Factors of Leading the Adoption of Diet/Exercise Apps on Smartphones: Application of Channel Expansion Theory

Abstract

This study paid attention to the notable increase of the use of diet/exercise apps on smartphones. Based on channel expansion theory and technology acceptance model (TAM), this study investigated how three predictors-Internet information use efficacy, Internet information credibility, smartphone use efficacy-affected one's attitude toward and intention to use diet/exercise apps. Results from a path analysis indicated that Internet information use efficacy and smartphone use efficacy positively predict the perceived ease of use of diet/exercise apps. Internet information credibility positively predicts the perceived usefulness of such apps. Moreover, there were gender differences in the effects of both Internet information use efficacy and Internet information credibility on the perceived usefulness of diet/exercise apps.

The Intention to Use Fitness and Physical Activity Apps: A Systematic Review

“…Most studies (n = 9) found a moderate and significant influence between both variables with a range of Fisher's Z = 0.31-0.66 [1,[43][44][45]51,53,56,60,61]. Some studies found a significant high to very high ratio with a large influence of PU on ITU based on effect size values above 0.70 [52,59] and in two of the three models proposed by Kim et al [1]. Mohammadi and Isanejad [57] found a low influence on sports organization employees [Fisher's Z = 0.19 (0.09, 0.30) and Ha et al [42] found no PU influence on ITU in university students on sports information Apps.…”

“…To test quality, risk of bias analysis of the 19 studies evaluated in the research showed that only three studies had a high score of 15 points or more out of 20 total [1,45,51], most studies (n = 14) had a mean score between 10 and 15 points and only two studies had a score below 10 points [52,53]. It should be noted that none of the studies analyzed carried out a calculation of the sampling required for the generalization of the results, which could be due to the fact that all the studies carried out a selection of the sample for convenience within a certain population.…”

“…Li, Liu, Ma and Zhang [46] sampled subjects over 25 years of age, while Huang and Ren [60] and Mohammadi and Isanejad [57] were at least 30 years old. Regarding the type of App evaluated, six studies evaluated the intention to use diet and fitness applications [30,44,51,52,55,59], another five studies evaluated sports information Apps [42,43,53,57,62], two studies measured the intention of fans to use the sports team app [1,45], health and fitness app [56,61], or fitness [58,60] and one study evaluated a social fitness-tracking app [63] and mHealth related to promote physical activity [54]. The most widely used theory for the design and use of the mobile sports app intent of use assessment instrument was TAM (n = 10).…”

“…Seven studies found a significant relationship, however, except Beldad and Hegner [43] which had a moderate influence [Fisher's Z = 0.47 (0.38, 0.56)] and Chen and Lin [44] in their study evaluated two models with moderate-low influences [Fisher's Z1 = 0.31 (0.25, 0.37); Fisher's Z2 = 0.32 (0.26, 0.38)]. The rest had a low influence [45,57,60,61] or nonsignificant influence [1,51,52].…”

“…The studies have been classified into three groups considering the mean age or the age range with the highest percentage. One group was established with six studies that had a higher proportion of subjects under 25 years [1,[51][52][53]59,61], a second group formed by three studies with the age of the subjects between 25 and 30 years [42,43,56] and finally, a third group had a higher proportion of subjects over 30 years [45,57,60]. Chen and Lin's study [44] was not classified as it indicated that their sample was mostly composed of subjects between 20 and 30 years of age.…”