Smartphone Application for Physical Activity Enhancement at Workplace: Would Office Workers Actually Use It?

Mohadis, Hazwani Mohd; Ali, Nazlena Mohamad

doi:10.1109/ict4m.2018.00035

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…), we found positive relationships among habit, performance expectancy, facilitating conditions, price value, effort expectancy, and behavioral intentions to use fitness apps. Habit and performance expectancy were found to be the most important predictors of intention to use fitness apps, consistent with prior studies (eg, habit [19,20,30] and performance expectancy [14,15,30]). Positive relationships have also been identified for effort expectancy [18][19][20], facilitating conditions [18,20,21], and price value [19,21,30].…”

Section: Theoretical Contributionsupporting

confidence: 88%

“…Three-and four-way interactions of age, gender, and experience were included in the original UTAUT2 study [9]. Despite the fact that the original studies supported these proposed moderator relationships, previous studies on mobile health and fitness apps applying the UTAUT or UTAUT2 did not fully consider them [14,15,[18][19][20][21]49]. The moderators have been meta-analyzed and suggested as worthy of study [22] or noted as future work [19].…”

Section: Moderating Effects Of Individual Differencesmentioning

confidence: 99%

“…This may be explained by the high demands of fitness app users on app usage to achieve their physical activity goals, compared with the fun or pleasure derived from the apps. However, focusing solely on the four determinants proposed by the first version of UTAUT [14,15,34] may be insufficient. Habit, in particular, is the strongest determinant linked to the intention to use fitness apps in this study.…”

Section: Theoretical Contributionmentioning

confidence: 99%

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Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

Yang¹,

Koenigstorfer²

2021

J Med Internet Res

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Background Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active. Objective Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions. Methods Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships. Results The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit (β=.42; P<.001), performance expectancy (β=.36; P<.001), facilitating conditions (β=.15; P<.001), price value (β=.13; P<.001), and effort expectancy (β=.09; P=.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active (β=.12; P<.001; R2=0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects. Conclusions This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity.

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Section: Theoretical Contributionsupporting

confidence: 88%

Section: Moderating Effects Of Individual Differencesmentioning

confidence: 99%

Section: Theoretical Contributionmentioning

confidence: 99%

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Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

Yang¹,

Koenigstorfer²

2021

J Med Internet Res

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“…Hence, subjective norm is commonly used in the studies related to technology acceptance. For example, a recent study [48] was conducted in an oce environment and focused on the eects of social norms on the adoption of mobile applications for promoting physical activity. The authors found that social inuence is an eective factor for using such applications.…”

Section: Subjective Norm In Oice Seing and Oice Typementioning

confidence: 99%

“…• RQ3: How are oce-related factors associated with oce workers' responsiveness to breakreminder notications? Previous studies showed how behaviors of oce workers could be aected by external factors such as peers and colleagues [24,48,71]. In addition, oce type (shared or private oce) has an eect on distractions observed by employees or their sitting behavior [51,52,65].…”

Section: Introductionmentioning

confidence: 99%

A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System

Cavdar

Temizel

Musolesi

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels of knowledge workers. We also developed multi-level (within- and between-subjects) models for the inference of attentional states and engagement/challenge levels with mobile application usage indicators as inputs, such as the number of applications used prior to notifications, the number of switches between applications, and application category usage. The results of our analysis show that the following features are effective for the inference of attentional states and engagement/challenge levels: the number of switches between mobile applications in the last 45 minutes and the duration of application usage in the last 5 minutes before users' response to ESM messages.

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Factors Influencing Acceptance of Personal Health Record Apps for Workplace Health Promotion: Cross-Sectional Questionnaire Study

Park¹,

Kim²,

Soh³

et al. 2020

JMIR Mhealth Uhealth

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Background Health care technologies can help improve workers’ health and productivity by supporting workplace health promotion. A personal health record app is used to manage medical data such as results from medical checkups, which facilitates decision making for medical personnel. However, an analysis of users’ technology acceptance is required to provide appropriate services based on personal health record apps. Objective The purpose of this study was to analyze the factors influencing the behavioral intention of health experts and workers to use an app in workers’ health centers and to examine differences in their perception of the main variables. Methods The study involved health experts and workers who visited 21 workers’ health centers in Korea to verify a research model in which perceived risk was added to the unified theory of acceptance and use of technology, a representative theory of information technology acceptance. After receiving ethical approval from the Korea National Institute for Bioethics Policy, 1050 questionnaires were distributed over 7 weeks with cooperation of the Korea Occupational Safety and Health Agency. A multiple linear regression analysis and multigroup path analysis were performed to verify the hypotheses, and independent samples t tests were performed to analyze differences between workers’ and health experts’ perception of the main variables. Results The analysis included data from 866 respondents (687 workers and 179 health experts). Effort expectancy (beta=.08, P=.03), social influence (beta=.43, P<.001), performance expectancy (beta=.07, P=.008), and facilitating conditions (beta=.13, P<.001) exerted significant positive effects on behavioral intention, whereas perceived risk (beta=–.29, P<.001) exerted a significant negative effect on behavioral intention. Performance expectancy had a significant effect on path differences depending on gender (critical ratio=–3.38) and age (critical ratio=1.97). Workers’ mean scores for the main variables were higher relative to those of health experts for all remaining variables except perceived risk, and significant differences were observed for all remaining variables except facilitating condition. Conclusions Social influence exerted the strongest effect on behavioral intention to use the personal health record app. Consequently, it is necessary to coordinate health promotion activities in the workplace as well as the operational direction of community institutions such as in workers’ health centers to allow workers to manage their own health via continuous use of the app. In addition, the app should be developed based on a requirement analysis of the balance between both interest groups in consideration of differences in perspective between consumers and service providers.

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Smartphone Application for Physical Activity Enhancement at Workplace: Would Office Workers Actually Use It?

Cited by 5 publications

References 24 publications

Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study

A Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the Design of an Effective Mobile Notification System

Factors Influencing Acceptance of Personal Health Record Apps for Workplace Health Promotion: Cross-Sectional Questionnaire Study

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