participation in PA (Ames, 1992; Dweck, 1999; Dweck & Leggett, 1988; Nicholls, 1984, 1989). AGT divides individual motivation into two distinct goal orientations: mastery (self-improvement) and performance (social comparison and competition). Mastery-oriented individuals judge success using internal criteria such as personal progress, improvement, and internal satisfaction, which promote internal motivation and continuity of action (Dweck, 1999; Nicholls, 1989). By contrast, performance-oriented individuals evaluate success through external criteria, such as surpassing others or gaining social recognition, and may experience increased anxiety and reduce the sustainability of behaviour because of their dependence on external validation (Pintrich, 2000). Previous studies have consistently demonstrated that mastery-oriented individuals have high levels of internal motivation, persistence, positive emotions, and persistent participation in PA. By contrast, performance-oriented individuals who want to surpass their peers or gain external recognition often show maladaptive results, such as increased anxiety, irregular participation, and low persistence of behavioural changes, because they rely on external reinforcement (Biddle et al., 2003; Standage et al., 2003; Wang et al., 2010, 2016; Yang et al., 2024). For example, Standage et al. (2003) reported that a mastery climate positively affected students’ self-deterministic motivation and intention to participate in PA. Similarly, Wang et al. (2010, 2016) found that students with mastery-oriented goals had higher internal motivation than students with performance-oriented goals and were less sensitive to changes in motivation because of external feedback or competition results; therefore, they maintained more consistent PA participation. Although these findings theoretically and practically suggest that continuous participation in PA can be promoted by emphasising mastery-oriented goals and the environment in PA intervention programmes, few studies have applied this theory to an mHealth environment and verified its effectiveness. Specifically, there is a lack of understanding of the feasibility and practical mechanisms by which AGT-based synchronous messages delivered through mobile technology promote continuous PA participation. Moreover, empirical evidence comparing the relative effectiveness of delivering mastery- and performance-oriented messages through mobile nudging technology has not been sufficiently presented. AGT has a clear goal orientation that can be transformed directly into a clear, concise, and actionable mobile message, providing unique advantages in a mobile environment. Mastery-oriented messages can be tailored to promote personal development, autonomy, and internal motivation, whereas performance-oriented messages can take advantage of the motivational potential of social comparison and competition common on digital platforms. This direct transition from theoretical to practical mobile messages represents a significant improvement over existing AGT application methods, which make it difficult to provide individualised continuous synchronous feedback. Therefore, this study primarily aimed to compare the relative effectiveness of AGT-based mastery- and performance-oriented messages delivered through mobile nudging interventions in increasing Korean university students’ participation in PA. We hypothesised that mastery-oriented motivational messages would more effectively improve continuous walking behaviour, as measured by duration, distance, and frequency, compared to the performance-oriented group and control conditions in which motivation prompts were not provided. This study also aimed to provide specific and actionable guidance for public health initiatives aimed at solving the physical inactivity problem in college students by clarifying the practical mechanisms of AGT-based mobile interventions and directly comparing different types of motivational messages. These findings can serve as useful fundamental data for the future development of customised, theory-based mHealth intervention programmes that can promote sustainable PA both within and outside Korea. Methods Study design The study was conducted for nine weeks using a parallel-group randomised controlled trial design, with the first week being the baseline measurement period followed by an eight-week intervention period. The participants were randomly assigned to one of three conditions (Figure 1): 1) a mastery-oriented intervention group (self-improvement target message), 2) a performance-oriented group (competition and comparison target messages), and 3) a control group (no motivational message). Randomisation was performed using a stratified block randomisation procedure, and a balanced distribution between each group was obtained by considering both the initial walking duration (baseline data measured during the first week) and sex. The randomisation procedure was as follows: After the initial baseline measurement week, the total walking duration (in seconds) of the participants was calculated using objective data from the Nike Run Club mobile application. After the participants were ranked based on their baseline walking duration, they were divided into homogeneous subgroups according to their walking activity level using quartiles: Low (25 th percentile), medium (50 th percentile), and high (75 th percentile). The participants were further stratified according to sex (male or female) within each walking duration subgroup to balance the sex distribution between each intervention group. After stratification by baseline activity level and sex, the participants were randomly assigned within a small fixed size (three persons) block to maintain the same group size. Within each block, a participant was randomly assigned to one of three conditions (mastery-oriented, performance-oriented, or control). An independent researcher generated a randomised assignment order using a computer-based random number generator, thus ensuring unbiased group assignments. The researchers in charge of data collection and analysis remained blinded to the participants’ group assignments until the end of the study. However, owing to the nature of the intervention, the participants were informed of their group assignments. [Insert Figure 1] Research ethics issues were thoroughly observed and all research procedures were approved by the institutional review board of the first author’s institution (HIRB20241017-003). In addition, participants provided written consent prior to randomisation, and in this process, they were clearly informed about their rights, confidentiality, research objectives, intervention requirements, potential risks, and right to withdraw from the study. Participants were informed that they could withdraw from the study whenever they wanted without disadvantage. All participants received 50,000 Korean won (36.44 US dollars) as compensation for their participation. Sample size calculation The appropriate sample size was calculated using G*Power program (version 3.1.9.4; Faul et al., 2007). A significance level of 0.05, a statistical power of 95%, and an effect size of 0.72 were set, based on data from previous studies examining the effect of AGT on PA adherence (Yang et al., 2024). Based on this calculation, at least 36 participants were required to achieve the desired power. Considering the 57% dropout rate (Richards & Richards, 2012) reported in existing computer-based psychotherapy studies, the total required sample size was 56. Therefore, the statistical power of this study was expected to be sufficiently secured by recruiting 87 participants. Participants The participants were university students recruited through posters on campus and online social networking sites. A total of 92 undergraduate students were recruited for this study. However, five students were excluded from the experiment because they did not meet the inclusion criteria and had personal illnesses. Thus, a final tally of 87 undergraduate students participated in baseline evaluations. The inclusion criteria for the study were as follows: (1) undergraduate students aged 18–30 years, (2) smartphone owners who had installed and executed the Nike Run Club application, (3) individuals without physical disabilities that interfere with moderate-intensity walking activities, and (4) willingness and ability to voluntarily measure and report PA through mobile applications. The exclusion criteria were as follows: (1) having significant restrictions on walking due to cardiovascular, respiratory, or orthopaedic diseases; (2) current participation in regular exercise programmes or other intervention studies; (3) refusal to comply with the data collection procedures; and (4) difficulty or lack of persistence in using the required mobile apps. Baseline walking duration Baseline activity levels were measured in the first week of the study (Week 0). Walking activity was recorded as objective data using the Nike Run Club application, and the recorded data included walking duration (seconds), distance (kilometres), and frequency (number of valid walking sessions). A valid walking session (successive walk) was defined as walking that lasted for more than 15 min and covered a total distance of 1 km or more. Participants were grouped by summing the total walking duration (seconds) of valid sessions during the one-week baseline period and then randomly assigned to the intervention or control group. Procedure After providing consent to participate in the study, the participants installed the Nike Run Club application on their smartphones and received training on how to use it to ensure consistency and accuracy in data collection. At the initial baseline measurement (Week 0), data on demographic information, self-reported physical health status, Internet usage habits, fitness application usage experience, and walking activity level were collected. During the intervention period (Weeks 1–8), each participant’s walking activity was continuously monitored using the mobile application. As soon as each walking session was completed, participants submitted the walking activity screen (screenshot) recorded on the application to the researchers. The research assistant reviewed the screenshots submitted daily to check participants’ compliance, checked the validity of each session, and recorded them in a shared database based on Google Sheets. The researchers also regularly conducted checks to maintain the participants’ motivation, check whether they used the app, solved technical problems, continued to participate, and secured data accuracy through KakaoTalk messages once a week. Intervention The mastery-oriented group received customised nudge messages that focused on self-improvement, personal growth, and internal motivation (e.g. ‘It was difficult to walk for less than a mile at first, but now it has improved a lot. Shall we break personal records this week?’). The performance-oriented group received customised messages to stimulate external motivations through social comparison and competition (e.g., ‘The amount of walking activity this week is above average. Try to outperform the other group!’). Finally, the control group received no separate nudging or motivational messages; only the application was used for self-observation. To develop intervention messages systematically, existing literature on AGT was first extensively reviewed and integrated, focusing on two target structures: mastery and performance goals. Based on the literature review, draft motivational messages consistent with the intervention strategy were developed to conform to each theoretical component. Subsequently, two professors from the fields of sports and exercise psychology reviewed the draft messages and evaluated their theoretical adequacy and suitability. The experts determined whether each message had clarity, accuracy, and compatibility with the theoretical components of the AGT and provided feedback. The content was elaborated to maintain the face validity and theoretical fidelity of the messages by reflecting the proposed modifications. During the eight weeks of the intervention period, messages were sent to participants thrice a day (10:00 a.m., 1:00 p.m., and 6:00 p.m.), alternating three days a week (odd weeks: Monday, Wednesday, Friday; even weeks: Tuesday, Thursday, Saturday) via a social network messenger (KakaoTalk). The participants were encouraged to actively interact with these messages and apply their content to their daily walking activities. Outcome measure The main outcome was the cumulative walking duration (seconds) during the eight-week intervention period. Secondary outcomes were total walking distance (km) and frequency of walking sessions (number of valid walking sessions). All result variables were objectively measured using the Nike Run Club application, which accurately recorded each participant’s walking behaviour based on global positioning system. Experienced research assistants checked all data weekly to ensure accuracy and completeness. Statistical analyses The collected data were analysed using IBM SPSS Statistics 28.0 (IBM Corp., Armonk, NY, USA). Participants’ demographic characteristics and baseline PA data were presented as mean ( M ) ± standard deviation ( SD ), frequency, and percentage through descriptive statistical analysis. Prior to inferential analysis, the data were checked for normality, homogeneity of variance, and outliers. A one-way ANOVA was performed to compare differences in total walking duration, walking distance, and session frequency between the mastery-oriented, performance-oriented, and control groups. When the ANOVA results were significant, pairwise comparisons using Tukey’s post hoc test were performed to determine which groups showed significant differences. Results Participant characteristics at baseline The demographic and baseline characteristics of the participants were analysed using descriptive statistics (Table 1). The average age of the mastery-oriented group ( n = 29) was 22.07 years old ( SD = 1.94), average daily internet use time was 322.41 min ( SD = 309.84), internet use confidence score was 3.69 ( SD = 0.76), and self-reported physical health score was 3.72 ( SD = 0.70). The average age of the performance-oriented group ( n = 29) was 22.83 years ( SD = 2.25), internet use time was 318.97 min ( SD = 164.89), internet use confidence score was 3.83 ( SD = 0.76), and self-reported physical health score was 3.48 ( SD = 0.83). The average age of the control group ( n = 29) was 22.86 years old ( SD =1.75), internet usage time was 265.52 min ( SD = 113.22), internet use confidence score was 3.83 ( SD = 0.85), and perceived physical health score was 3.59 ( SD = 0.91). The chi-square analysis showed no statistically significant differences between the three groups in terms of sex distribution ( p = .95) and previous experience using the PA application ( p = .79). In addition, the one-way ANOVA showed that participants’ age ( F 2,84 = 1.46, p = .24), Internet use time ( F 2,84 = 0.65, p =. 52), Internet use confidence ( F 2,84 = 0.29, p = .75), perceived physical health ( F 2,84 = 0.63, p = .53), walking duration ( F 2,84 = 0.33, p = .81), walking distance ( F 2,84 = 0.291, p = .92), and walking frequency ( F 2,84 = 0.30, p = .96) were homogeneous at baseline and that the differences observed after the intervention were due to the intervention’s effects rather than the existing characteristics. Main results Walking duration, distance, and frequency during the eight-week intervention period were compared between the three groups using one-way ANOVA (Table 2). Significant differences between the groups were found in total walking duration ( F 2,84 = 5.52, p = .006, η² = .12). The average walking duration was longer in the mastery-oriented group ( M = 26,519.44 s, SD = 23,175.59 s) than in the performance-oriented ( M = 21,294.72 s, SD = 12,169.28 s) and control ( M = 12,685.55 s, SD = 9,177.42 s) groups. The average walking distance was also significantly different between the groups ( F 2,84 = 6.44, p = .003, η² = .13), with the mastery-oriented group ( M = 37.42 km, SD = 34.23 km) walking the longest distance, followed by the performance-oriented ( M = 28.59 km, SD = 15.61 km), and control ( M = 16.15 km, SD = 11.30 km) groups. Finally, the average walking frequency per week was significantly different between the groups ( F 2,84 = 4.06, p = .021; η² = .09). The walking frequency of the mastery-oriented group ( M = 15.06, SD = 8.27) was higher than that of the performance-oriented group ( M = 14.10, SD = 8.80), and significantly higher than that of the control group ( M = 9.34, SD = 7.44). The additional post-hoc Tukey analysis revealed that the mastery-oriented group showed significantly higher results in walking duration (general mean difference [GMD] = 13,833.90 s, p = .004), total walking distance (GMD = 21.27 km, p = .002), and walking frequency (GMD = 5.72, p = .025) than the control group. However, the difference between the mastery- and performance-oriented groups and between the performance-oriented and control groups was not statistically significant for any of the three outcome variables ( p > .05). Changes in weekly walking duration, distance, and frequency over the eight-week intervention period for each group are illustrated in Figure 2. Discussion This study verified whether motivational messages of mastery and performance promote college students’ PA using an AGT-based mHealth intervention programme. This study contributed to the literature related to PA by revealing the differential effects of motivational messages for mastery and performance orientation on the walking activity of college students. The findings indicated significant differences between the groups in walking duration, distance, and frequency, and partially supported our hypothesis. Specifically, the mastery-oriented group walked significantly longer, farther, and more frequently than the control group, proving that mastery-oriented motivational messages were effective in promoting sustainable PA. However, the performance-oriented group did not show a significant improvement in walking compared to the control group. Furthermore, the difference between the mastery- and performance-oriented groups was not statistically significant; only the mastery-oriented group showed a significant improvement in PA compared to the control group. Main findings Consistent with existing AGT studies (Biddle et al., 2003; Standage et al., 2003; Wang et al., 2010, 2016; Yang et al., 2024), mastery-oriented motivational messages significantly improved the walking behaviour of participants compared to the control group, but did not significantly outperform performance-oriented messages. This pattern suggests that in an mHealth context, mastery-oriented framing may be particularly effective in walking behaviour above a no-intervention baseline, while added value over performance framing remains uncertain. The psychological mechanisms underlying these results can be explained by an increase in intrinsic motivation, perceived competence, and autonomy related to the pursuit of mastery-oriented goals. Specifically, mastery-oriented messages emphasise internal criteria such as personal growth, effort, self-improvement, and self-satisfaction for success, which deepens inner motivation (Nicholls, 1984, 1989; Dweck, 1999). Intrinsic motivation is a key factor that promotes enjoyment, interest, and immersion in PA regardless of external reinforcement or social comparison (Wang et al., 2010, 2016). In addition, the mastery-oriented approach is effective in improving sense of competence and autonomy perception by consistently emphasising individual progress, efforts, and development rather than external results (Bentia et al., 2014; Cho et al., 2011). Such messages can increase the self-efficacy and personal satisfaction of individuals, thereby strengthening intrinsic motivation and increasing the likelihood of continuing the target behaviour (Wang et al., 2010, 2016; Yang et al., 2024). Even from a self-determination theory perspective, mastery-oriented messages can satisfy basic psychological needs, such as autonomy, competence, and relationships (Bentia et al., 2014; Cho et al., 2011). A mastery-oriented approach that continuously emphasises individual effort and progress could have provided this study’s participants with the opportunity to participate autonomously in walking, strengthened their personal competence through self-referential achievements, and enhanced relationships through motivational experiences shared within a mobile context (Nicholls, 1984, 1989; Dweck, 1999; Standage et al., 2003). Wang et al. (2010, 2016) and Yang et al. (2024) reported that creating a mastery-oriented environment effectively promotes sustainable PA by strengthening internal and self-deterministic motivations. The present results are particularly consistent with those of a recent study emphasizing the potential limitations of traditional specific goal-setting methods (SMART goals; Hawkins et al., 2023; Swann & Rosenbaum, 2018; Swann et al., 2023). According to an increasing number of studies, specific and challenging performance goals (e.g., SMART goals) increase psychological pressure and anxiety and reduce internal motivation in people in the early stages of PA, thereby reducing the persistence of long-term behavioural changes (Swann & Rosenbaum, 2018; Swann et al., 2023; Hawkins et al., 2023, 2024). In fact, non-specific goals such as ‘open goals’ or ‘do your best goals’ are emerging as effective alternatives to promote PA participation, increase positive psychological experiences such as pleasure and autonomy, and reduce feelings of failure or guilt (Swann et al., 2020; Hawkins et al., 2023, 2024; Goddard et al., 2025). Above all, this study further expands the relevant knowledge base by bridging the existing research gap pointed out in a goal-setting study (Swann et al., 2021), showing that mastery-oriented (learning) goal-setting is not only theoretically valid but also superior to the case of no goal-setting in actually increasing participation in PA. Behavioural economics theory provides additional insights into the results of this study. Behavioural economics emphasises the specific synchronisation role of social incentives, goal gradients, pre-commitment, and loss avoidance (van Mierlo et al., 2016; Zimmerman, 2009), and it is highly likely that mastery-oriented messages effectively utilise these mechanisms. In other words, mastery-oriented messages would have greatly strengthened the motivational effect on the control group by clearly defining gradual personal growth (goal gradient), emphasising consistent progress towards the goals set (pre-commitment strategy), and highlighting potential losses if established activity behaviour was not continued (loss avoidance; Shuval et al., 2017; Thaler, 2016; van Mierlo et al., 2016; Vlaev et al., 2019; Zimmerman, 2009). An important theoretical implication of this study is that it expands existing knowledge about the positive role of a mastery-oriented environment, from a traditional face-to-face or structured education and sports environment to an mHealth environment. Existing AGT studies have reported that mastery-oriented atmospheres positively affect motivation and behaviour mainly in classrooms (Bentia et al., 2014; Cho et al., 2011), PA (Ntoumanis & Biddle, 1999), sports teams (Ntoumanis, 2001), and structured PA environments (Standage et al., 2003). Our study showed that mastery-oriented messages delivered through mobile technology can effectively sustain participants’ internal motivation and PA participation without direct social interaction or the presence of visible peers. Therefore, this study further expands the applicability of AGT, demonstrates its theoretical robustness in various environments, and reaffirms that mastery-oriented motivational strategies can be effectively utilised in scalable digital health interventions to promote sustainable PA. By contrast, performance-oriented motivational messages centred on social comparison and competition did not significantly increase walking behaviour compared with the control group. According to AGT, performance-oriented goals are highly dependent on external verification and explicit competitive situations, which can increase anxiety and lower internal motivation, eventually limiting sustainable behavioural changes (Nicholls, 1984, 1989; Dweck, 1999; Yang et al., 2024). Therefore, the fact that performance-oriented interventions did not show significant results in this study is consistent with AGT’s theoretical prediction, revealing the potential limitations of external motivation strategies in a mobile-based context. However, the results of this study significantly differ from those of Fortunato et al. (2019), which proved the effectiveness of competition-based gamification intervention in increasing PA. The main reason for this could be the difference in the competitive contexts used in the two studies. Fortunato et al. (2019) used explicit and visible competitive factors such as real-time leaderboards, clear rankings, and specific and visible competitive comparisons between participants. This explicit social comparison effectively increased participants’ instantaneousness, urgency, and competitive needs based on behavioural economic principles, such as loss aversion, goal gradients, and commitment strategies. By contrast, the performance-oriented group in this study received motivational messages based on implicit and abstract competition with the ‘invisible’ mastery-oriented group, which lacked explicit visibility or specific social feedback. According to AGT and related psychological literature, competitive motives rely heavily on explicit and specific social comparisons, such as classmates, team members, or clearly presented competitors, which are essential to effectively promote motives and behaviours (Dweck, 1999; Pintrich, 2000). Because there were no specific comparisons, it was difficult for participants to vividly recognise their competitive position, which may have reduced competitive motivation, perceived urgency, and the potential benefits of performance-oriented target strategies. This comparison emphasises the importance of explicit and specific competitive framing, particularly in maximising the motivational potential of performance-oriented messages in mobile-based arbitration. Finally, this study contributes to the development of the mHealth field by addressing major research gaps related to the optimal application of behavioural theory in mHealth interventions to promote PA. Existing meta-analyses and systematic literature reviews have demonstrated that mHealth interventions effectively improve PA (Tong et al., 2024; Mönninghoff et al., 2021), but limitations that significant variability exists in the results according to intervention design, context, and cultural suitability are presented. Tong et al. (2024) emphasised that interventions that combine culturally customised and context-specific messages with strong participatory strategies, such as goal-setting and regular notifications, are effective in promoting continuous changes in PA. In addition, Mönninghoff et al. (2021) reported the short- and long-term effects of mHealth interventions on various PA indicators, such as walking, but emphasised the need for a theory-based approach to maximise the efficacy of the intervention. This study successfully integrated AGT with mobile-based messaging and directly addressed this research gap. In particular, by showing that mastery-oriented messages emphasising internal motivation and self-development can effectively increase the PA of college students, the importance of designing motivational messages based on theory was empirically proven. These findings can provide practical guidance for the development and improvement of mHealth interventions that are theory-oriented, culturally appropriate, and contextual, considering the sustainable improvement of PA in the future. Practical implications The results provide useful guidance for designing effective mHealth interventions that promote PA among college students. As mastery-oriented motivational messages have proven to be effective, intervention designers, health promotion experts, and mobile app developers must prioritise strategies that emphasise intrinsic motivation, personal development, and self-referenced goals. These customised motivational messages can be easily integrated into smartphone applications to provide continuous personalised encouragement to users and promote sustainable PA participation. Educational institutions and public health initiatives can practically apply these findings by incorporating mastery-oriented nutsedge messages into existing digital wellness platforms and campus-based wellness programmes. Mobile platforms can promote consistent PA through automated personalised notifications that emphasise milestones in individual growth, improvement, and intrinsic rewards. This approach, which combines theoretically elaborated message strategies with behavioural economics principles, provides an effective and scalable method of promoting PA, which in turn can contribute to the improvement of PA among college students. Limitations and future research directions Despite the significant results of this study, it has some limitations that future studies should address. First, as this study used self-report measurements and mobile app-based activity data, there may have been data inaccuracies owing to social desirability bias or technical problems. Future research should increase the accuracy of the data by using an objective measurement tool, such as an accelerometer or pedometer. Second, the intervention period in this study was relatively short (eight weeks). Although significant improvements were observed, long-term studies are needed to evaluate whether these effects persist after the intervention period. Extending the duration of the intervention may lead to more pronounced differences between mastery- and performance-oriented messages, thus providing in-depth insights into the sustainability of behavioural change strategies. Third, the participants were mainly recruited from one university; therefore, the generalisability of the results is limited. Future studies should increase the generalisability and robustness of the results by including groups with various cultural and academic backgrounds. Fourth, the specific mechanism of the observed motivational effects was not directly evaluated. Future research should clarify psychological mechanisms through mediational analyses to explain why certain motivational messages show better results. Understanding these psychological mediators will enable interventions that are more goal-oriented and sophisticated. Fifth, although randomisation and baseline equivalence were achieved, the study may have been underpowered to detect small-to-moderate differences between the mastery- and performance-oriented groups. Additionally, participant engagement with the motivational messages (e.g., reading rate, perceived relevance) was not monitored, which could influence the observed effects. Finally, research using qualitative methods, such as in-depth interviews or focus groups, can provide an in-depth understanding of participants’ subjective experiences with intervention programmes, and the effect of motivational messages on PA can be more clearly understood, allowing researchers and practitioners to refine their intervention strategies more precisely. Conclusion This study demonstrated that AGT-based mobile interventions focusing on mastery-oriented messages can effectively improve college students’ PA compared to no-intervention controls. While mastery-oriented messages improved walking duration, distance, and frequency over control, they did not significantly outperform performance-oriented messages, and performance-oriented messages did not produce significant gains relative to control. Practitioners may prioritise the mastery-oriented approach for sustainable PA participation targeting young adults. Performance-oriented strategies may still be considered as auxiliary tools depending on the situation and individual characteristics. Future studies should increase the duration of intervention, include objective PA measurement methods, study various groups, and explore psychological mechanisms to optimise the effectiveness and persistence of synchronous interventions to promote PA. Declarations Funding Statement This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) (NRF-2021M3A9E4080780) and Hankuk University of Foreign Studies (2025). Author contributions YH.L. and HS.K. contributed to conception and design of the study. YH.L. collected and analyzed the data and wrote the first draft of the manuscript. HS.K. contributed to reviewing and editing the manuscript. HS.K. contributed to funding acquisition. All authors read and approved the submitted version. Ethical Approval: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional review board of Hankuk University of Foreign Studies and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval was granted by the Institutional Review Board of Hankuk University of Foreign Studies (Approval No. HIRB20241017-003, approved on October 17, 2024). Informed Consent: Written informed consent was obtained from all participants prior to their participation in the study. After randomization, the researchers contacted participants via the Webex platform (Cisco) to conduct baseline evaluations at Week 0, held on October 25 and 26. During the baseline evaluation, participants were provided with detailed information about the study’s objectives, procedures, potential risks, and privacy safeguards, along with assurances regarding confidentiality and the voluntary nature of participation. Participants were instructed to complete a web-based questionnaire covering demographic information, self-reported physical health status, Internet usage habits, fitness application usage experience, and baseline walking activity. Participants then provided written consent electronically, confirming their understanding of the study procedures and their right to withdraw at any time without penalty. Data availability statement The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests References Ames C (1992) Classrooms: goals, structures, and student motivation. 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PLoS One 19(12):e0315831. https://doi.org/10.1371/journal.pone.0315831 Zimmerman FJ (2009) Using behavioral economics to promote physical activity. Prev Med 49(4):289–291. https://doi.org/10.1016/j.ypmed.2009.07.008 Tables Table 1. Demographic characteristics Characteristics Mastery ( n = 29) Performance ( n = 29) Control ( n = 29) p n (%) n (%) n (%) Categorical variables Gender .95 Male 16 (55.2) 17 (58.6) 16 (55.2) Female 13 (44.8) 12 (41.4) 13 (44.8) Experience in mobile-based physical activity app .79 Yes 6 (20.7) 4 (13.8) 5 (17.2) No 23 (79.3) 25 (86.2) 24 (82.8) Continuous variables (M±SD) Age 22.07±1.94 22.83±2.25 22.86±1.75 .24 Internet usage (min) 322.41±309.83 318.97±164.88 302.30±212.05 .52 Internet confidence 3.69±0.76 3.83±0.76 3.83±0.84 .75 Perceived physical health 3.72±0.72 3.48±0.83 3.59±0.90 .53 Baseline walking duration (s) 3973.34±3316.84 3508.31±3462.39 3468.00±3127.53 .81 Baseline walking distance (km) 5.42±5.62 4.93±5.19 4.95±4.90 .92 Baseline walking frequency 2.10±1.26 2.00±1.75 2.07±1.53 .96 Table 2. ANOVA results and post-hoc comparisons for walking duration, distance, and frequency Mastery ( n = 29) Performance ( n = 29) Control ( n = 29) F P η² Post-hoc comparison GMD p M ( SE ) M ( SE ) M ( SE ) Average walking duration (s) 26,519.44 (23,175.59) 21,294.72 (12,169.28) 12,685.55 (9,177.42) 5.52 .006** .12 Mastery vs. Control 13,833.89 .04* Mastery vs. Performance 5,224.72 .43 Performance vs. Control 8609.17 .10 Average walking distance (km) 37.42 (34.23) 28.59 (15.61) 16.15 (11.30) 6.44 .003** .13 Mastery vs. Control 21.27 .002** Mastery vs. Performance 8.83 .30 Performance vs. Control 12.44 .09 Average walking frequency (session per week) 15.06 (8.27) 14.10 (8.80) 9.34 (7.44) 4.06 .021* .09 Mastery vs. Control 5.72 .02* Mastery vs. Performance 0.96 .89 Performance vs. Control 4.75 .08 ** p < .01, * p < .05. GMD = group mean difference; SE = standard error Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7189266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":574108263,"identity":"f319b65f-5954-46c3-88b3-79e6fee117bc","order_by":0,"name":"Ye Hoon Lee","email":"","orcid":"","institution":"Hankuk University of Foreign Studies","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"Hoon","lastName":"Lee","suffix":""},{"id":574108264,"identity":"c199c142-708c-4377-bd71-0384cd795996","order_by":1,"name":"Hyungsook Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACCQkQeUBCjl8CSZSxgbAWC2PJGSRqqUjccINYLZKz2x8++HBGgnHz7eZnj3lq7tg1sB9+wDhzD24t0jJnjA1n3JBgNrtzzNyY59iz5AaeNAPGDc9wa5GTyGGT5vkgwWZ2I8FMmoftcDIDQw4D44MD+LSkPwNp4TGekf5NmucfUAv/G/xapCVAht+QkDCQyDGT5m07bMcgAbRlAx4tkjNygH45A9RxI6dMcm7f4QQ2iWcGB2fg0SJxIx0YYsfq6vtnpG+TePPtsD0/f/LDhz14tKAAJh4GhsQ2IINYDcAY/MHAYE+06lEwCkbBKBgxAAA031NxewVUgwAAAABJRU5ErkJggg==","orcid":"","institution":"Hanyang University","correspondingAuthor":true,"prefix":"","firstName":"Hyungsook","middleName":"","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-22 16:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7189266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7189266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100422832,"identity":"817f65ed-4b68-4c81-a9a0-29c709c44809","added_by":"auto","created_at":"2026-01-16 14:11:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178741,"visible":true,"origin":"","legend":"\u003cp\u003eResearch flow chart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7189266/v1/ad20019615fa4aba4c6e631c.png"},{"id":100422862,"identity":"a2dc8132-0b70-40e4-8c8d-f0531f1a49d5","added_by":"auto","created_at":"2026-01-16 14:11:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90200,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in weekly walking duration (seconds), distance (km), frequency across the eight-week intervention period by group\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7189266/v1/58ef809de1a07022bdcfdd29.png"},{"id":106399352,"identity":"40a598cf-0466-4450-916f-0fdd5f62ab76","added_by":"auto","created_at":"2026-04-08 08:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886191,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189266/v1/8780e8d9-41e3-45d7-b9ba-08e611a02c5c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of an achievement goal theory-based mobile intervention for increasing physical activity among college students: A randomised controlled trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlthough it is well known that regular physical activity (PA), even low PA, is beneficial to physical (Penedo \u0026amp; Dahn, 2005; Spartano et al., 2019; Warburton et al., 2006) and psychological (Margulis et al., 2023; Paluska \u0026amp; Schwenk, 2000; Ranjbar et al., 2015) health, the PA level of adults and college students worldwide remains insufficient (Piercy et al., 2018; World Health Organization, 2024). In Particular, according to a recent nationwide survey in South Korea, less than 25% of young Korean adults regularly participate in structured leisure sports, and more than half of men and two-thirds of women in their 20s are completely inactive (Ministry of Culture, Sports, \u0026amp; Tourism, 2022, 2023). Given that sedentary lifestyles can lead to serious health risks such as cardiovascular disease, metabolic disorders, and psychological distress (Department of Health and Human Services, 1996; Guthold et al., 2018; Uddin et al., 2020), there is an urgent need to develop effective, scalable, and cost-effective intervention programmes to promote sustainable PA participation among young adults, especially college students.\u003c/p\u003e\n\u003cp\u003eMobile health (mHealth) interventions are emerging as promising alternatives because they are more scalable and economical than traditional face-to-face PA promotion programmes and can provide personalised real-time feedback and motivational prompts (Buckingham et al., 2019; Direito et al., 2017; Sequi-Dominguez et al., 2020). Recent meta-analytical evidence supports the overall efficacy of mHealth interventions, which have been shown to improve PA levels. However, the magnitude of the effect varies greatly, depending on the intervention design, context, and cultural suitability (Tong et al., 2024; Mönninghoff et al., 2021). More specifically, Tong et al. (2024) found that mHealth interventions that combine culturally suitable and contextually customised messages with active participation strategies, such as regular notification and goal-setting functions, are particularly effective in promoting sustainable PA. In addition, a systematic literature review and meta-analysis by Mönninghoff et al. (2021) reported that mHealth interventions showed significant improvement in various outcome indicators such as walking, moderate-to-high-intensity PA, and overall PA, and this effect continued in short-term (up to six months) and long-term (six months or longer) follow-up observations. However, despite these positive results, there is a lack of research on how to optimally apply specific behavioural theories in a digital environment to strengthen message design and delivery and increase the persistence of changes in PA-related behaviour. Therefore, to maximise the potential of mobile-based PA interventions, an in-depth and systematic study of theory-based message framing and intervention delivery methods is required.\u003c/p\u003e\n\u003cp\u003eGoal-setting is widely recognised as a key strategy for promoting PA participation with consistently proven effectiveness in various population groups and contexts (McEwan et al., 2016; Howlett et al., 2019). Traditionally, specific, measurable, achievable, realistic, and time-limited (SMART) goals have been mainstream in PA intervention. However, recent studies have pointed out limitations such as SMART goals increasing psychological pressure and anxiety and reducing internal motivation, especially for PA or for novice athletes (Swann \u0026amp; Rosenbaum, 2018; Swann et al., 2023). The empirical evidence accumulated through recent experimental studies shows that open goals such as ‘see how much you can walk’ and non-specific goals such as ‘do your best’ not only have the same effect as SMART goals in increasing PA but also greatly promote psychological benefits such as enjoyment, autonomy, and continuous participation (Hawkins et al., 2023, 2024; Swann et al., 2020; Goddard et al., 2025). In fact, recent feasibility studies show that open goals can be successfully integrated into longer-term intervention programmes, effectively reducing negative emotions such as pressure, anxiety, and failure and increasing the continuity of participants’ PA (Goddard et al., 2025).\u003c/p\u003e\n\u003cp\u003eDespite the accumulated evidence on the goal-setting method, there is still a remarkable research gap. In the context of promoting PA, there is a relative lack of empirical investigations of mastery-oriented (learning-oriented) goals (Swann et al., 2021). Mastery goals, central to Achievement Goal Theory (AGT), emphasise personal improvement, skill acquisition, intrinsic motivation, and enjoyment, aligning theoretically with the psychological benefits identified for open goals. Although AGT has extensively validated mastery goals as effective for promoting sustained motivation, self-efficacy, and positive psychological outcomes (Ames, 1992; Dweck, 1999; Nicholls, 1989; Standage et al., 2003; Wang et al., 2016), few studies have explicitly tested their effectiveness within mobile-based PA promotion interventions. The theoretical and practical alignment between mastery goals and beneficial psychological outcomes for individuals suggests an urgent need to empirically explore their effectiveness in mobile health (mHealth) contexts, providing clearer guidance for PA interventions.\u003c/p\u003e\n\u003cp\u003eAGT provides a convincing theoretical framework for understanding the motivational process in the context of achievement, such as participation in PA (Ames, 1992; Dweck, 1999; Dweck \u0026amp; Leggett, 1988; Nicholls, 1984, 1989). AGT divides individual motivation into two distinct goal orientations: mastery (self-improvement) and performance (social comparison and competition). Mastery-oriented individuals judge success using internal criteria such as personal progress, improvement, and internal satisfaction, which promote internal motivation and continuity of action (Dweck, 1999; Nicholls, 1989). By contrast, performance-oriented individuals evaluate success through external criteria, such as surpassing others or gaining social recognition, and may experience increased anxiety and reduce the sustainability of behaviour because of their dependence on external validation (Pintrich, 2000). Previous studies have consistently demonstrated that mastery-oriented individuals have high levels of internal motivation, persistence, positive emotions, and persistent participation in PA. By contrast, performance-oriented individuals who want to surpass their peers or gain external recognition often show maladaptive results, such as increased anxiety, irregular participation, and low persistence of behavioural changes, because they rely on external reinforcement (Biddle et al., 2003; Standage et al., 2003; Wang et al., 2010, 2016; Yang et al., 2024). For example, Standage et al. (2003) reported that a mastery climate positively affected students’ self-deterministic motivation and intention to participate in PA. Similarly, Wang et al. (2010, 2016) found that students with mastery-oriented goals had higher internal motivation than students with performance-oriented goals and were less sensitive to changes in motivation because of external feedback or competition results; therefore, they maintained more consistent PA participation. Although these findings theoretically and practically suggest that continuous participation in PA can be promoted by emphasising mastery-oriented goals and the environment in PA intervention programmes, few studies have applied this theory to an mHealth environment and verified its effectiveness. Specifically, there is a lack of understanding of the feasibility and practical mechanisms by which AGT-based synchronous messages delivered through mobile technology promote continuous PA participation. Moreover, empirical evidence comparing the relative effectiveness of delivering mastery- and performance-oriented messages through mobile nudging technology has not been sufficiently presented.\u003c/p\u003e\n\u003cp\u003eAGT has a clear goal orientation that can be transformed directly into a clear, concise, and actionable mobile message, providing unique advantages in a mobile environment. Mastery-oriented messages can be tailored to promote personal development, autonomy, and internal motivation, whereas performance-oriented messages can take advantage of the motivational potential of social comparison and competition common on digital platforms. This direct transition from theoretical to practical mobile messages represents a significant improvement over existing AGT application methods, which make it difficult to provide individualised continuous synchronous feedback.\u003c/p\u003e\n\u003cp\u003eTherefore, this study primarily aimed to compare the relative effectiveness of AGT-based mastery- and performance-oriented messages delivered through mobile nudging interventions in increasing Korean university students’ participation in PA. We hypothesised that mastery-oriented motivational messages would more effectively improve continuous walking behaviour, as measured by duration, distance, and frequency, compared to the performance-oriented group and control conditions in which motivation prompts were not provided.\u003c/p\u003e\n\u003cp\u003eThis study also aimed to provide specific and actionable guidance for public health initiatives aimed at solving the physical inactivity problem in college students by clarifying the practical mechanisms of AGT-based mobile interventions and directly comparing different types of motivational messages. These findings can serve as useful fundamental data for the future development of customised, theory-based mHealth intervention programmes that can promote sustainable PA both within and outside Korea.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted for nine weeks using a parallel-group randomised controlled trial design, with the first week being the baseline measurement period followed by an eight-week intervention period. The participants were randomly assigned to one of three conditions (Figure 1): 1) a mastery-oriented intervention group (self-improvement target message), 2) a performance-oriented group (competition and comparison target messages), and 3) a control group (no motivational message). Randomisation was performed using a stratified block randomisation procedure, and a balanced distribution between each group was obtained by considering both the initial walking duration (baseline data measured during the first week) and sex. The randomisation procedure was as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter the initial baseline measurement week, the total walking duration (in seconds) of the participants was calculated using objective data from the Nike Run Club mobile application. After the participants were ranked based on their baseline walking duration, they were divided into homogeneous subgroups according to their walking activity level using quartiles: Low (25\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile), medium (50\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile), and high (75\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile). The participants were further stratified according to sex (male or female) within each walking duration subgroup to balance the sex distribution between each intervention group. After stratification by baseline activity level and sex, the participants were randomly assigned within a small fixed size (three persons) block to maintain the same group size. Within each block, a participant was randomly assigned to one of three conditions (mastery-oriented, performance-oriented, or control). An independent researcher generated a randomised assignment order using a computer-based random number generator, thus ensuring unbiased group assignments. The researchers in charge of data collection and analysis remained blinded to the participants’ group assignments until the end of the study. However, owing to the nature of the intervention, the participants were informed of their group assignments.\u003c/p\u003e\n\u003cp\u003e[Insert Figure 1]\u003c/p\u003e\n\u003cp\u003eResearch ethics issues were thoroughly observed and all research procedures were approved by the institutional review board of the first author’s institution (HIRB20241017-003). In addition, participants provided written consent prior to randomisation, and in this process, they were clearly informed about their rights, confidentiality, research objectives, intervention requirements, potential risks, and right to withdraw from the study. Participants were informed that they could withdraw from the study whenever they wanted without disadvantage. All participants received 50,000 Korean won (36.44 US dollars) as compensation for their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe appropriate sample size was calculated using G*Power program (version 3.1.9.4; Faul et al., 2007). A significance level of 0.05, a statistical power of 95%, and an effect size of 0.72 were set, based on data from previous studies examining the effect of AGT on PA adherence (Yang et al., 2024). Based on this calculation, at least 36 participants were required to achieve the desired power. Considering the 57% dropout rate (Richards \u0026amp; Richards, 2012) reported in existing computer-based psychotherapy studies, the total required sample size was 56. Therefore, the statistical power of this study was expected to be sufficiently secured by recruiting 87 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants were university students recruited through posters on campus and online social networking sites. A total of 92 undergraduate students were recruited for this study. However, five students were excluded from the experiment because they did not meet the inclusion criteria and had personal illnesses. Thus, a final tally of 87 undergraduate students participated in baseline evaluations. The inclusion criteria for the study were as follows: (1) undergraduate students aged 18–30 years, (2) smartphone owners who had installed and executed the Nike Run Club application, (3) individuals without physical disabilities that interfere with moderate-intensity walking activities, and (4) willingness and ability to voluntarily measure and report PA through mobile applications. The exclusion criteria were as follows: (1) having significant restrictions on walking due to cardiovascular, respiratory, or orthopaedic diseases; (2) current participation in regular exercise programmes or other intervention studies; (3) refusal to comply with the data collection procedures; and (4) difficulty or lack of persistence in using the required mobile apps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline walking duration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline activity levels were measured in the first week of the study (Week 0). Walking activity was recorded as objective data using the Nike Run Club application, and the recorded data included walking duration (seconds), distance (kilometres), and frequency (number of valid walking sessions). A valid walking session (successive walk) was defined as walking that lasted for more than 15 min and covered a total distance of 1 km or more. Participants were grouped by summing the total walking duration (seconds) of valid sessions during the one-week baseline period and then randomly assigned to the intervention or control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter providing consent to participate in the study, the participants installed the Nike Run Club application on their smartphones and received training on how to use it to ensure consistency and accuracy in data collection. At the initial baseline measurement (Week 0), data on demographic information, self-reported physical health status, Internet usage habits, fitness application usage experience, and walking activity level were collected.\u003c/p\u003e\n\u003cp\u003eDuring the intervention period (Weeks 1–8), each participant’s walking activity was continuously monitored using the mobile application. As soon as each walking session was completed, participants submitted the walking activity screen (screenshot) recorded on the application to the researchers. The research assistant reviewed the screenshots submitted daily to check participants’ compliance, checked the validity of each session, and recorded them in a shared database based on Google Sheets. The researchers also regularly conducted checks to maintain the participants’ motivation, check whether they used the app, solved technical problems, continued to participate, and secured data accuracy through KakaoTalk messages once a week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mastery-oriented group received customised nudge messages that focused on self-improvement, personal growth, and internal motivation (e.g. ‘It was difficult to walk for less than a mile at first, but now it has improved a lot. Shall we break personal records this week?’). The performance-oriented group received customised messages to stimulate external motivations through social comparison and competition (e.g., ‘The amount of walking activity this week is above average. Try to outperform the other group!’). Finally, the control group received no separate nudging or motivational messages; only the application was used for self-observation.\u003c/p\u003e\n\u003cp\u003eTo develop intervention messages systematically, existing literature on AGT was first extensively reviewed and integrated, focusing on two target structures: mastery and performance goals. Based on the literature review, draft motivational messages consistent with the intervention strategy were developed to conform to each theoretical component. Subsequently, two professors from the fields of sports and exercise psychology reviewed the draft messages and evaluated their theoretical adequacy and suitability. The experts determined whether each message had clarity, accuracy, and compatibility with the theoretical components of the AGT and provided feedback. The content was elaborated to maintain the face validity and theoretical fidelity of the messages by reflecting the proposed modifications.\u003c/p\u003e\n\u003cp\u003eDuring the eight weeks of the intervention period, messages were sent to participants thrice a day (10:00 a.m., 1:00 p.m., and 6:00 p.m.), alternating three days a week (odd weeks: Monday, Wednesday, Friday; even weeks: Tuesday, Thursday, Saturday) via a social network messenger (KakaoTalk). The participants were encouraged to actively interact with these messages and apply their content to their daily walking activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main outcome was the cumulative walking duration (seconds) during the eight-week intervention period. Secondary outcomes were total walking distance (km) and frequency of walking sessions (number of valid walking sessions). All result variables were objectively measured using the Nike Run Club application, which accurately recorded each participant’s walking behaviour based on global positioning system. Experienced research assistants checked all data weekly to ensure accuracy and completeness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collected data were analysed using IBM SPSS Statistics 28.0 (IBM Corp., Armonk, NY, USA). Participants’ demographic characteristics and baseline PA data were presented as mean (\u003cem\u003eM\u003c/em\u003e) ± standard deviation (\u003cem\u003eSD\u003c/em\u003e), frequency, and percentage through descriptive statistical analysis. Prior to inferential analysis, the data were checked for normality, homogeneity of variance, and outliers.\u003c/p\u003e\n\u003cp\u003eA one-way ANOVA was performed to compare differences in total walking duration, walking distance, and session frequency between the mastery-oriented, performance-oriented, and control groups. When the ANOVA results were significant, pairwise comparisons using Tukey’s post hoc test were performed to determine which groups showed significant differences.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics at baseline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic and baseline characteristics of the participants were analysed using descriptive statistics (Table 1). The average age of the mastery-oriented group (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 29) was 22.07 years old (\u003cem\u003eSD\u003c/em\u003e = 1.94), average daily internet use time was 322.41 min (\u003cem\u003eSD\u003c/em\u003e = 309.84), internet use confidence score was 3.69 (\u003cem\u003eSD\u003c/em\u003e = 0.76), and self-reported physical health score was 3.72 (\u003cem\u003eSD\u003c/em\u003e = 0.70). The average age of the performance-oriented group (\u003cem\u003en\u003c/em\u003e = 29) was 22.83 years (\u003cem\u003eSD\u003c/em\u003e = 2.25), internet use time was 318.97 min (\u003cem\u003eSD\u003c/em\u003e = 164.89), internet use confidence score was 3.83 (\u003cem\u003eSD\u003c/em\u003e = 0.76), and self-reported physical health score was 3.48 (\u003cem\u003eSD\u003c/em\u003e = 0.83). The average age of the control group (\u003cem\u003en\u003c/em\u003e = 29) was 22.86 years old (\u003cem\u003eSD\u003c/em\u003e=1.75), internet usage time was 265.52 min (\u003cem\u003eSD\u003c/em\u003e = 113.22), internet use confidence score was 3.83 (\u003cem\u003eSD\u003c/em\u003e = 0.85), and perceived physical health score was 3.59 (\u003cem\u003eSD\u003c/em\u003e = 0.91).\u003c/p\u003e\n\u003cp\u003eThe chi-square analysis showed no statistically significant differences between the three groups in terms of sex distribution (\u003cem\u003ep\u003c/em\u003e = .95) and previous experience using the PA application (\u003cem\u003ep\u003c/em\u003e = .79). In addition, the one-way ANOVA showed that participants\u0026rsquo; age (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 1.46, \u003cem\u003ep\u003c/em\u003e = .24), Internet use time (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u0026nbsp;\u003c/sub\u003e= 0.65, \u003cem\u003ep\u003c/em\u003e =. 52), Internet use confidence (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 0.29, \u003cem\u003ep\u003c/em\u003e = .75), perceived physical health (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u0026nbsp;\u003c/sub\u003e= 0.63, \u003cem\u003ep\u003c/em\u003e = .53), walking duration (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 0.33, \u003cem\u003ep\u003c/em\u003e = .81), walking distance (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 0.291, \u003cem\u003ep\u003c/em\u003e = .92), and walking frequency (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 0.30, \u003cem\u003ep\u003c/em\u003e = .96) were homogeneous at baseline and that the differences observed after the intervention were due to the intervention\u0026rsquo;s effects rather than the existing characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWalking duration, distance, and frequency during the eight-week intervention period were compared between the three groups using one-way ANOVA (Table 2). Significant differences between the groups were found in total walking duration (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 5.52, \u003cem\u003ep\u003c/em\u003e = .006, \u0026eta;\u0026sup2; = .12). The average walking duration was longer in the mastery-oriented group (\u003cem\u003eM\u003c/em\u003e = 26,519.44 s, \u003cem\u003eSD\u003c/em\u003e = 23,175.59 s) than in the performance-oriented (\u003cem\u003eM\u003c/em\u003e = 21,294.72 s, \u003cem\u003eSD\u003c/em\u003e = 12,169.28 s) and control (\u003cem\u003eM\u003c/em\u003e = 12,685.55 s, \u003cem\u003eSD\u003c/em\u003e = 9,177.42 s) groups. The average walking distance was also significantly different between the groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 6.44, \u003cem\u003ep\u003c/em\u003e = .003, \u0026eta;\u0026sup2; = .13), with the mastery-oriented group (\u003cem\u003eM\u003c/em\u003e = 37.42 km, \u003cem\u003eSD\u003c/em\u003e = 34.23 km) walking the longest distance, followed by the performance-oriented (\u003cem\u003eM\u003c/em\u003e = 28.59 km, \u003cem\u003eSD\u003c/em\u003e = 15.61 km), and control (\u003cem\u003eM\u003c/em\u003e = 16.15 km, \u003cem\u003eSD\u003c/em\u003e = 11.30 km) groups. Finally, the average walking frequency per week was significantly different between the groups (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,84\u003c/sub\u003e = 4.06, \u003cem\u003ep\u003c/em\u003e = .021; \u0026eta;\u0026sup2; = .09). The walking frequency of the mastery-oriented group (\u003cem\u003eM\u003c/em\u003e = 15.06, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 8.27) was higher than that of the performance-oriented group (\u003cem\u003eM\u003c/em\u003e = 14.10, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 8.80), and significantly higher than that of the control group (\u003cem\u003eM\u003c/em\u003e = 9.34, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= 7.44).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe additional post-hoc Tukey analysis revealed that the mastery-oriented group showed significantly higher results in walking duration (general mean difference [GMD] = 13,833.90 s, \u003cem\u003ep\u003c/em\u003e = .004), total walking distance (GMD = 21.27 km, \u003cem\u003ep\u003c/em\u003e = .002), and walking frequency (GMD = 5.72, \u003cem\u003ep\u003c/em\u003e = .025) than the control group. However, the difference between the mastery- and performance-oriented groups and between the performance-oriented and control groups was not statistically significant for any of the three outcome variables (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05). Changes in weekly walking duration, distance, and frequency over the eight-week intervention period for each group are illustrated in Figure 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study verified whether motivational messages of mastery and performance promote college students’ PA using an AGT-based mHealth intervention programme. This study contributed to the literature related to PA by revealing the differential effects of motivational messages for mastery and performance orientation on the walking activity of college students. The findings indicated significant differences between the groups in walking duration, distance, and frequency, and partially supported our hypothesis. Specifically, the mastery-oriented group walked significantly longer, farther, and more frequently than the control group, proving that mastery-oriented motivational messages were effective in promoting sustainable PA. However, the performance-oriented group did not show a significant improvement in walking compared to the control group. Furthermore, the difference between the mastery- and performance-oriented groups was not statistically significant; only the mastery-oriented group showed a significant improvement in PA compared to the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with existing AGT studies (Biddle et al., 2003; Standage et al., 2003; Wang et al., 2010, 2016; Yang et al., 2024), mastery-oriented motivational messages significantly improved the walking behaviour of participants compared to the control group, but did not significantly outperform performance-oriented messages. This pattern suggests that in an mHealth context, mastery-oriented framing may be particularly effective in walking behaviour above a no-intervention baseline, while added value over performance framing remains uncertain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe psychological mechanisms underlying these results can be explained by an increase in intrinsic motivation, perceived competence, and autonomy related to the pursuit of mastery-oriented goals. Specifically, mastery-oriented messages emphasise internal criteria such as personal growth, effort, self-improvement, and self-satisfaction for success, which deepens inner motivation (Nicholls, 1984, 1989; Dweck, 1999). Intrinsic motivation is a key factor that promotes enjoyment, interest, and immersion in PA regardless of external reinforcement or social comparison (Wang et al., 2010, 2016). In addition, the mastery-oriented approach is effective in improving sense of competence and autonomy perception by consistently emphasising individual progress, efforts, and development rather than external results (Bentia et al., 2014; Cho et al., 2011). Such messages can increase the self-efficacy and personal satisfaction of individuals, thereby strengthening intrinsic motivation and increasing the likelihood of continuing the target behaviour (Wang et al., 2010, 2016; Yang et al., 2024).\u003c/p\u003e\n\u003cp\u003eEven from a self-determination theory perspective, mastery-oriented messages can satisfy basic psychological needs, such as autonomy, competence, and relationships (Bentia et al., 2014; Cho et al., 2011). A mastery-oriented approach that continuously emphasises individual effort and progress could have provided this study’s participants with the opportunity to participate autonomously in walking, strengthened their personal competence through self-referential achievements, and enhanced relationships through motivational experiences shared within a mobile context (Nicholls, 1984, 1989; Dweck, 1999; Standage et al., 2003). Wang et al. (2010, 2016) and Yang et al. (2024) reported that creating a mastery-oriented environment effectively promotes sustainable PA by strengthening internal and self-deterministic motivations.\u003c/p\u003e\n\u003cp\u003eThe present results are particularly consistent with those of a recent study emphasizing the potential limitations of traditional specific goal-setting methods (SMART goals; Hawkins et al., 2023; Swann \u0026amp; Rosenbaum, 2018; Swann et al., 2023). According to an increasing number of studies, specific and challenging performance goals (e.g., SMART goals) increase psychological pressure and anxiety and reduce internal motivation in people in the early stages of PA, thereby reducing the persistence of long-term behavioural changes (Swann \u0026amp; Rosenbaum, 2018; Swann et al., 2023; Hawkins et al., 2023, 2024). In fact, non-specific goals such as ‘open goals’ or ‘do your best goals’ are emerging as effective alternatives to promote PA participation, increase positive psychological experiences such as pleasure and autonomy, and reduce feelings of failure or guilt (Swann et al., 2020; Hawkins et al., 2023, 2024; Goddard et al., 2025). Above all, this study further expands the relevant knowledge base by bridging the existing research gap pointed out in a goal-setting study (Swann et al., 2021), showing that mastery-oriented (learning) goal-setting is not only theoretically valid but also superior to the case of no goal-setting in actually increasing participation in PA.\u003c/p\u003e\n\u003cp\u003eBehavioural economics theory provides additional insights into the results of this study. Behavioural economics emphasises the specific synchronisation role of social incentives, goal gradients, pre-commitment, and loss avoidance (van Mierlo et al., 2016; Zimmerman, 2009), and it is highly likely that mastery-oriented messages effectively utilise these mechanisms. In other words, mastery-oriented messages would have greatly strengthened the motivational effect on the control group by clearly defining gradual personal growth (goal gradient), emphasising consistent progress towards the goals set (pre-commitment strategy), and highlighting potential losses if established activity behaviour was not continued (loss avoidance; Shuval et al., 2017; Thaler, 2016; van Mierlo et al., 2016; Vlaev et al., 2019; Zimmerman, 2009).\u003c/p\u003e\n\u003cp\u003eAn important theoretical implication of this study is that it expands existing knowledge about the positive role of a mastery-oriented environment, from a traditional face-to-face or structured education and sports environment to an mHealth environment. Existing AGT studies have reported that mastery-oriented atmospheres positively affect motivation and behaviour mainly in classrooms (Bentia et al., 2014; Cho et al., 2011), PA (Ntoumanis \u0026amp; Biddle, 1999), sports teams (Ntoumanis, 2001), and structured PA environments (Standage et al., 2003). Our study showed that mastery-oriented messages delivered through mobile technology can effectively sustain participants’ internal motivation and PA participation without direct social interaction or the presence of visible peers. Therefore, this study further expands the applicability of AGT, demonstrates its theoretical robustness in various environments, and reaffirms that mastery-oriented motivational strategies can be effectively utilised in scalable digital health interventions to promote sustainable PA.\u003c/p\u003e\n\u003cp\u003eBy contrast, performance-oriented motivational messages centred on social comparison and competition did not significantly increase walking behaviour compared with the control group. According to AGT, performance-oriented goals are highly dependent on external verification and explicit competitive situations, which can increase anxiety and lower internal motivation, eventually limiting sustainable behavioural changes (Nicholls, 1984, 1989; Dweck, 1999; Yang et al., 2024). Therefore, the fact that performance-oriented interventions did not show significant results in this study is consistent with AGT’s theoretical prediction, revealing the potential limitations of external motivation strategies in a mobile-based context.\u003c/p\u003e\n\u003cp\u003eHowever, the results of this study significantly differ from those of Fortunato et al. (2019), which proved the effectiveness of competition-based gamification intervention in increasing PA. The main reason for this could be the difference in the competitive contexts used in the two studies. Fortunato et al. (2019) used explicit and visible competitive factors such as real-time leaderboards, clear rankings, and specific and visible competitive comparisons between participants. This explicit social comparison effectively increased participants’ instantaneousness, urgency, and competitive needs based on behavioural economic principles, such as loss aversion, goal gradients, and commitment strategies. By contrast, the performance-oriented group in this study received motivational messages based on implicit and abstract competition with the ‘invisible’ mastery-oriented group, which lacked explicit visibility or specific social feedback. According to AGT and related psychological literature, competitive motives rely heavily on explicit and specific social comparisons, such as classmates, team members, or clearly presented competitors, which are essential to effectively promote motives and behaviours (Dweck, 1999; Pintrich, 2000). Because there were no specific comparisons, it was difficult for participants to vividly recognise their competitive position, which may have reduced competitive motivation, perceived urgency, and the potential benefits of performance-oriented target strategies. This comparison emphasises the importance of explicit and specific competitive framing, particularly in maximising the motivational potential of performance-oriented messages in mobile-based arbitration.\u003c/p\u003e\n\u003cp\u003eFinally, this study contributes to the development of the mHealth field by addressing major research gaps related to the optimal application of behavioural theory in mHealth interventions to promote PA. Existing meta-analyses and systematic literature reviews have demonstrated that mHealth interventions effectively improve PA (Tong et al., 2024; Mönninghoff et al., 2021), but limitations that significant variability exists in the results according to intervention design, context, and cultural suitability are presented. Tong et al. (2024) emphasised that interventions that combine culturally customised and context-specific messages with strong participatory strategies, such as goal-setting and regular notifications, are effective in promoting continuous changes in PA. In addition, Mönninghoff et al. (2021) reported the short- and long-term effects of mHealth interventions on various PA indicators, such as walking, but emphasised the need for a theory-based approach to maximise the efficacy of the intervention. This study successfully integrated AGT with mobile-based messaging and directly addressed this research gap. In particular, by showing that mastery-oriented messages emphasising internal motivation and self-development can effectively increase the PA of college students, the importance of designing motivational messages based on theory was empirically proven. These findings can provide practical guidance for the development and improvement of mHealth interventions that are theory-oriented, culturally appropriate, and contextual, considering the sustainable improvement of PA in the future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results provide useful guidance for designing effective mHealth interventions that promote PA among college students. As mastery-oriented motivational messages have proven to be effective, intervention designers, health promotion experts, and mobile app developers must prioritise strategies that emphasise intrinsic motivation, personal development, and self-referenced goals. These customised motivational messages can be easily integrated into smartphone applications to provide continuous personalised encouragement to users and promote sustainable PA participation.\u003c/p\u003e\n\u003cp\u003eEducational institutions and public health initiatives can practically apply these findings by incorporating mastery-oriented nutsedge messages into existing digital wellness platforms and campus-based wellness programmes. Mobile platforms can promote consistent PA through automated personalised notifications that emphasise milestones in individual growth, improvement, and intrinsic rewards. This approach, which combines theoretically elaborated message strategies with behavioural economics principles, provides an effective and scalable method of promoting PA, which in turn can contribute to the improvement of PA among college students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and future research directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite the significant results of this study, it has some limitations that future studies should address. First, as this study used self-report measurements and mobile app-based activity data, there may have been data inaccuracies owing to social desirability bias or technical problems. Future research should increase the accuracy of the data by using an objective measurement tool, such as an accelerometer or pedometer.\u003c/p\u003e\n\u003cp\u003eSecond, the intervention period in this study was relatively short (eight weeks). Although significant improvements were observed, long-term studies are needed to evaluate whether these effects persist after the intervention period. Extending the duration of the intervention may lead to more pronounced differences between mastery- and performance-oriented messages, thus providing in-depth insights into the sustainability of behavioural change strategies.\u003c/p\u003e\n\u003cp\u003eThird, the participants were mainly recruited from one university; therefore, the generalisability of the results is limited. Future studies should increase the generalisability and robustness of the results by including groups with various cultural and academic backgrounds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, the specific mechanism of the observed motivational effects was not directly evaluated. Future research should clarify psychological mechanisms through mediational analyses to explain why certain motivational messages show better results. Understanding these psychological mediators will enable interventions that are more goal-oriented and sophisticated.\u003c/p\u003e\n\u003cp\u003eFifth, although randomisation and baseline equivalence were achieved, the study may have been underpowered to detect small-to-moderate differences between the mastery- and performance-oriented groups. Additionally, participant engagement with the motivational messages (e.g., reading rate, perceived relevance) was not monitored, which could influence the observed effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, research using qualitative methods, such as in-depth interviews or focus groups, can provide an in-depth understanding of participants’ subjective experiences with intervention programmes, and the effect of motivational messages on PA can be more clearly understood, allowing researchers and practitioners to refine their intervention strategies more precisely.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that AGT-based mobile interventions focusing on mastery-oriented messages can effectively improve college students’ PA compared to no-intervention controls. While mastery-oriented messages improved walking duration, distance, and frequency over control, they did not significantly outperform performance-oriented messages, and performance-oriented messages did not produce significant gains relative to control.\u0026nbsp;Practitioners may prioritise the mastery-oriented approach for sustainable PA\u0026nbsp;participation targeting young adults. Performance-oriented strategies may still be considered as auxiliary tools depending on the situation and individual characteristics.\u0026nbsp;Future studies should increase the duration of intervention, include objective PA measurement methods, study various groups, and explore psychological mechanisms to optimise the effectiveness and persistence of synchronous interventions to promote\u0026nbsp;PA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Bio \u0026amp; Medical Technology Development Program of the National Research Foundation (NRF) \u0026amp; funded by the Korean government (MSIT) (NRF-2021M3A9E4080780) and Hankuk University of Foreign Studies (2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYH.L. and HS.K. contributed to conception and design of the study. YH.L. collected and analyzed the data and wrote the first draft of the manuscript. HS.K. contributed to reviewing and editing the manuscript. HS.K. contributed to funding acquisition. All authors read and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional review board of Hankuk University of Foreign Studies and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Approval was granted by the Institutional Review Board of Hankuk University of Foreign Studies (Approval No. HIRB20241017-003, approved on October 17, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to their participation in the study. After randomization, the researchers contacted participants via the Webex platform (Cisco) to conduct baseline evaluations at Week 0, held on October 25 and 26. During the baseline evaluation, participants were provided with detailed information about the study’s objectives, procedures, potential risks, and privacy safeguards, along with assurances regarding confidentiality and the voluntary nature of participation. Participants were instructed to complete a web-based questionnaire covering demographic information, self-reported physical health status, Internet usage habits, fitness application usage experience, and baseline walking activity. Participants then provided written consent electronically, confirming their understanding of the study procedures and their right to withdraw at any time without penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmes C (1992) Classrooms: goals, structures, and student motivation. J Educ Psychol 84:261\u0026ndash;271. https://doi.org/10.1037/0022-0663.84.3.261\u003c/li\u003e\n\u003cli\u003eBentia M, Roth G, Deci E (2014) When are mastery goals more adaptive? It depends on experiences of autonomy support and autonomy. J Educ Psychol 106(1):258\u0026ndash;267. https://doi.org/10.1037/a0034007\u003c/li\u003e\n\u003cli\u003eBiddle SJH, Wang CKJ, Kavussanu M, Spray CM (2003) Correlates of achievement goal orientations in physical activity: a systematic review. Eur J Sport Sci 3(5):1\u0026ndash;20. https://doi.org/10.1080/17461390300073504\u003c/li\u003e\n\u003cli\u003eBuckingham SA, Williams AJ, Morrissey K, Price L, Harrison J (2019) Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review. Digit Health 5:2055207619839883. https://doi.org/10.1177/2055207619839883\u003c/li\u003e\n\u003cli\u003eCho Y, Weinstein CE, Wicker F (2011) Perceived competence and autonomy as moderators of the effects of achievement goal orientations. Educ Psychol 31(4):393\u0026ndash;411. https://doi.org/10.1080/01443410.2011.560597\u003c/li\u003e\n\u003cli\u003eDireito A, Carra\u0026ccedil;a E, Rawstorn J, Whittaker R, Maddison R (2017) mHealth technologies to influence physical activity and sedentary behaviors. Sports Med 47(7):1339\u0026ndash;1357. https://doi.org/10.1007/s40279-017-0684-7\u003c/li\u003e\n\u003cli\u003eDweck CS (1999) Self-theories: their role in motivation, personality, and development. Psychology Press, Philadelphia, PA\u003c/li\u003e\n\u003cli\u003eDweck CS, Leggett E (1988) A social-cognitive approach to motivation and personality. Psychol Rev 95:256\u0026ndash;273. https://doi.org/10.1037/0033-295X.95.2.256\u003c/li\u003e\n\u003cli\u003eFaul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39(2):175\u0026ndash;191. https://doi.org/10.3758/BF03193146\u003c/li\u003e\n\u003cli\u003eFortunato M, Harrison J, Oon AL, Small D, Hilbert V, Rareshide CAL et al (2019) Remotely monitored gamification and social incentives to improve physical activity among adults with overweight and obesity (STEP UP): a randomized clinical trial. JAMA Cardiol 4(4):363\u0026ndash;370. https://doi.org/10.1001/jamacardio.2019.0233\u003c/li\u003e\n\u003cli\u003eGoddard SG, Dossetor J, Barry S, Lawrence A, Stevens CJ, Swann C (2025) \u0026ldquo;It took away the trauma of failing\u0026rdquo;: a mixed methods feasibility trial of an open goals physical activity program. Res Q Exerc Sport 96(2):389\u0026ndash;400. https://doi.org/10.1080/02701367.2024.2412661\u003c/li\u003e\n\u003cli\u003eGuthold R, Stevens G, Riley L, Bull F (2018) Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health 6(10):e1077\u0026ndash;e1086. https://doi.org/10.1016/S2214-109X(18)30357-7\u003c/li\u003e\n\u003cli\u003eHawkins RM, Crust L, Swann C, Jackman PC (2023) The effects of goal types on psychological outcomes in active and insufficiently active adults in a walking task. Psychol Sport Exerc 64:102317. https://doi.org/10.1016/j.psychsport.2022.102317\u003c/li\u003e\n\u003cli\u003eHawkins RM, Swann C, Jackman PC (2024) Exploring how active and insufficiently active individuals respond to specific and non-specific physical activity goals. 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Health Psychol Rev 10(1):67\u0026ndash;88. https://doi.org/10.1080/17437199.2015.1104258\u003c/li\u003e\n\u003cli\u003eMinistry of Culture, Sports and Tourism (2023) Korea national leisure activity survey 2022. Ministry of Culture, Sports and Tourism, Seoul. https://www.mcst.go.kr. Accessed 6 Apr 2025\u003c/li\u003e\n\u003cli\u003eMinistry of Culture, Sports and Tourism (2022) Survey on participation in physical activities among Koreans in their 20s. Ministry of Culture, Sports and Tourism, Seoul\u003c/li\u003e\n\u003cli\u003eM\u0026ouml;nninghoff A, Kramer JN, Hess AJ, Ismailova K, Teepe GW, Tudor Car L et al (2021) Long-term effectiveness of mHealth physical activity interventions: systematic review and meta-analysis of randomized controlled trials. J Med Internet Res 23(4):e26699. https://doi.org/10.2196/26699\u003c/li\u003e\n\u003cli\u003eNicholls JG (1984) Conceptions of ability and achievement motivation. 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Sports Med 29(3):167\u0026ndash;180. https://doi.org/10.2165/00007256-200029030-00003\u003c/li\u003e\n\u003cli\u003ePiercy KL, Troiano RP, Ballard RM et al (2018) The physical activity guidelines for Americans. JAMA 320(19):2020\u0026ndash;2028. https://doi.org/10.1001/jama.2018.14854\u003c/li\u003e\n\u003cli\u003ePintrich PR (2000) Multiple goals, multiple pathways: the role of goal orientation in learning and achievement. J Educ Psychol 92:544\u0026ndash;555. https://doi.org/10.1037/0022-0663.92.3.544\u003c/li\u003e\n\u003cli\u003eRanjbar E, Memari AH, Hafizi S, Shayestehfar M, Mirfazeli FS, Eshghi MA (2015) Depression and exercise: a clinical review and management guideline. Asian J Sports Med 6(2):e24055. https://doi.org/10.5812/asjsm.6(2)2015.24055\u003c/li\u003e\n\u003cli\u003eRichards D, Richardson T (2012) Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev 32(4):329\u0026ndash;342\u003c/li\u003e\n\u003cli\u003eSequi-Dominguez I, Alvarez-Bueno C, Martinez-Vizcaino V, Fernandez-Rodriguez R, del Saz Lara A, Cavero-Redondo I (2020) Effectiveness of mobile health interventions promoting physical activity and lifestyle interventions to reduce cardiovascular risk among individuals with metabolic syndrome: systematic review and meta-analysis. J Med Internet Res 22(8):e17790. https://doi.org/10.2196/17790\u003c/li\u003e\n\u003cli\u003eShuval K, Leonard T, Drope J, Katz D, Patel A, Maitin-Shepard M et al (2017) Physical activity counseling in primary care: insights from public health and behavioral economics. CA Cancer J Clin 67(3):233\u0026ndash;244. https://doi.org/10.3322/caac.21394\u003c/li\u003e\n\u003cli\u003eSpartano NL, Davis-Plourde KL, Himali JJ et al (2019) Association of accelerometer-measured light-intensity physical activity with brain volume: the Framingham Heart Study. JAMA Netw Open 2(4):e192745. https://doi.org/10.1001/jamanetworkopen.2019.2745\u003c/li\u003e\n\u003cli\u003eStandage M, Duda JL, Ntoumanis N (2003) Predicting motivational regulations in physical education: the interplay between dispositional goal orientations, motivational climate and perceived competence. J Sports Sci 21(8):631\u0026ndash;647. https://doi.org/10.1080/0264041031000101962\u003c/li\u003e\n\u003cli\u003eSwann C, Rosenbaum S (2018) Do we need to reconsider best practice in goal setting for physical activity promotion? Br J Sports Med 52(8):485\u0026ndash;486. https://doi.org/10.1136/bjsports-2017-098186\u003c/li\u003e\n\u003cli\u003eSwann C, Schweickle MJ, Peoples GE, Goddard SG, Stevens CJ, Vella SA (2020) Comparing the effects of goal types in a walking session with healthy adults: preliminary evidence for open goals in physical activity. Psychol Sport Exerc 47:101475.\u003c/li\u003e\n\u003cli\u003eSwann C, Hooper A, Schweickle MJ, Peoples GE, Mullan J, Hutto D, et al (2020) The potential benefits of non-specific goals in physical activity promotion: comparing open, do-your-best, and as-well-as-possible goals in a walking task. J Appl Sport Psychol 32(4):392\u0026ndash;416. https://doi.org/10.1080/10413200.2019.1604395\u003c/li\u003e\n\u003cli\u003eSwann C, Rosenbaum S, Lawrence A, Vella SA, McEwan D, Ekkekakis P (2021) Updating goal-setting theory in physical activity promotion: a critical conceptual review. Health Psychol Rev 15(1):34\u0026ndash;50. https://doi.org/10.1080/17437199.2019.1706616\u003c/li\u003e\n\u003cli\u003eSwann C, Jackman PC, Lawrence A, Hawkins RM, Goddard SG, Williamson O, et al (2023) The (over)use of SMART goals for physical activity promotion: a narrative review and critique. Health Psychol Rev 17(2):211\u0026ndash;226. https://doi.org/10.1080/17437199.2021.2023608\u003c/li\u003e\n\u003cli\u003eThaler RH (2016) Behavioral economics: past, present, and future. Am Econ Rev 106(7):1577\u0026ndash;1600. https://doi.org/10.1257/aer.106.7.1577\u003c/li\u003e\n\u003cli\u003eTong HL, Alnasser A, Alshahrani NZ, Bawaked RA, AlAhmed R, Alsukait RF et al (2024) The use of mobile technologies to promote physical activity and reduce sedentary behaviors in the Middle East and North Africa region: systematic review and meta-analysis. J Med Internet Res 26:e53651. https://doi.org/10.2196/53651\u003c/li\u003e\n\u003cli\u003eUddin R, Burton NW, Khan A (2020) Combined effects of physical inactivity and sedentary behaviour on psychological distress among university-based young adults: a one-year prospective study. Psychiatr Q 91:191\u0026ndash;202. https://doi.org/10.1007/s11126-019-09697-2\u003c/li\u003e\n\u003cli\u003eU.S. Department of Health and Human Services (1996) Physical activity and health: a report of the Surgeon General. Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office of the Surgeon General, President's Council on Physical Fitness and Sports, Atlanta, GA\u003c/li\u003e\n\u003cli\u003evan Mierlo T, Hyatt D, Ching AT, Fournier R, Dembo RS (2016) Behavioral economics, wearable devices, and cooperative games: results from a population-based intervention to increase physical activity. JMIR Serious Games 4(1):e1. https://doi.org/10.2196/games.5358\u003c/li\u003e\n\u003cli\u003eVlaev I, King D, Darzi A, Dolan P (2019) Changing health behaviors using financial incentives: a review from behavioral economics. BMC Public Health 19:1059. https://doi.org/10.1186/s12889-019-7407-8\u003c/li\u003e\n\u003cli\u003eWang CKJ, Liu WC, Sun Y, Lim BSC, Chatzisarantis NLD (2010) Chinese students' motivation in physical activity: goal profile analysis using Nicholls' achievement goal theory. Int J Sport Exerc Psychol 8(3):284\u0026ndash;301. https://doi.org/10.1080/1612197X.2010.9671958\u003c/li\u003e\n\u003cli\u003eWang CKJ, Morin AJS, Liu WC, Chian LK (2016) Predicting physical activity intention and behaviour using achievement goal theory: a person-centred analysis. Psychol Sport Exerc 23:13\u0026ndash;20. https://doi.org/10.1016/j.psychsport.2015.10.004\u003c/li\u003e\n\u003cli\u003eWarburton DER, Nicol CW, Bredin SSD (2006) Health benefits of physical activity: the evidence. CMAJ 174(6):801\u0026ndash;809. https://doi.org/10.1503/cmaj.051351\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (n.d.) Insufficient physical activity indicator group. World Health Organization, Geneva. https://www.who.int/data/gho/data/themes/topics/indicator-groups/insufficient-physical-activity-indicator-group. Accessed 6 Apr 2025\u003c/li\u003e\n\u003cli\u003eYang N, Quan H, Guo Z (2024) The influence of motivational climate on physical activity adherence among junior high school students: the mediating effect of achievement goal orientation. PLoS One 19(12):e0315831. https://doi.org/10.1371/journal.pone.0315831\u003c/li\u003e\n\u003cli\u003eZimmerman FJ (2009) Using behavioral economics to promote physical activity. Prev Med 49(4):289\u0026ndash;291. https://doi.org/10.1016/j.ypmed.2009.07.008\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Demographic characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eMastery\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePerformance\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eControl\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategorical variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e16 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e17 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e16 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e13 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e12 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e13 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 567px;\"\u003e\n \u003cp\u003eExperience in mobile-based physical activity app\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e6 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e4 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e23 (79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e25 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e24 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 602px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous variables (M\u0026plusmn;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e22.07\u0026plusmn;1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e22.83\u0026plusmn;2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e22.86\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eInternet usage (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e322.41\u0026plusmn;309.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e318.97\u0026plusmn;164.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e302.30\u0026plusmn;212.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eInternet confidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.69\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.83\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.83\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003ePerceived physical health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.72\u0026plusmn;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.48\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3.59\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eBaseline walking duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3973.34\u0026plusmn;3316.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3508.31\u0026plusmn;3462.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e3468.00\u0026plusmn;3127.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eBaseline walking distance (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e5.42\u0026plusmn;5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e4.93\u0026plusmn;5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e4.95\u0026plusmn;4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eBaseline walking frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.10\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.00\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e2.07\u0026plusmn;1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. ANOVA results and post-hoc comparisons for walking duration, distance, and frequency\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"917\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 101px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMastery\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePerformance\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e = 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 154px;\"\u003e\n \u003cp\u003ePost-hoc comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eGMD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e (\u003cem\u003eSE\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAverage walking duration (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26,519.44 (23,175.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e21,294.72 (12,169.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12,685.55 (9,177.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e13,833.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e5,224.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003ePerformance vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8609.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAverage walking distance (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e37.42 (34.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e28.59 (15.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e16.15 (11.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e21.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003ePerformance vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 101px;\"\u003e\n \u003cp\u003eAverage walking frequency (session per week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15.06 (8.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e14.10 (8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9.34 (7.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eMastery vs. Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003ePerformance vs. Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01, * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05. \u003cem\u003eGMD\u003c/em\u003e = group mean difference; SE = standard error\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7189266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7189266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Although it is well known that regular physical activity is beneficial to physical and psychological health, the physical activity levels of adults and college students worldwide remain insufficient, and few studies have applied the achievement goal theory to a mobile health environment to verify its effectiveness. Therefore, this study examined the effects of a mobile intervention based on the achievement goal theory to improve college students’ physical activity. Specifically, this study compared the effects of mastery- and performance-oriented messages on participants’ walking behaviour. A total of 87 South Korean university students (mean age = 22.59 ± 2.00 years; 56.3% men) were randomly assigned to one of three groups: mastery-oriented (n = 29), performance-oriented (n = 29), or control (n = 29). During the eight-week intervention period, the mastery-oriented group received nudging messages emphasising self-referential goals and personal growth, whereas the performance-oriented group received messages emphasising social comparison and competition. The control group did not receive any motivational messages. Walking duration, distance, and frequency were objectively measured using the Nike Run Club mobile application. The results showed significant differences between groups in walking duration (p = .006), distance (p = .021), and frequency (p = .021). More specifically, the mastery-oriented group showed significantly better performance on all three measurement indicators than the control group, but there was no significant difference between the performance-oriented and control groups. These results emphasise the potential of mobile interventions to promote physical activity by promoting intrinsic motivation and self-referential growth with mastery-based motivational messages. Therefore, practitioners and health experts should integrate mastery-oriented nudge strategies into mobile health platforms to promote sustainable physical activity participation among young adults.","manuscriptTitle":"Effectiveness of an achievement goal theory-based mobile intervention for increasing physical activity among college students: A randomised controlled trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:18:30","doi":"10.21203/rs.3.rs-7189266/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20690db3-3432-413b-baa5-7064239835d8","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61089827,"name":"Health sciences/Health care"},{"id":61089828,"name":"Biological sciences/Psychology"},{"id":61089829,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-08T08:27:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7189266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7189266","identity":"rs-7189266","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}