a BMI was classified according to the US Centers for Disease Control and Prevention’s BMI weight status categories: underweight (below 18.5 kg/m 2 ); normal or healthy weight (18.5 to 24.9 kg/m 2 ); overweight (25.0 to 29.9 kg/m 2 ); and obese (over 30.0 kg/m 2 ).
Table 2 provides an overview of the descriptive statistics of the variables. The average ratings of the UTAUT2 determinants ranged from 4.26, for social influence, to 6.02, for facilitating conditions. Education-, motivation-, and gamification-related app features were considered important, with the highest ratings for motivation (mean 5.21) compared with gamification- and education-related app features (mean 5 for both). Participant ratings of their behavioral intentions to use fitness apps were above the midpoint of the scale (mean 5.53); intentions of being physically active in the future were very high for both MET values and the ratings on the seven-point rating scale (mean 4589 MET min/week, SD 3137; and mean 6.07, SD 1.05, respectively). All values of skewness and kurtosis were within the suggested criteria (ie, skewness <2 and kurtosis <7 [ 63 ]), indicating normality of the univariate distribution.
Constructs and items | Value, mean (SD) | Skewness | Kurtosis | Reliability | Convergent validity | ||||||||||||||
Cronbach α | Composite reliability | Factor loadings | AVE | ||||||||||||||||
.87 | 0.87 | 0.70 | |||||||||||||||||
I find the [xx] app useful in my daily life | 5.54 (1.41) | −1.07 | 0.88 | 0.84 | |||||||||||||||
Using the [xx] app helps me accomplish things | 5.43 (1.38) | −1.02 | 0.98 | 0.86 | |||||||||||||||
Using the [xx] app increases my physical activity levels | 5.50 (1.35) | −1.05 | 1.08 | 0.80 | |||||||||||||||
.89 | 0.89 | 0.68 | |||||||||||||||||
Learning how to use the [xx] app is easy to me | 6.02 (1.11) | −1.41 | 2.52 | 0.84 | |||||||||||||||
My interaction with the [xx] app is clear and understandable | 6.01 (1.09) | −1.4 | 2.62 | 0.84 | |||||||||||||||
I find the [xx] app easy to use | 6.05 (1.09) | −1.48 | 2.64 | 0.86 | |||||||||||||||
It is easy for me to become skillful at using the [xx] app | 5.90 (1.12) | −1.27 | 2.13 | 0.77 | |||||||||||||||
.94 | 0.94 | 0.83 | |||||||||||||||||
People who are important to me think that I should use the [xx] app | 4.30 (1.70) | −0.26 | −0.56 | 0.87 | |||||||||||||||
People who influence my behavior think that I should use the [xx] app | 4.24 (1.73) | −0.25 | −0.64 | 0.92 | |||||||||||||||
People whose opinions that I value prefer that I use the [xx] app | 4.23 (1.72) | −0.29 | −0.60 | 0.94 | |||||||||||||||
.77 | 0.78 | 0.54 | |||||||||||||||||
I have the resources necessary to use the [xx] app | 6.08 (1.11) | −1.54 | 3.03 | 0.83 | |||||||||||||||
I have the knowledge necessary to use the [xx] app | 6.18 (1.05) | −1.53 | 2.87 | 0.83 | |||||||||||||||
The [xx] app is compatible with other technologies I use | 5.80 (1.29) | −1.24 | 1.61 | 0.57 | |||||||||||||||
.91 | 0.91 | 0.78 | |||||||||||||||||
Using the [xx] app is fun | 5.07 (1.42) | −0.66 | 0.27 | 0.93 | |||||||||||||||
Using the [xx] app is enjoyable | 5.24 (1.40) | −0.80 | 0.50 | 0.91 | |||||||||||||||
Using the [xx] app is very entertaining | 4.71 (1.58) | −0.48 | −0.32 | 0.82 | |||||||||||||||
.90 | 0.91 | 0.76 | |||||||||||||||||
The [xx] app is reasonably priced | 6.28 (1.13) | −1.7 | 2.59 | 0.81 | |||||||||||||||
The [xx] app is a good value for the money | 6.21 (1.14) | −1.5 | 1.85 | 0.93 | |||||||||||||||
At the current price, the [xx] app provides a good value | 5.23 (1.15) | −1.72 | 2.98 | 0.88 | |||||||||||||||
.80 | 0.84 | 0.66 | |||||||||||||||||
The use of the [xx] app has become a habit to me | 5.34 (1.67) | −1.04 | 0.33 | 0.54 | |||||||||||||||
I am addicted to using the [xx] app | 3.65 (1.96) | 0.09 | −1.25 | 0.87 | |||||||||||||||
I must use the [xx] app | 3.84 (1.98) | −0.05 | −1.24 | 0.90 | |||||||||||||||
.89 | 0.89 | 0.73 | |||||||||||||||||
I intend to continue using the [xx] app in the future | 5.77 (1.37) | −1.41 | 2.02 | 0.83 | |||||||||||||||
I will always try to use the [xx] app in my daily life | 5.22 (1.55) | −0.92 | 0.37 | 0.85 | |||||||||||||||
I plan to continue to use the [xx] app frequently | 5.61 (1.45) | −1.27 | 1.46 | 0.89 | |||||||||||||||
.85 | 0.85 | 0.65 | |||||||||||||||||
How important to you are app features that motivate you to be physically active? | 5.13 (1.54) | −0.88 | 0.31 | 0.83 | |||||||||||||||
How important are app features that help you to increase your physical activity levels? | 5.38 (1.42) | −1.04 | 0.88 | 0.82 | |||||||||||||||
How important to you are app features that remind you to be physically active? | 5.11 (1.63) | −0.87 | 0.17 | 0.77 | |||||||||||||||
.90 | 0.90 | 0.74 | |||||||||||||||||
How important to you are app features that educate yourself about how to exercise best? | 5.01 (1.62) | −0.77 | −0.11 | 0.86 | |||||||||||||||
How important to you are app features that tell you how things work when exercising? | 4.87 (1.61) | −0.66 | −0.30 | 0.85 | |||||||||||||||
How important to you are app features that help you do the right things when exercising? | 5.11 (1.58) | −0.81 | 0.12 | 0.87 | |||||||||||||||
.84 | 0.84 | 0.63 | |||||||||||||||||
How important to you are app features to enjoy yourself while exercising? | 5.20 (1.55) | −0.88 | 0.30 | 0.86 | |||||||||||||||
How important to you are app features that gamify the exercise experience? | 4.62 (1.83) | −0.51 | −0.74 | 0.68 | |||||||||||||||
How important to you are app features that make the exercise experience joyful? | 5.16 (1.52) | −0.93 | 0.47 | 0.88 | |||||||||||||||
N/A | N/A | N/A | |||||||||||||||||
Intentions of being physically active during the next 4 weeks (MET min/week) | 4589 (3137) | 1.13 | 1.66 | 1 | |||||||||||||||
Intentions of being physically active during the next 4 weeks (1-7 rating scale) | 6.07 (1.05) | −1.17 | 1.68 | 1 | |||||||||||||||
When did you download a fitness app for the first time? (months ago) | 30.07 (25.76) | 1.39 | 2.62 | N/A | N/A | 1 | N/A |
a Model fit was satisfactory: χ 2 564 =2112.2; χ 2 /df=3.8; comparative fit index=0.93; Tucker-Lewis index=0.91; root mean square error of approximation=0.06; and standardized root mean square residual=0.07.
b The criteria for skewness (absolute value <2) and kurtosis (absolute value <7) were fulfilled for a sample size greater than 300 (ie, N=839), indicating normality of the univariate distribution [ 63 ].
c AVE: average variance extracted.
d [xx] refers to the brand name of the specified fitness app.
e BI: behavioral intentions to use the fitness app.
f MO: motivation-related app features.
g ED: education-related app features.
h GA: gamification-related app features.
i PA: Intentions of being physically active. The intentions were measured using the International Physical Activity Questionnaire (metabolic equivalent of task min/week) and a single-item 7-point rating scale. The reported measurement model is based on the first measure.
j N/A: not applicable.
k MET: metabolic equivalent of task.
l EXP: user experience with fitness apps.
The overall model fit using MET minutes per week values for physical activity intentions as the dependent variable was found to be satisfactory (χ 2 564 =2112.2; χ 2 /df=3.8; CFI=0.93; TLI=0.91; RMSEA=0.06; and SRMR=0.07), after excluding one item for facilitating conditions (ie, “I can get help from others when I have difficulties using the [ brand name ] app” with a factor loading of 0.30). The internal reliability, convergent validity, and discriminant validity of the measurement model were evaluated. All Cronbach α and construct reliability values were ≥.77 (ie, above the suggested threshold of 0.70), indicating internal reliability. The AVE and factor loadings were >0.54, in all cases, above the thresholds of 0.50, suggesting convergent validity ( Table 2 ).
Table 3 shows the results of the discriminant validity. First, no cross-loadings were detected among the measurement items. Second, all the square roots of AVE were greater than the relevant interconstruct correlations with two exceptions (ie, performance expectancy: 0.88; and facilitating conditions: 0.87). The HTMT criteria were fulfilled (ie, all HTMT values were ≤0.85) with one exception (performance expectancy: 0.88), but the value is still within the acceptable range between 0.85 and 0.90 [ 60 ].
Variables | BI | PE | EE | SI | FC | HM | PV | HA | MO | ED | GA | PA | Age | GEN | EXP |
BI | .879 | .646 | .414 | .623 | .604 | .473 | .795 | .423 | .218 | .241 | N/A | N/A | N/A | N/A | |
PE | .875 | .651 | .464 | .594 | .694 | .405 | .747 | .635 | .368 | .378 | N/A | N/A | N/A | N/A | |
EE | .637 | .648 | .181 | .785 | .435 | .614 | .341 | .321 | .147 | .179 | N/A | N/A | N/A | N/A | |
SI | .407 | .455 | .168 | .135 | .536 | .057 | .616 | .366 | .366 | .375 | N/A | N/A | N/A | N/A | |
FC | .584 | .561 | .871 | .090 | .394 | .678 | .281 | .278 | .146 | .178 | N/A | N/A | N/A | N/A | |
HM | .607 | .693 | .446 | .517 | .363 | .254 | .650 | .515 | .458 | .571 | N/A | N/A | N/A | N/A | |
PV | .467 | .412 | .619 | .046 | .645 | .266 | .181 | .199 | .077 | .097 | N/A | N/A | N/A | N/A | |
HA | .592 | .569 | .180 | .590 | .091 | .536 | .027 | .470 | .316 | .366 | N/A | N/A | N/A | N/A | |
MO | .423 | .630 | .319 | .356 | .253 | .519 | .203 | .404 | .683 | .712 | N/A | N/A | N/A | N/A | |
ED | .222 | .365 | .148 | .364 | .125 | .451 | .078 | .303 | .680 | .632 | N/A | N/A | N/A | N/A | |
GA | .243 | .366 | .188 | .346 | .156 | .549 | .107 | .339 | .706 | .637 | N/A | N/A | N/A | N/A | |
PA | .133 | .130 | .073 | .032 | .060 | .176 | .067 | .079 | .046 | .104 | .036 | N/A | N/A | N/A | |
Age | .038 | .026 | .003 | −.036 | .053 | −.034 | .084 | .001 | .055 | −.033 | −.011 | −.035 | N/A | N/A | |
GEN | .019 | .064 | .118 | −.092 | .058 | −.038 | .041 | −.016 | .157 | .063 | .096 | −.057 | .061 | N/A | |
EXP | .095 | .043 | .159 | −.140 | .196 | .009 | .179 | −.099 | −.040 | −.068 | −.061 | .084 | .051 | −.011 |
a BI: behavioral intentions to use the fitness app.
b PE: performance expectancy.
c EE: effort expectancy.
d SI: social influence.
e FC: facilitating conditions.
f HM: hedonic motivation.
g PV: price value.
h HA: habit.
i MO: motivation-related app features.
j ED: education-related app features.
k GA: gamification-related app features.
l PA: intentions of being physically active.
m GEN: gender.
n EXP: user experience with fitness apps.
o Terms in italics along the diagonal are square roots of average variance extracted. Below the diagonal, the lower left metrics test the discriminant validity according to the Fornell-Larcker criterion. Discriminant validity is fulfilled if the square roots of the average variance extracted are larger than the relevant interconstruct correlations. Furthermore, above the diagonal, the upper right metrics refer to the heterotrait-monotrait ratio, where <0.85 or <0.90 indicates good discriminant validity.
p N/A: not applicable.
Path modeling was used to test the hypotheses. The model was established by modeling the hypothesized paths among the UTAUT2 determinants, behavioral intentions of using fitness apps, intentions of being physically active, and the three app features ( Figure 1 ). On the basis of the different measures of intention to be physically active, two models were established. The first model (considering physical activity intentions measured in MET min/week) had an excellent fit (χ 2 79.00 =97.74; χ 2 /df=1.2; P= .08; CFI=0.984; TLI=0.968; RMSEA=0.017; SRMR=0.006). The model fit for the second model (taking into account physical activity intentions measured on a single-item rating scale) was also good (χ 2 79.00 =179.07; χ 2 /df=2.3; P <.001; CFI=0.925; TLI=0.849; RMSEA=0.039; SRMR=0.010). Both models explained 76% of the variance in the behavioral intentions to use fitness apps.
In what follows, we first present the results of model 1. Performance expectancy ( β= .36, SE 0.04; P <.001), effort expectancy ( β= .09, SE 0.04; P= .04), facilitating conditions ( β= .15, SE 0.04; P <.001), price value ( β= .13, SE 0.03; P <.001), and habit ( β= .42, SE 0.04; P <.001) were positively related to behavioral intention to use fitness apps, whereas social influence ( β= .03, SE 0.03; P= .37) and hedonic motivation ( β= .02, SE 0.03; P= .63) were nonsignificant predictors. Behavioral intentions to use fitness apps relate positively to intentions of being physically active ( β= .12, SE 0.03; P <.001), explaining 2% of the variance in physical activity intentions. For model 2, the path coefficients between the UTAUT2 determinants and behavioral intentions of using the fitness app were identical to the results obtained from model 1. Behavioral intentions to use fitness apps relate positively to intentions of being physically active ( β= .37, SE 0.03; P <.001), explaining 12% of the variance in physical activity intentions. Thus, hypotheses 1, 2, 4, 6, 7, and 8 were supported, whereas hypotheses 3 and 5 were not supported ( Table 4 ; Figure 2 ).
Path | ( ) | Z value | value | Hypothesis testing | |||||
PE →BI | .36 (0.04) | 8.62 | <.001 | Hypothesis 1 is supported | |||||
EE →BI | .09 (0.04) | 2.02 | .04 | Hypothesis 2 is supported | |||||
SI →BI | .03 (0.03) | 0.90 | .37 | Hypothesis 3 is not supported | |||||
FC →BI | .15 (0.04) | 3.55 | <.001 | Hypothesis 4 is supported | |||||
HM →BI | .02 (0.03) | 0.49 | .63 | Hypothesis 5 is not supported | |||||
PV →BI | .13 (0.03) | 3.97 | <.001 | Hypothesis 6 is supported | |||||
HA →BI | .42 (0.04) | 11.52 | <.001 | Hypothesis 7 is supported | |||||
BI→PA | .12 (0.03) | 3.60 | <.001 | Hypothesis 8 is supported | |||||
ED →BI | −.02 (0.03) | −0.89 | .37 | N/A | |||||
ED×PE→BI | −.08 (0.03) | −2.46 | .01 | N/A | |||||
ED×EE→BI | .01 (0.04) | 0.17 | .86 | N/A | |||||
ED×FC→BI | .06 (0.04) | 1.80 | .07 | N/A | |||||
ED×HM→BI | −.02 (0.03) | −0.76 | .45 | N/A | |||||
ED×PV→BI | −.04 (0.03) | −1.17 | .24 | N/A | |||||
ED×SI→BI | .02 (0.03) | 0.70 | .48 | N/A | |||||
ED×HA→BI | .08 (0.03) | 2.63 | .009 | N/A | |||||
MO →BI | −.07 (0.03) | −2.34 | .02 | N/A | |||||
MO×PE→BI | .10 (0.03) | 3.16 | .002 | N/A | |||||
MO×EE→BI | .08 (0.04) | 2.07 | .06 | N/A | |||||
MO×FC→BI | −.11 (0.04) | −2.79 | .005 | N/A | |||||
MO×HM→BI | .02 (0.03) | 0.69 | .49 | N/A | |||||
MO×PV→BI | −.03 (0.04) | −0.72 | .47 | N/A | |||||
MO×SI→BI | −.01 (0.03) | −0.47 | .64 | N/A | |||||
MO×HA→BI | −.18 (0.03) | −5.46 | <.001 | N/A | |||||
GA →BI | −.01 (0.03) | −0.47 | .64 | N/A | |||||
GA×PE→BI | −.03 (0.03) | −0.87 | .38 | N/A | |||||
GA×EE→BI | −.01 (0.04) | −0.29 | .77 | N/A | |||||
GA×FC→BI | −.04 (0.03) | −1.06 | .29 | N/A | |||||
GA×HM→BI | .07 (0.03) | 2.77 | .006 | N/A | |||||
GA×PV→BI | .02 (0.03) | 0.68 | .49 | N/A | |||||
GA×SI→BI | .01 (0.03) | 0.52 | .60 | N/A | |||||
GA×HA→BI | −.04 (0.03) | −1.26 | .21 | N/A |
a Unstandardized path coefficient. See Table 5 for the path coefficients of the individual-difference moderators and their interaction effects.
b UTAUT2: Unified Theory of Acceptance and Use of Technology 2.
c PE: performance expectancy.
d BI: behavioral intentions to use the fitness app.
e EE: effort expectancy.
f SI: social influence.
g FC: facilitating conditions.
h HM: hedonic motivation.
i PV: price value.
j HA: habit.
k PA: intentions of being physically active, measured in metabolic equivalent of task minutes per week.
l ED: education-related app features.
m N/A: not applicable.
n MO: motivation-related app features.
o GA: gamification-related app features.
The testing of the interaction effects of app features and the seven UTAUT2 determinants was performed next ( Table 4 ). Education-related app features moderated the relationships between performance expectancy and behavioral intentions to use fitness apps ( β= −.08, SE 0.03; P =.01), as well as between habit and behavioral intentions of using fitness apps ( β= .08, SE 0.03; P =.009). Motivation-related app features moderated the relationships between performance expectancy and behavioral intentions of using fitness apps ( β= .10, SE 0.03; P =.002), facilitating conditions and behavioral intentions to use fitness apps ( β=− .11, SE 0.04; P =.005), and habit and behavioral intentions to use fitness apps ( β=− .18, SE 0.03; P <.001). Gamification-related app features moderated the relationship between hedonic motivation and behavioral intention to use fitness apps ( β= .07, SE 0.03; P= .006).
The testing of the interaction effects of individual differences and the seven UTAUT2 determinants ( Table 5 ) also revealed that age moderated the relationship between effort expectancy and behavioral intention to use fitness apps ( β =−.11, SE 0.04; P =.008). Gender moderated the relationships among performance expectancy and behavioral intention to use fitness apps ( β= .13, SE 0.06; P =.03), habit, and behavioral intentions ( β=− .12, SE 0.05; P =.02). Experience was a nonsignificant moderator. In addition, the joint moderating tests (three- and four-way effects) taking into account individual differences revealed a significant three-way interaction for age, gender, and hedonic motivation ( β =−.14, SE 0.06; P =.02); a significant three-way interaction for age, experience, and effort expectancy ( β =.09, SE 0.03; P =.007), and a significant three-way interaction of age, experience, and habit on behavioral intentions to use fitness apps ( β =−.12, SE 0.04; P =.004). There were no significant four-way interaction effects.
Subsequently, we conducted follow-up tests to describe how the moderators changed the relationships ( Table 6 ), considering low (−1 SD of the mean) and high (+1 SD of the mean) values of the moderators. First, when education-related features were rated as important, the relationship between performance expectancy and usage intentions was weaker compared with when this feature was rated as unimportant. Second, when education-related features were rated as important, the relationship between habit and usage intentions was stronger compared with when these features were rated as unimportant. Third, when motivation-related features were rated as important, the relationship between performance expectancy and usage intentions was stronger, the relationship between facilitating conditions and usage intentions became nonsignificant, and the relationship between habit and usage intentions was weaker compared with when these features were rated unimportant. Fourth, when gamification-related features were rated as important, the relationship between hedonic motivation and usage intentions was stronger but still nonsignificant compared with when this feature was rated unimportant. Furthermore, the relationship between effort expectancy and usage intentions was positive for younger users but nonsignificant for older users. Finally, the relationship between performance expectancy and usage intentions was stronger among males, whereas the relationship between habit and usage intentions was stronger among females.
Path | ( ) | Z value | value |
Age→BI | .03 (0.03) | 1.26 | .21 |
Age×PE →BI | .03 (0.04) | 0.74 | .46 |
Age×EE →BI | −.11 (0.04) | −2.65 | .008 |
Age×SI →BI | −.04 (0.03) | −1.35 | .18 |
Age×FC →BI | .04 (0.04) | 1.08 | .28 |
Age×HM →BI | .02 (0.04) | 0.45 | .65 |
Age×PV →BI | .01 (0.03) | 0.30 | .77 |
Age×HA →BI | .04 (0.04) | 1.05 | .29 |
GEN →BI | .06 (0.04) | 1.48 | .14 |
GEN×PE→BI | .13 (0.06) | 2.20 | .03 |
GEN×EE→BI | .004 (0.06) | −0.07 | .94 |
GEN×SI→BI | −.04 (0.05) | −0.77 | .44 |
GEN×FC→BI | −.06 (0.06) | −1.03 | .30 |
GEN×HM→BI | .06 (0.05) | 1.22 | .22 |
GEN×PV→BI | −.05 (0.05) | −1.01 | .31 |
GEN×HA→BI | −.12 (0.05) | −2.34 | .02 |
EXP →BI | .01 (0.03) | 0.55 | .58 |
EXP×EE→BI | −.01 (0.04) | −0.38 | .70 |
EXP×SI→BI | −.02 (0.03) | −0.44 | .66 |
EXP×FC→BI | .05 (0.04) | 1.15 | .25 |
EXP×HM→BI | .02 (0.03) | 0.75 | .46 |
EXP×HA→BI | .01 (0.03) | 0.30 | .76 |
Age×GEN→BI | −.02 (0.04) | −0.55 | .58 |
Age×GEN×PE→BI | .10 (0.06) | 1.62 | .10 |
Age×GEN×EE→BI | .04 (0.07) | 0.63 | .53 |
Age×GEN×SI→BI | .09 (0.04) | 1.96 | .052 |
Age×GEN×FC→BI | −.002 (0.06) | −0.04 | .97 |
Age×GEN×HM→BI | −.14 (0.06) | −2.41 | .02 |
Age×GEN×PV→BI | −.02 (0.05) | −0.32 | .75 |
Age×GEN×HA→BI | −.06 (0.05) | −1.16 | .25 |
EXP×GEN→BI | .06 (0.03) | 1.99 | .047 |
EXP×GEN×EE→BI | .10 (0.06) | 1.72 | .09 |
EXP×GEN×SI→BI | .06 (0.05) | 1.32 | .19 |
EXP×GEN×FC→BI | −.04 (0.06) | −0.62 | .54 |
EXP×GEN×HM→BI | −.07 (0.05) | −1.54 | .12 |
EXP×GEN×HA→BI | −.02 (0.05) | −0.53 | .60 |
Age×EXP→BI | .04 (0.04) | 1.10 | .27 |
Age×EXP×EE→BI | .09 (0.03) | 2.70 | .007 |
Age×EXP×SI→BI | −.02 (0.03) | −0.45 | .65 |
Age×EXP×FC→BI | −.07 (0.04) | −1.71 | .09 |
Age×EXP×HM→BI | .06 (0.04) | 1.76 | .08 |
Age×EXP×HA→BI | −.12 (0.04) | −2.85 | .004 |
Age×GEN×EXP→BI | −.002 (0.04) | −0.05 | .96 |
Age×GEN×EXP × EE→BI | −.02 (0.06) | −0.25 | .80 |
Age×GEN×EXP×SI→BI | −.02 (0.05) | −0.41 | .70 |
Age×GEN×EXP×FC→BI | .04 (0.07) | 0.58 | .56 |
Age×GEN×EXP×HM→BI | −.09 (0.06) | −1.47 | .14 |
Age×GEN×EXP×HA→BI | .03 (0.05) | 0.57 | .57 |
a Unstandardized path coefficient. See Table 4 for the path coefficients of the seven UTAUT2 determinants and app-feature moderators.
b BI: behavioral intentions to use the fitness app.
d EE: effort expectancy.
e SI: social influence.
f FC: facilitating conditions.
g HM: hedonic motivation.
h PV: price value.
i HA: habit.
j GEN: gender.
k EXP: user experience with fitness apps.
Interactions | Low (−1 SD of mean) | High (+1 SD of mean) | ||||
Slope | test | value | Slope | test | value | |
ED ×PE | 0.36 | 8.05 | <.001 | 0.28 | 2.56 | .01 |
ED×HA | 0.42 | 9.39 | <.001 | 0.50 | 4.56 | <.001 |
MO ×PE | 0.36 | 8.05 | <.001 | 0.46 | 4.20 | <.001 |
MO×FC | 0.14 | 3.13 | .002 | 0.03 | 0.27 | .78 |
MO×HA | 0.42 | 9.39 | <.001 | 0.24 | 2.19 | .03 |
GA ×HM | 0.02 | 0.45 | .66 | 0.09 | 0.82 | .41 |
Age×EE | 0.09 | 2.01 | .04 | −0.02 | −0.18 | .86 |
GEN ×PE | 0.36 | 8.05 | <.001 | 0.49 | 4.47 | <.001 |
GEN×HA | 0.42 | 9.39 | <.001 | 0.30 | 2.74 | .006 |
a Low: low moderators.
b High: high moderators.
c ED: education-related app features.
d PE: performance expectancy.
e HA: habit.
i HM: hedonic motivation.
j EE: effort expectancy.
k GEN: gender. The results for females (dummy: 0) are reported as low moderators; the results for males (dummy: 1) are reported as high moderators.
The purpose of this study was to examine the influence of the UTAUT2 determinants, 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 the app usage intentions of individuals and their behavioral intentions of being physically active. The results showed that habit and performance expectancy were the two strongest predictors of intentions of individuals to use fitness apps. The effects of performance expectancy were greater when motivation-related features were rated as important and when education-related features were rated as less important. Moreover, the effects of performance expectancy were greater for males. The effects of habit were greater when education-related features were rated as important and when motivation-related features were rated as less important. Furthermore, the effects of habit were greater for females. Age moderated the relationship between effort expectancy and app usage intention. The intentions of individuals to use fitness apps predicted their intentions of being physically active, using two different means of measuring future physical activity.
We contribute to the literature on mobile health and physical activity in several ways. Answering the first research question ( What are the relationships between the UTAUT2 determinants and intentions to use smartphone fitness apps? ), 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 - 20 ], facilitating conditions [ 18 , 20 , 21 ], and price value [ 19 , 21 , 30 ].
Social influence was a nonsignificant predictor of intention [ 18 , 20 , 30 ]. Interestingly, the latter finding is not due to the high domain-specific experience of users (given the nonsignificant interaction effect of social influence and experience), who might have relied less on peer opinions for their evaluations and intentions than low-experience users. Furthermore, in contrast to the original UTAUT2 study [ 9 ] and previous studies [ 18 , 20 , 21 , 30 ], but in agreement with Dhiman et al [ 19 ], we found a nonsignificant relationship between hedonic motivation and app usage intentions. 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.
Answering the second research question ( What is the downstream relationship between the behavioral intentions of using fitness apps and of being physically active? ), we contribute to UTAUT2-based research by showing that app usage intentions have important downstream consequences. In particular, individuals have greater intentions of being physically active when they have higher intentions to use fitness apps. Assessing the downstream effect of intention to use fitness apps is important, because downloaded but unused apps or apps unable to motivate people to become or remain physically active will have little health effects [ 5 , 16 ]. The positive relationship between fitness app usage intentions and physical activity intentions indicates that app usage might motivate people to become or remain active. The findings thus contribute to previous research into whether, and when, mobile health and fitness apps may help individuals become physically active [ 64 , 65 ]. However, it should be noted that the intentions of individuals to be physically active are affected by numerous correlates and determinants (eg, self-efficacy, sociodemographic variables, sport club membership, among others) [ 66 ], and the intention-behavior gap is considerable [ 67 ]. Thus, adding these factors and incorporating measurements of actual physical activity may be warranted in the future.
Answering the third research question ( Do fitness apps moderate the relationships between the UTAUT2 determinants and intentions of using fitness apps? ), this study contributes to previous research that categorized app features [ 17 ] yet ignored their influence on the structural relationships proposed by the UTAUT2. On the basis of our exploratory analysis, we identified six relevant interaction effects. One of the most intuitive findings was that when motivation-related features were rated as important, the relationship between performance expectancy and intentions was strong. Research into goal achievement [ 68 , 69 ] might explain the interaction effect: individuals who are interested in improving their physical activity levels, or keeping them at certain levels, might use the app exactly for this purpose. Among the three features, motivational elements aim most directly to help users stick to their goals and plans [ 70 ]; as there is goal congruence, the effect is strong [ 71 ]. When motivation-related features were rated as important, the relationship between facilitating conditions and usage intentions was not significant. This makes sense, because people who lack resources and capacities are more dependent on help from others compared with people who do have these resources and capacities, particularly when motivation features are not considered crucial (ie, motivation might “not be the problem”). In addition, when motivation-related features were important, the relationship between habit and intention was weaker compared with when this feature was unimportant. This finding might indicate that when habits have been formed, features that motivate individuals to be active (eg, reminders) become less important to these app users [ 72 ].
This study also found that performance expectancy had a greater effect on usage intentions when education-related features were rated as unimportant. In this case, individuals might be less interested in being educated—an aspect that might distract them from achieving their goals. In addition, the effect of habit on usage intention was stronger when education-related features were rated as important. This may be explained by the fact that habits of individuals are formed best when they are exposed to education-related cues when using an app (eg, how and when to exercise best) [ 73 ]. Regarding the interaction between hedonic motivation and gamification-related features, no final conclusions can be drawn. Although research into intrinsic motivation [ 74 ] and flow [ 75 ] may lead us to propose that intrinsic motivation, as a principal source of enjoyment, may be enhanced by the gamification app features (eg, apps using incommensurate gamification elements [likes]) [ 76 ], the follow-up tests did not reach significant levels in this study.
Answering the fourth research question ( Are there individual differences in age, gender, and user experience between the relationships of the UTAUT2 determinants and intentions to use fitness apps? ), we found partly significant, partly nonsignificant moderating effects of age, gender, and experience. First, the relationship between effort expectancy and app usage intentions was stronger among younger individuals, which agrees with the original UTAUT2 study [ 8 , 9 ] and a meta-analysis (ie, age group of those aged 25 to 30 years) [ 22 ]. Second, the relationship between performance expectancy and usage intentions was stronger among males, which is consistent with the original UTAUT2 study. In contrast, the relationship between habit and usage intention was stronger among females [ 9 ]. Thus, females were not more sensitive to new cues, which might have weakened the effect of habit on behavioral intentions. In the context of fitness apps, females may indeed be prone to cues that help them form health-related habits, because they are interested in health- and body-appearance-related topics. Finally, in this study, experience was a nonsignificant moderator regarding the interaction effects of the UTAUT2 determinants on app usage intentions. Thus, differences in experiences between users might be less relevant today—a time in which smartphone users can easily add and delete new apps and in which users are technology savvy.
This study has implications for smartphone app designers and managers. First, they can be advised to focus on habit formation and performance (eg, goal setting) when designing fitness apps and tailoring them to potential users. Meeting users’ expectations concerning facilitating conditions, price value, and effort expectancy will also increase the likelihood of the app being accepted. Second, practitioners should highlight certain app features that depend on user preferences. For example, motivation-related features are important drivers of app usage intentions for target group users who value performance (education-related features might be less relevant here); habit formation and facilitating conditions are less important to these individuals. Third, health professionals should consider age and gender differences among users with regard to the effects of effort expectancy (age) as well as performance expectancy and habit (gender). Finally, practitioners may also be advised to monitor whether app usage intentions have a positive correlation with intentions of, or even actual, physical activities so that immediate action can be taken when users lose track of their original goals (having already downloaded the app).
This study has some limitations. First, the generalizability of our findings is limited. We used a nonrepresentative sample of US residents who owned a smartphone and had previously used fitness apps. Future studies may consider inexperienced people with fitness apps to reveal the influence of UTAUT2 determinants on usage intentions at the early- or preadoption stage. Second, given this research design, we did not consider one specific fitness app, but participants stated their preferred app and rated the features of this app. Thus, we considered a variety of apps (which might be beneficial for external validity, given the myriad of apps on the market [ 3 , 4 ]). Researchers might collaborate with certain providers and use real-world app data and objectively measure actual physical activity to validate our findings. Third, we relied on self-reported physical activity intentions using a single measure and the International Physical Activity Questionnaire Short Form. Overreporting is common for the latter (eg, approximately 84% [ 77 ]). Finally, future research could look into the mechanisms of moderation effects on individuals’ behavioral intentions to use apps, incorporate app features into mobile health interventions accordingly, and evaluate their long-term influence on physical activity levels.
The authors would like to thank the participants of this study. The authors did not receive any external funding from this study.
None declared.
Online instructions to participants.
average variance extracted |
comparative fit index |
heterotrait-monotrait |
metabolic equivalent of task |
root mean square error of approximation |
standardized root mean square residual |
Tucker-Lewis index |
Unified Theory of Acceptance and Use of Technology |
Edited by R Kukafka; submitted 26.11.20; peer-reviewed by C Ochoa-Zezzatti, J Offermann-van Heek; comments to author 09.02.21; revised version received 24.03.21; accepted 04.05.21; published 13.07.21
©Yanxiang Yang, Joerg Koenigstorfer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.07.2021.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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The increased health problems associated with lack of physical activity is of great concern around the world. Mobile phone based fitness applications appear to be a cost effective promising solution for this problem. The aim of this study is to develop a research model that can broaden understanding of the factors that influence the user acceptance of mobile fitness apps. Drawing from Unified Theory of Acceptance and Use of Technology (UTAUT) and Elaboration Likelihood Model (ELM), we conceptualize the antecedents and moderating factors of fitness app use. We validate our model using field survey. Implications for research and practice are discussed.
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Agarwal, R., Karahanna, E.: Time flies when you’re having fun: Cognitive Absorption and beliefs about information technology usage. MIS Quarterly 24, 665–694 (2000)
Article Google Scholar
Ajzen, I.: The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes 50(2), 179–211 (1991)
Atkinson, M.A., Kydd, C.: Individual characteristics associated with World Wide Web use: An empirical study of playfulness and motivation. The DATA BASE for Advancement in Information Systems 28, 53–62 (1997)
Anderson, J.C., Gerbing, D.W.: Structural Equation Modeling in Practice; A Review and Recommended Two-Step Approach. Psychological Bulletin 103(3), 41–423 (1988)
Becker, M.H., Maiman, L.A., Kirscht, J.P., Haefner, D.P., Drachman, R.H.: The health belief model and prediction of dietary compliance: A field experiment. Journal of Health and Social Behaviour 18, 348–366 (1977)
Brawley, L.R., Vallerand, R.J.: Enhancing intrinsic motivation for fitness activities: Its systematic increase in fitness environment (Unpublished manuscript, University of Waterloo) (1984)
Google Scholar
Boberg, E.W., Gustafson, D.H., Hawkins, R.P., et al.: Development, acceptance, and use patterns of a computer-based education and social support system for people living with AIDS HIV infection. Comput Human Behav. 11, 289–311 (1995)
Boothby, J., Tungatt, M.F., Townsend, A.R.: Ceasing participation in sports activity: Reported reasons and their implications. Journal of Leisure Research 12, 1–14 (1981)
Chen, C., Czerwinski, M., Macredie, R.: Individual differences in virtual environments - introduction and overview. Journal of the American Society for Information Science 51(6) (2000)
Chin, W.W.: The Partial Least Squares Approach to Structural Equation Modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 295–336. Lawrence Eribaum Associates, Mahwah (1998)
Chau, P.Y.K., Hu, P.J.-H.: Investigating healthcare professionals’ decision to accept telemedicine technology: An empirical test of competing theories. Information and Management 39, 297–311 (2002)
Cohen, S., Syme, S.L.: Social support and health. Academic Press, San Diego (1985)
Csikszentmihalyi, M., Rathunde, K.: The measurement of flow in everyday life: Toward a theory of emergent motivation. In: Jacobs, J. (ed.) Nebraska Symposium on Motivation, 8th edn., pp. 57–97. University of Nebraska Press, Lincoln (1992)
Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13, 319–340 (1989)
Davis, F.D., Bagozzi, R.P., Warshaw, P.P.: Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology 22, 1111–1132 (1992)
Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: A comparison of two theoretical models. Management Science 35, 982–1003 (1989)
Deci, E.L., Ryan, R. M.: Intrinsic Motivation and Self Determination in Human Behavior. Plenum Press, New York (1985)
Book Google Scholar
Deelstra, J.T., Peeters, M.C., et al.: Receiving Instrumental Support at Work: When Help Is Not Welcome. Journal of Applied Psychology 88(2), 324–331 (2003)
Dickinger, A., Arami, M., Meyer, D.: The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems 17, 4–11 (2008)
Dormann, C., Zapf, D.: Social Support, Social Stressors at Work, and Depressive Symptoms: Testing for Main and Moderating Effects with Structural Equations in a Three-Wave Longitudinal Study. Journal of Applied Psychology 84(6), 874–884 (1999)
Lanseng, E.J., Andreassen, T.W.: Electronic healthcare: A study of people’s readiness and attitude toward performing self-diagnosis. International Journal of Service Industry Management 18(4), 394–417 (2007)
Falk, R.F., Miller, N.B.: A primer for soft modeling. The University of Akron Press, Akron (1992)
Fredrickson, B.L.: Cultivating positive emotions to optimize health and well-being. Prevention and Treatment 3(1), 1 (2000)
Frederick, C.M., Ryan, R.M.: Differences in motivation for sport and exercise and their relationships with participation and mental health. Journal of Sport Behavior 16, 125–145 (1993)
Fitch, C.J.: Information systems in healthcare: Mind the gap. In: Proceeding of the 37th Hawaii International Conference on System Sciences (2004)
Foa, E., Rothbaum, B., Furr, J.: Augmenting exposure therapy with other CBT procedures. Psychiatric Annals 33(1), 47–56 (2011)
Grise, M., Gallupe, B.: Information overload: Addressing the productivity paradox in face-to-face electronic meeting. Journal of Management Information Systems 16(3), 157–185 (1999–2000)
Grime, P.R.: Computerized cognitive behavioural therapy at work: A randomized controlled trial in employees with recent stress-related absenteeism. Occup. Med. 54, 353–359 (2004)
Gould, S.J.: Consumer attitudes toward health and health care: A differential perspective. The Journal of Consumer Affairs 22(1), 96–118 (1988)
Heijden, H.: User acceptance of hedonic information systems. MIS Quarterly 28, 695–704 (2004)
Hu, P., Chau, P., Tam, K.: Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems 16(2), 91–112 (1999)
Jansson, M., Linton, S.J.: Cognitive-behavioral group therapy as an early intervention for insomnia: A randomized controlled trial. J. Occup. Rehabil. 15, 177–190 (2005)
Jayasuriya, R.: Determinants of microcomputer technology use: Implications for education and training of health staff. International Journal of Medical Informatics 50, 187–194 (1998)
Kerr, J.H., Fujiyama, H., Campano, J.: Emotion and Stress in Serious and Hedonistic Leisure Sport Activities. Journal of Leisure Research 34, 272–289 (2002)
Kraft, F.B., Goodell, P.W.: Identifying the health conscious consumer. Journal of Health Care Marketing 13(3), 18–25 (1993)
Kleijnen, M., Wetzels, M., de Ruyter, K.: Consumer acceptance of wireless finance. Journal of Financial Services Marketing 8, 206–217 (2004)
Koestner, R., McClelland, D.C.: Perspectives on competence motivation. In: Pervin, L.A. (ed.) Handbook of Personality: Theory and Research, pp. 527–548. Guilford Press, New York (1990)
Koh. C.: Ezyhealth & Beauty (2011)
Lai, T.Y., Larson, E.L., Rockoff, M.L.: User acceptance of HIV TIDES-Tailored interventions for management of depressive symptoms in persons living with HIV/ AIDS. J. Am. Med. Inform. Assoc. 15, 217–226 (2008)
Lamminmäki, E., Pärkkä, J., Hermersdorf, M., Kaasinen, J., Samposalo, K., Vainio, J., Kolari, J., Kulju, M., Lappalainen, R., Korhonen, I.: Wellness Diary for Mobile Phones. In: EMBEC (2005)
Lee, Y., Kim, J., Lee, I., Kim, H.: A crosscultural study on the value structure of mobile internet usage: Comparison between Korea and Japan. Journal of Electronic Commerce Research 3, 227–239 (2002)
Lee, G., Tsai, C., Griswold, W.G., Raab, F., Patrick, K.: PmEB: A Mobile Phone Application for Monitoring Caloric Balance. CHI, Work-in-Progress, 1013–1018 (2006)
McLean, Motivating Patients to Use Smartphone Health Apps, Consumer Health Information Corporation (April 25, 2011)
Marsh, H.S.: Positive and Negative Global Self-Esteem: A Substantively Meaningful Distinction or Artifactors? Journal of Personality and Social Psychology 70(4), 810–819 (1996)
Article MathSciNet Google Scholar
Moore, G., Benbasat, I.: Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 2(3), 192–222 (1991)
Newsom, J.T., McFarland, B.H., Kaplan, M.S., Huguet, N., Zani, B.: The health consciousness myth: Implications of the near independence of major health behaviours in the North American population. Social Science & Medicine 60, 433–437 (2005)
Nunnally, J.C.: Psychometric Theory. Mcgraw-Hill Book Company, New York (1978)
Pavlou, P.A.: Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce 7(3), 69–103 (2003)
Plank, R.E., Gould, S.J.: Health consciousness, scientific orientation and wellness; An examination of the determinants of wellness attitudes and behaviors. Health Marketing Quarterly 7(3-4), 65–83 (1990)
Pelletier, L.G., Fortier, M.S., Vallerand, R.J., Tuston, K.M., Blais, M.R.: Toward a new measure of intrinsic motivation, extrinsic motivation and amotivation in sports: The Sport Motivation Scale (SMS). Journal of Sports &- Exercise Psychology 17, 35–53 (1995)
Prochaska, T.R., Leventhal, E.A., et al.: Health practices and illness cognition in young, middle aged and elderly adults. Journal of Gerontology 40(5), 569–578 (1985)
Reeve, J., Deci, E.L.: Elements of the competitive situation that affect intrinsic motivation. Personality and Social Psychology Bulletin 22(14), 33 (1996)
Scheier, M., Carver, C.S.: Effects of optimism on psychological and physical well-being: Theoretical overview and empirical update. Cognitive Therapy and Research 16(2), 201–228 (1992)
Sheppard, B.H., Hartwick, J., Warshaw, P.R.: The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. J Consumer Res. 15, 325–343 (1988)
Skinner, B.F.: Contingencies of reinforcement: A theoretical analysis. Prentice-Hall, Englewood Cliffs (1969)
Sloan, R.P., Gruman, J.C.: Participation in Workplace Health Promotion Programs - The Contribution of Health and Organizational-Factors. Health Education Quarterly 15(3), 269–288 (1988)
Taylor, S., Todd, P.: Understanding information technology usage: A test of competing models. Information Systems Research 6, 144–176 (1995)
Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly 15(1), 124–143 (1991)
Venkatesh, V., Morris, M.G., Davis, G.B.: User acceptance of information technology: Toward a unified view. MIS Quarterly 27, 425–478 (2003)
Venkatesh, V., Morris, M.G., Ackerman, P.L.: A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes 83(1), 33–60 (2000)
Valdivieso-López, E., et al.: Efficacy of a mobile application for smoking cessation in young people: Study protocol for a clustered, randomized trial. BMC Public Health 13(1), 1–6 (2013)
Yoganathan, D., Kajanan, S.: Persuasive Technology for Smartphone Fitness Apps (2013)
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Yoganathan, D., Kajanan, S. (2014). What Drives Fitness Apps Usage? An Empirical Evaluation. In: Bergvall-Kåreborn, B., Nielsen, P.A. (eds) Creating Value for All Through IT. TDIT 2014. IFIP Advances in Information and Communication Technology, vol 429. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43459-8_12
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The use of technology in sports and fitness is proliferating thanks to advances to facilitate its practice and improve adherence. Beyond adherence, it is important that technology is understood as a facilitating medium. The main objective of this study is to know the influence of the use of the fitness application (app) on sports habits, customer satisfaction and maintenance intention of fitness center users. For this, an experimental, controlled and randomized study was carried out, characterized by being a field trial, with a sample of 66 participants divided into a control group ( n = 33) and an experimental group ( n = 33), with 38 (57.6%) men and 28 (42.4%) women who self-monitored their physical activity for 8 weeks. The dimensions analyzed between the pre- and post-intervention phases were the changes in their sporting habits (frequency of attendance and duration of the session), the changes in satisfaction and the intention to stay with respect to the fitness center. The results in general do not show significant differences between the two groups and conclude that the use of the fitness app did not directly influence the sports habits of the participants. There were also no significant differences in terms of satisfaction with the fitness center or in their intention to stay in the fitness center. Therefore, it is shown that the use of the fitness app, as a single download or use element, is not enough to improve habits, satisfaction or the intention to stay in the fitness center.
Keywords: experimental study; fitness app; fitness industry; loyalty; satisfaction.
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1 GICAFE “Physical Activity and Exercise Sciences Research Group”, University of Balearic Islands, Balearic Islands, Spain
2 PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain
3 Chronobiology Research Group, Department of Physiology, Faculty of Biology, University of Murcia, Campus Mare Nostrum, IUIE, IMIB-Arrixaca, Murcia, Spain
4 Ciber Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
5 Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School, The University of Queensland School of Medicine in New Orleans, New Orleans, LA USA
6 Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC USA
7 School of Kinesiology and Health Studies, Queen’s University, Kingston, ON Canada
8 Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL USA
9 Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden
Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility. Technological advancements have led to increased digitization within healthcare and athletics. Nevertheless, further investigation is needed to enhance the validity and reliability of existing fitness apps for CRF assessment in both contexts.
The present review aimed to (1) systematically review the scientific literature, examining the validity and reliability of apps designed for CRF assessment; and (2) systematically review and qualitatively score available fitness apps in the two main app markets. Lastly, this systematic review outlines evidence-based practical recommendations for developing future apps that measure CRF.
The following sources were searched for relevant studies: PubMed, Web of Science ® , ScopusTM, and SPORTDiscus, and data was also found within app markets (Google Play and the App Store).
Eligible scientific studies examined the validity and/or reliability of apps for assessing CRF through a field-based fitness test. Criteria for the app markets involved apps that estimated CRF.
The scientific literature search included four major electronic databases and the timeframe was set between 01 January 2000 and 31 October 2018. A total of 2796 articles were identified using a set of fitness-related terms, of which five articles were finally selected and included in this review. The app market search was undertaken by introducing keywords into the search engine of each app market without specified search categories. A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis.
Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps. Out of the scientific apps reviewed, the SA-6MWTapp showed the best validity against a criterion measure ( r = 0.88), whilst the InterWalk app showed the highest test–retest reliability (ICC range 0.85–0.86).
Levels of evidence based on scientific validity/reliability of apps and on commercial apps could not be robustly determined due to the limited number of studies identified in the literature and the low-to-moderate quality of commercial apps.
The results from this scientific review showed that few apps have been empirically tested, and among those that have, not all were valid or reliable. In addition, commercial apps were of low-to-moderate quality, suggesting that their potential for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations that apps might potentially offer to objectively and remotely assess CRF as a complementary tool to traditional methods in the clinical and sports settings.
The online version of this article (10.1007/s40279-019-01084-y) contains supplementary material, which is available to authorized users.
The validity and reliability of existing and/or under-development fitness apps should be further investigated. |
Physiological signals should be incorporated into fitness apps, such as heart rate measures using a smartphone camera, during or after exercise testing. |
There is a need to develop interoperable fitness apps (e.g., different languages, apps integrated into both app markets, data that is device-independent). |
Fitness apps should incorporate evidence-based cut-points of CRF, allowing interpretation of fitness testing results. |
Cardiorespiratory fitness (CRF) is a powerful marker of cardiovascular (CV) health [ 1 – 6 ]. Despite the strong existing evidence linking CRF to CV health, the recent eHealth tools developed to assess CV disease (CVD) risk on the basis of multiple risk factors does not include CRF as a measure [ 7 ]. Further, maximal oxygen uptake ( V O 2max ) is an objective measure of CRF and has been considered a key indicator of sports performance [ 8 – 10 ]. In fact, V O 2max assessment has been historically recommended in both the clinical and sports settings by the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) [ 11 , 12 ]. Since an incremental maximal or submaximal exercise test is not always possible in clinical or field settings, in part due to feasibility concerns with its routine measurement (e.g., time needed, expensive equipment, expertise required, etc.), estimations of CRF using non-exercise algorithms have a pragmatic importance that may enhance CVD risk and sports performance prediction [ 13 – 16 ]. However, the rapid development in smartphone technology might provide a novel alternative to non-exercise algorithms to estimate CRF (i.e., V O 2max ) in the present and future. Such an approach could be useful and meaningful from a clinical point of view as well as from a sports and training landscape.
Technological advancements have led to increased digitization within healthcare and sports [ 17 , 18 ]. The emergence of available smartphone applications (apps) in Google Play and the App Store (iTunes) in September 2008 and June 2009, respectively, have contributed to a better understanding of human health by allowing us to gather vast amounts of medical and fitness data [ 19 ]. Specifically, some improvements in app technology (e.g., a built-in camera for heart rate assessment, accelerometers, etc.) have opened new opportunities for collecting relevant information in the clinical and sports settings.
In fact, successful examples of clinicians and scientists using apps that allow for a flood of new information for better management of a patients’ CV health are already available [ 20 ]. More specifically, the usefulness of apps in clinical practice is supported by current reviews of CV mHealth (healthcare practice supported by mobile devices), which have outlined the potential of these apps to improve access to a large number of people living far from clinical centers, reduce costs, and enhance health outcomes for CVD management [ 21 – 23 ]. Within this context, the use of apps for telemedicine purposes has demonstrated their potential and effectiveness for remote monitoring of clinical parameters, such as CVD risk factors [ 24 ]. An example of this practice is the AliveCor Kardia device, a clinically validated smartphone-based electrocardiogram recording [ 25 ]. A recent randomized controlled trial has examined the assessment of remote heart rhythm in 1001 ambulatory patients ≥ 65 years of age at increased risk of stroke who were using this device. The results highlight that this approach was significantly more likely to identify incident atrial fibrillation than routine care over a 12-month period [ 26 ]. If these innovative clinical practices are viable with other vital signs, it can be speculated that apps may hold utility in detecting patients with low CRF levels, and in turn allow for a more accurate determination of CVD risk [ 27 ].
The use of apps to collect data has also drawn widespread attention among sports professionals and exercise scientists. In fact, some apps have already been developed to collect physiological, kinanthropometric, and sports performance data [ 28 ]. The use of apps for data collection is likely the most popular in recreational sporting activities, although they are also utilized in a higher performance sporting context [ 29 ]. In high-performance sports, the expertise required to quantify an athlete’s physical performance with traditional methods is often expensive and non-user-friendly, especially for trainers [ 28 ]. However, apps hold great potential by making physical performance measurements for coaches and trainers more affordable in field conditions. A popular recreational example is the various apps designed for tracking distance or pace during endurance sports [ 30 – 32 ]. A real-world app is Strava , commonly used for individuals practicing recreational endurance activities [ 32 ]. Among the most attractive Strava features is its ability to track all aspects of logged physical activities (e.g., distance, pace, watts, heart rate) and its capability to analyze them on a per-minute basis. Likewise, in a competitive sporting context, there are already validated apps aimed at coaches for assessing sports performance data such as sprint mechanical outputs [ 33 ] and running technique [ 34 ].
Fitness apps might provide a valuable opportunity for assessing CRF, bringing the laboratory into the pocket, and making fitness assessments feasible as part of routine clinical care [ 35 ]. Furthermore, using apps for CRF self-assessment as part of the clinical workflow might provide clinicians with health information difficult to collect during brief patient visits, furthermore allowing integration of this data into the electronic health record and aid in ongoing care [ 35 , 36 ]. Despite the plethora of existing traditional approaches to CRF assessment, some barriers hamper its use in clinical practice. For instance, the correct selection of a CRF protocol according to a person’s individualized exercise or functional capacity can be challenging at times [ 37 ]. In addition, making this selection often requires professionals with advanced training in CRF measurement not always available in the clinical setting. Another hurdle to performing clinical CRF assessments is the use of specialized equipment (e.g., ECG, pulse oximetry, accelerometers, etc.) that may not be available. In this context, an app-based approach could overcome these challenges associated with traditional approaches, allowing for broader application of CRF assessments. For instance, a valid and reliable fitness app might assist health professionals in the selection of the optimal CRF assessment protocol and integrate the measurement of physiological signals to more objectively assess CRF. Also, these apps could be useful for screening programs identifying individuals with higher versus lower CVD risk based on the app-assessed CRF level. As a current clinical example, the MyHeart Counts app has demonstrated real-world feasibility in assessing CRF on a large scale, incorporating this assessment into the broader evaluation of CV health [ 36 , 38 ].
The assessment of CRF for sports and training purposes is also an important function of health fitness professionals [ 11 ]. In fact, the use of apps for assessing other physical fitness components such as muscular fitness is a current practice in the sports field. As examples, the My Jump and PowerLift apps have shown scientific validity and reliability for measuring distinct aspects of muscular performance [ 39 , 40 ]. Further, both apps are being used by many sports professionals in field settings. However, health fitness professionals are demanding valid tools for the remote and objective assessment of CRF. In this context, the usefulness of apps for CRF assessment might provide coaches with additional data difficult to obtain in field settings with traditional approaches. Specifically, an app-based approach for determining CRF in sports could add value to exercise prescription and monitoring training. For instance, with exercise prescription, these apps could be used before a training session to adjust intensities for training in the appropriate intensity zone and obtain the best physiological adaptations for athletes, reducing the risk of overtraining. In this context, an example is the HRV4Training app that provides analyses on the relationship between physiological parameters (taking measures of heart rate and heart rate variability), training and performance. In brief, the HRV4Training app estimates acute heart rate variability changes in response to acute stressors that affect an athlete’s acute physiology. This data can be used for determining athlete fatigue and thus modifying the training program of the athlete from day to day and in real time. Likewise, apps assessing CRF might hold value in monitoring cardiovascular performance changes throughout a conditioning program and tracking injury risk factors affecting cardiovascular functioning. Also, CRF measurements with fitness apps could advance the research field, making CRF assessment more affordable and potentially self-administered by the athletes.
The purpose of this review article is to facilitate a scientific discussion about the new opportunities that advances in apps offer, such as the ability to objectively and remotely assess CRF as a complementary tool to traditional methods for estimating CVD risk, as well as to assess CRF for improving performance in sports and training. To address the purpose of this review and provide evidence-based practical recommendations for researchers, clinicians, and sports professionals, the following original two-pronged approach has been employed: (1) a systematic review of the available scientific literature, examining the validity and reliability of apps designed for CRF assessment, and (2) a systematic analysis of apps estimating CRF and stored within the two major app markets (Google Play and App Store).
The search strategy, criteria, and related terms used in both the scientific literature and the app market search are presented in Supplemental Tables 1–5 in the electronic supplementary material (ESM).
Literature search strategy and study selection process.
The literature search was carried out according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [ 41 ]. The search included four major electronic databases (i.e., PubMed, Web of Science ® , Scopus™, and SPORTDiscus) and the timeframe was set between 01 January 2000 and 31 October 2018. Even though the two main markets, Google Play, and the App Store, were launched between 2007 and 2008, native apps (apps developed for use on a specific device) began to appear commercially around 2000; therefore, the timeframe was set based on the emergence of the first native apps. For searching in PubMed, we used Medical Subject Heading (MeSH) terms and a combination of relevant keywords in the field (see Supplemental Table 1 in the ESM). The same search strategy and the combination of terms were repeated in Web of Science ® , Scopus™, and SPORTDiscus, but without using MeSH terms (see Supplemental Tables 2–4 in the ESM). The reference lists of included articles were also searched for additional studies. Included were studies that examined the validity and/or reliability of apps for assessing CRF using a field-based fitness test. Studies were excluded according to the following criteria: (1) studies written in languages other than English and Spanish, and (2) studies from which we could not access the full text. The selection procedure of the 2796 articles initially identified was undertaken following a two-step approach: (1) screening based on the title and abstract; and (2) search of the full text of the articles selected in the previous step. The first two authors (AM-M and AM-N) independently performed the study selection process and disagreements were resolved in a consensus meeting. The selection process for scientific studies is illustrated in Fig. 1 a.
Flow chart: an overview of the review process for the scientific literature search and the app market search
For selected articles, data extraction was undertaken by the first author (AM-M) and the second author (AM-N) confirmed accuracy. A standardized data extraction form was utilized and is presented in Table 1 . Data extracted included details on source (authors, year, name of the fitness app, and market platform), information about the participants (age, sample size, and gender), fitness test examined, criterion measure used as gold standard, main outcomes studied, statistical methods, and the validity and/or reliability of results.
Scientific studies examining the validity/reliability of apps for assessing cardiorespiratory fitness
Study Name of the app (market platform) | Age (years) ± SD Sample size (male, female) | Fitness test | Measure criterion | Main outcomes | Statistical method | Validity and/or reliability results |
---|---|---|---|---|---|---|
Altini et al. [ ] HRV4Training (Google Play and iTunes) | 25.0 ± 6.2 48 (22, 26) | Prediction of O taking anthropometrics data, physiological data, and training data | Incremental test on a cycle ergometer | O | Correlation coefficient ( ) Bland–Altman plots RMSE | Validity ( = 0.72–0.8 between estimated and measured O ) |
Brinkløv et al. [ ] InterWalk app (iTunes) | 64.2 ± 5.9 27 (9, 18) | InterWalk Fitness Test (sub-maximal) | Indirect calorimetry with a graded walking test protocol on a treadmill | O and O | The correlation coefficient ( ), Leave-one-out cross-validation, ICC, Bland-Altman plots, minimal detectable change | O prediction of the algorithm ( ) was 0.60 and 0.45 and the test–retest ICC was 0.85 and 0.86 when the smartphone was placed in the pockets of the pants or jacket, respectively |
Brooks et al. [ ] SA-6MWTapp (not available in markets) | ( ) 54 ± 19 19 (6, 13) | 6-Minute walk test (sub-maximal) | In-clinic known steps count and measured distance | Step count and distance walked | Paired test, 3-knot restricted cubic splines, tenfold cross-validation, ICC, CV | = 0.88 between in-clinic distance and SA-6MWTapp. The CV during home validation trials was 4.6% |
Capela et al. [ ] 2–6MWT app (not available in markets) | 5 participants | 2-minute walk test (sub-maximal) | Digital video and measuring tape | Distance walked, total number of steps, number of steps per walkway length, cadence, step time, stride time, step time symmetry | The systematic error between studied outcomes and the gold standard | Foot strike time was within 0.07 seconds when compared with gold standard video recordings. The total distance calculated by the app was within 1 m of the measured distance |
Capela et al. [ ] TOHRC Walk Test (Google Play) | 10 males (40.6 ± 15.9 years) 5 females (38.8 ± 9.7 years) | 6-Minute walk test (sub-maximal) | Digital video and measuring tape | Distance walked, total number of steps, number of steps per walkway length, cadence, step time, stride time, step time symmetry | The systematic error between studied outcomes and the gold standard | The average error in the calculated distance was 0.12%. The average difference between smartphone and gold standard foot strike timing was 0.014 ± 0.015 s |
CV coefficient of variation, HR heart rate, HRV heart rate variability, ICC intraclass correlation coefficient, RMSE root mean square error, VO 2 oxygen consumption
The risk of bias within scientific studies finally included was assessed by using some elements of the Cochrane Collaboration’s tool for assessing the risk of bias [ 42 ]. Specifically, the domains analyzed in the present systematic review were detection bias, attrition bias, reporting bias, and other bias.
Apps search strategy and selection process.
The app market searches were conducted in the Spanish Google Play and App Store. However, we also performed manual searches for other relevant apps in the United States app market. Apps from Google Play and App Store were screened in October 2018. The apps were identified by introducing keywords into the search engine of each app market without specified search categories (see Supplemental Table 5 in the ESM). The inclusion criteria were fitness apps that assess CRF with physiological signals, integrated algorithms, and/or fitness apps serving as a simple CRF calculator. However, those apps not in English or Spanish, not readily accessible or unable to be downloaded were excluded. The selection procedure of the apps on both app markets was carried out by the first author (AM-M) using the following two-step method: (1) screening apps based on description and screenshots; and (2) 1 week after the first search, a second screening was performed following the identical selection process. Apps identified in the two steps were selected for download and content assessment. The selection process for apps is illustrated in Fig. 1 b.
Selected apps were downloaded to a smartphone (Apple or Android software) by AM-M and AM-N. In those cases in which an app had a free and a paid (premium/more advanced) version, both apps were downloaded and assessed. Further, the data extraction was undertaken using the following two-step method: (1) AM-M and AM-N independently assessed five apps using two qualitative instruments (explained below); this first step was to ensure that both reviewers used the same criteria to fill both qualitative instruments; and (2) AM-M extracted and scored information of all apps stored on App Store and AM-N did the same with apps stored in Google Play.
Two qualitative assessments were carried out with apps ultimately included in the systematic review. First, the Mobile App Rating Scale (MARS) was used to rate app quality [ 43 ]. The selected apps were tested for at least 10 min and rated with this scale. In brief, the MARS has 29 items measured on a 5-point scale grouped into six domains (engagement, functionality, aesthetics, information, subjective and perceived impact). An overall score was computed as a MARS mean considering four domains (engagement, functionality, aesthetics, information). Second, a standardized instrument was developed specifically for this review to evaluate some features of the apps included (score, ratings, downloads, price, fitness test, test instructions, heart rate measurement, GPS, maximal or peak oxygen consumption ( V O 2 ) estimation, external equipment needed, historic measurement, export results, prompt self-monitoring of behavioural outcome, social network, reference values, scientific validation, multiple user, language, and last update date). Twelve of these items were rated as ‘yes’ or ‘no’, for instance, if an app included CRF reference values, the app was rated as ‘yes’ for this item. The sum of total ‘yes’ responses per app was computed as an average of the quality of the apps. Pearson correlations were used to examine the relationships between the cost of apps, their features, and MARS mean score. All statistical analyses were conducted using IBM SPSS Statistics version 22.4 (Armonk, NY, USA), with significance levels set at p < 0.05.
Studies’ characteristics.
Figure 1 illustrates the flow chart of the scientific review (according to PRISMA), as well as the flow chart of the review of the current app markets. The scientific studies selected are summarized in Table 1 . The results of the scientific literature’s search revealed that there were five studies [ 44 – 48 ] published in peer-reviewed journals, of which only three apps were available to be downloaded on commercial platforms ( HRV4Training , InterWalk app, and TOHRC Walk Test ). HRV4Training [ 44 ] was the only app stored in both app markets (Google Play and App Store). All the included apps were available in English, except for the InterWalk app, which was only available in Danish [ 46 ]. Four studies [ 45 – 48 ] included < 20 participants, and one study [ 44 ] included 48 individuals. Three studies included healthy adults [ 44 , 47 , 48 ], whereas Brinkløv et al. [ 46 ] included participants with type 2 diabetes mellitus and Brooks et al. [ 45 ] included those with congestive heart failure and pulmonary hypertension. The 6-minute walk test was used in two studies [ 45 , 47 ]. Two studies [ 45 , 46 ] were adjudicated to be of low risk of bias and three [ 44 , 47 , 48 ] were considered to have a high risk of bias. The criteria for high risk of bias were (1) the study failed to include the complete methodology to assess validate/reliability of the fitness test with the app; and (2) the study did not entirely report the results or analysis methods of the outcomes studied.
Altini et al. [ 44 ] used information from three sets of predictors (models hereafter) for quantifying CRF: (1) anthropometric data (body mass index, age, and gender) taken from the HRV4Training app; (2) physiological data (morning heart rate and heart rate variability) acquired with the HRV4Training app at rest conditions plus model 1; and (3) training data measured as the ratio between running speed (retrieved from the Strava app and linked to HRV4Training ) and morning heart rate (retrieved from HRV4Training ) plus model 1. The criterion CRF was determined as V O 2max , by means of cardiopulmonary exercise testing (CPX) (incremental protocol on a cycle ergometer). Root mean square error (validity results) was 4.2 ± 3.0 mLO 2 ·kg −1 ·min −1 for model 1, 4.1 ± 3.1 mLO 2 ·kg −1 ·min −1 for model 2 and 3.5 ± 2.8 mLO 2 ·kg −1 ·min −1 for model 3. Participant-independent root mean square error decreased by 15% and 18% when model 3 was compared with model 1 and 2, respectively.
Brinkløv et al. [ 46 ] developed the InterWalk app, integrating the InterWalk Fitness Test. The on-board accelerometer of the smartphone was used as a predictor of peak V O 2 during the test. Specifically, the vector magnitude during the last 30 seconds of the test, body weight, height, and gender were used to create a linear regression equation to predict peak V O 2 . The criterion CRF was determined as peak V O 2 assessed by CPX (graded walking test protocol on a treadmill). The overall peak VO 2 prediction of the algorithm ( R 2 ) was 0.60 and 0.45 when the smartphone was placed in the right pocket of the pants (lower position) or jacket (upper position), respectively ( p < 0.001). No differences were found in peak V O 2 when the test was performed with or without verbal encouragement ( p = 0.70). The reliability (intraclass correlation coefficient, ICC [95% CI]) was 0.86 [0.64–0.96] of the predicted peak VO 2 for the lower position of the smartphone and 0.85 [0.60–0.96] for the upper position.
Brooks et al. [ 45 ] developed the SA-6MWTapp integrating the 6-minute walk fitness test (6MWT). They developed a distance estimation algorithm for the SA-6MWTapp , considering step counts from an ActiGraph GT3X and measured distance on a pre-measured 6MWT course. The best-fit algorithm was incorporated into the SA-6MWTapp . In addition, self-reported information from the app (age, birth date, height, and weight) and heart rate immediately at the end of the 6MWT was collected. The heart rate was taken using photoplethysmography from the user’s finger placed over the phone’s camera at the end of the test. The validation protocol was undertaken with one smartphone placed in a hip holster and the other smartphone placed in the front pants pocket. The correlation between SA-6MWTapp estimated distance and in-clinic measured distance along a pre-measured course was 0.88 (95% CI 0.87–0.86) and the mean difference ± SD was 7.6 ± 26 m ( p = 0.30). The smartphone position did not influence the estimation of measured distance ( p = 0.70). The coefficient of variation from distances estimated from the SA-6MWTapp was 3.2 ± 1 m (home validation phase) and highly correlated with in-clinic measured distance ( r = 0.88 [95% CI 0.87–0.89]).
Capela et al. [ 47 , 48 ] developed the 2–6MWT app and the TOHRC Walk Test app, respectively. The 2-minute walk fitness test and 6MWT, respectively, were integrated into a Blackberry Z10. They used the accelerometer, gyroscope, and magnetometer of a Blackberry Z10 at approximately 50 Hz and developed an algorithm capable of estimating total distance walked, total number of steps, number of steps per length, cadence, step time (left and right steps), stride time, and step time symmetry (left and right steps). The smartphone was placed around the person’s waist using a belt that included a rear pocket (to fit the smartphone) at the center of the lower back. A digital video recorded from a separate BlackBerry 9900 smartphone was used as a gold standard. Capela et al. [ 48 ] found that the foot strike time measured with the 2–6MWT app was within 0.07 s when compared with gold standard video recordings. Furthermore, the total distance calculated by the 2–6MWT app was within 1 m of the measured distance. Capela et al. [ 47 ] showed that the average difference between the TOHRC Walk Test app and gold standard foot strike timing was 0.014 ± 0.015 s. Also, the total distance calculated by the TOHRC Walk Test app was within 1 m of the measured distance for all but one participant.
The app markets search led to a total of 88 apps meeting our inclusion criteria, of which 42 were stored in Google Play and 46 in App Store, with only four apps simultaneously stored on both platforms. The cost of the apps ranged from €0 to €10.99 (mean 1.24, SD 2.15) with more than half offered for free ( n = 53, 60.22%). Google Play ( n = 31, 73.80%) market stored more free apps than App Store ( n = 22, 47.08%). Supplemental Tables 6–7 (in the ESM) show MARS mean and domain scores of the apps rated. Apps were sorted from highest to lowest according to the MARS mean scores (see Supplemental Tables 6–7 in the ESM). The average total MARS score was 2.97 (SD 0.73) out of 5 and 46.59% ( n = 41) had a minimum acceptability score of 3.00. Regarding the MARS domains, functionality was the highest scoring (mean 3.85, SD 0.76), followed by aesthetics (mean 2.79, SD 1.14), information (mean 2.72, SD 0.95), engagement (mean 2.54, SD 0.86), subjective (mean 1.90, SD 1.07) and perceived impact (mean 1.70, SD 1.01). The top five ranked apps in App Store were HRV4Training , MyHeart Counts , Fitness Test pro , AeroExaminer—Aerobic VO 2 Max Test and Conditioning and CardioCoach , respectively. The top five ranked apps in Google Play were HRV4Training , Fitness Test pro , iWalkAssess , Bruce Treadmill Test Lite , and Bruce Treadmill Test Protocol , respectively.
Supplemental Tables 8–9 (in the ESM) provide the reader with a complete set of apps currently available, including a direct link (by clicking on the app’s name) to each specific app, as well as a qualitative evaluation of each of the apps. The 20-m shuttle run test was the most prevalent field-based fitness test used within Google Play and App Store apps ( n = 31, 27.28%). Only one app ( HRV4Training ) [ 44 ] included a measure of a physiological signal and four apps calculated the distance by GPS. Sixty-one apps (69.31%) provided V O 2max estimation without considering any physiological signal, 31 (35.22%) included reference values for maximal/peak V O 2 interpretation, 47 (53.40%) allowed assessments to be saved and 30 (34.09%) had the chance to add multiple users. Five apps (5.68%) required external equipment to estimate CRF, 28 (31.81%) enabled the user to export data, 31 (35.22%) enabled the user to share results in major social networks, and 49 apps of 88 (55.68%) provided test instructions to participants.
Figure 2 shows a comparison between apps stored in Google Play and App Store regarding the quality scoring with MARS (A) and the apps’ features (B). MARS mean was slightly higher for App Store apps in comparison with Google Play (3.17 vs. 2.77). Likewise, all the MARS domains obtained higher scores for App Store apps. Regarding the apps’ features, the App Store repository stored more apps than Google Play in all items except for the V O 2 estimation item. A positive association was observed between the cost of apps and the total MARS mean score ( r = 0.46; p < 0.001). The total number of features was positively associated with the total MARS mean score ( r = 0.55; p < 0.001).
Quality scoring ( MARS , mobile app rating scale) of the apps ( a ) and apps’ futures ( b ). In a , the numbers 0–5 signify the score obtained in each MARS item, whereas in b , the numbers 0–50 refer to the total number of apps that contain such features
The purpose of this report was three-fold: first, to systematically review the validity and reliability of CRF apps assessment available in the scientific literature and app markets; second, to provide evidence-based practical recommendations; and third, to stimulate a scientific discussion on how the information retrieved from these apps might have clinical relevance for the assessment of CV health, as well as for sports performance and training.
One of our major findings is that, despite having identified 88 fitness apps, only five have been tested scientifically; all five for validity [ 44 – 48 ] and only two for reliability [ 45 , 46 ]. Three of the five apps were scored as having a moderate to good validity [ 44 – 46 ]. Nevertheless, there were some limitations, for example, (1) none of the five apps were stored on both app markets for free download and two were not for public use; (2) the sample size of the validation studies was small, and (3) four studies [ 45 – 48 ] used algorithms designed from data collected by the smartphone’s accelerometer, which could impact applicability to other smartphones since the algorithms may not provide valid data when used with other smartphone models.
Despite these limitations, indicating there is not the ability to reach a consensus on a preferred fitness app, some may be useful, particularly compared with performing no CRF assessment. Among them, the MyHeart Count app currently possesses the greatest clinical utility among the commercial apps reviewed. The MyHeart Count estimates CRF by means of the 6MWT, with more than 400 customer ratings and a current score of 4.5 out of 5 (see Supplemental Tables 8–9 in the ESM). In addition, its feasibility has been published [ 26 ] and the app contains many of the conditions described in Fig. 3 . Notwithstanding, the main limitation is that its validity and reliability have not been tested to date; therefore, caution should be taken with its use. Furthermore, MyHeart Counts is currently available in the United States alone, and only to iPhone users (version 5S and later). Regarding the sports field, for both recreational and high-performance sports purposes, the HRV4training app currently has proven to be the most useful tool. However, coaches and athletes should exercise caution when using this app. Although its validity for determining CRF is good, no reliability data is currently available. Moreover, CRF estimation is exclusively available for runners and cyclists linking the HV4training app to the Strava app and using a heart rate monitor and a power meter during their workouts. Also, the HV4training app is the most expensive within the two main markets analyzed in this review.
Apps identified in the scientific literature search and in the app markets review, future research directions, and key factors to be considered when selecting or developing an app for assessing cardiorespiratory fitness. CRF cardiorespiratory fitness, CVD cardiovascular disease, MARS Mobile App Rating Scale
In this review, we sought to contribute to this developing field of research by identifying limitations of current apps and outlining the desirable characteristics that are generalizable to multiple populations with differing needs. Accordingly, we discuss some fundamental points that should be considered when selecting an app among the many options available or when developing a new app. Future research directions based on the knowledge gaps identified herein are also considered.
Most of the apps assessed were based on the maximal CRF test, which, for patient populations, is a clinical standard requiring professionals with specialized training. To broaden applicability, validation of apps using sub-maximal tests to estimate CRF (e.g., 6-minute walk test, 1-mile walk test, etc.) enhance the ability to remotely assess CRF in safer and more feasible conditions. In this regard, a recent major study demonstrated the impact of changes in submaximal CRF on health outcomes [ 49 ]. Additionally, we have found that most apps served as a simple CRF calculator while only one included a physiological signal (e.g., heart rate and heart rate variability) to estimate CRF. The integration of physiological signals into apps might provide more accurate data to better estimate an individual’s CVD risk and/or sports performance. Therefore, technology advancements that incorporate relevant physiological signals into apps is another venue for future research in this area. According to our review, the modes of exercise used for CRF testing include running, walking, stepping, and cycling, with running tests as the most prevalent mode. However, care should be taken when this testing modality is used in individuals with physical limitations and when testing takes place remotely.
Another important aspect to be considered when choosing an app is where the fitness testing will take place. In this context, apps including step tests are a feasible solution when testing is performed in a room with limited resources. Otherwise, the external equipment needed, such as a treadmill or stationary bicycle, is another key factor when selecting an app. Those apps that require additional resources and equipment will make large-scale assessments more challenging and less feasible. Therefore, whenever possible, CRF tests without the need for additional equipment are preferable. It is important to note that submaximal exercise tests with fewer resource and equipment requirements can provide valuable information although they are not as precise as maximal exercise testing [ 37 ].
The cost and language of apps are also important factors, making apps universally accessible to individuals across broad socioeconomic strata. Most apps examined are available in English and half of them are offered at no cost. Another major problem identified in this systematic review was that very few apps (i.e., only four out of 88 [4.54%]) were simultaneously available on both the Google Play and App Store platforms. The low number of apps that are on both platforms can be attributed to different reasons. For instance, apps in the App Store must meet a quality guideline review prior to publication, demanding higher app quality than Google Play; however, the publishing cost is higher than with Google Play. This fact is likely the cause of the higher cost of apps in the App Store. Thus, app developers may choose to publish apps in one or another market depending on these factors. This is an important limitation since, ideally, a researcher, clinician or sports professional would like to use the same app regardless of which type of smartphone the individual may have. Ideally, future efforts in the field should be focused on published apps in the most spoken languages worldwide, that are low-cost, and stored in both app markets for optimal clinical and sports application.
An additional drawback of the scientific and commercial apps reviewed herein is the lack of data on interoperability. Despite the information collected from apps, opportunities for connected healthcare with respect to CRF assessment remains suboptimal; the transmission of patient-generated data, stored in Android and Apple devices, to the patients’ electronic health records has been previously documented [ 50 , 51 ] and should be considered for CRF assessment. In order to achieve app interoperability, a plan is needed to support developments in privacy and data security, as well as interoperability across smartphone devices and app markets, extending data from devices to electronic health records [ 52 ]. As mHealth matures, health information technology interoperability will bring a real integration of patient-generated CRF data into electronic health records, making data device-independent.
In addition, most of the apps already use existing and scientifically validated CRF field-based fitness tests (e.g., 20-m shuttle run test, 6-minute walk test, Cooper Test) transformed into an app format. However, the ability of the resulting apps to assess CRF against a criterion method has rarely been tested. Thus, it is highly recommended, whenever possible, to select scientifically validated apps, and for researchers to test the validity and reliability of existing and newly developed apps. Likewise, other functionalities such as the inclusion of test instructions, the capability to store repeated measurements to later perform longitudinal comparisons, the possibility to export data entered and the main results of the test, the integration of multiple users, and the ability to generate feedback based on results are important factors that should be kept in mind when selecting an app or developing a new one.
Figure 3 presents the main characteristics that would emulate a high-quality fitness app for clinical and sports fields. This information will assist researchers to work together with app developers to design better apps in the future.
In this sense, future potential of the use of apps for CRF testing in a clinical context might encourage patients to seek knowledge about their CRF level, which would, in turn, be translated into the management risk of CVDs [ 53 ]. In addition, high-quality fitness apps would be relatively inexpensive whereas the assessment of well established risk factors for developing a CVD (e.g., cholesterol and blood pressure) requires equipment with a high economic cost. Furthermore, monitoring is usually reserved for individuals with increased risk for or established CVD. Thanks to the universality of apps, people of all ages and socioeconomic status might be encouraged to self-assess their CRF to estimate the lifetime risk of CVD. Low CRF is independent of other CVD risk conditions traditionally controlled in the clinics (e.g., obesity, hypertension, type 2 diabetes, dyslipidemia), therefore the integration of CRF assessments through apps would enhance the traditional method for estimating CVD risk into clinical workflows. Patients do not always recognize themselves as being at CVD risk, hence CRF apps with alarms in case of low CRF would favor the early detection of CV abnormalities.
In the sports context, future apps for CRF testing might allow coaches to integrate this measurement into their routine practice, overcoming the limitations of traditional methods noted above. For instance, a desirable fitness app might have two unique components, one for the athlete to track measurements and the other for coaches to manage data collected from the individual or team as a whole. In this context, coaches might receive athletes’ data remotely, making it more feasible to make adjustments in training programs on a daily basis. Along the same lines, these fitness apps would incorporate a training index, based on daily CRF measurement and other acute stressors, to predict trainability of athletes according to current physiological status. The capability of fitness apps to collect and store CRF measurements effortlessly would make the interpretation of acute and chronic training loads more feasible. Although CRF is per se a recognized indicator of sports performance for recreational and elite athletes, the future ability of fitness apps to collect other physiological and non-physiological parameters may enrich the interpretation of CRF measurements in this field.
The main limitation of this review was the small number of scientific studies identified. A second important limitation was the bias found in some validation manuscripts, which makes it difficult to draw robust conclusions. Further, even though we provided a list of apps currently available in app markets, it is important to note that the volume and turnover of apps are high; thus, it is likely that new applications will appear while others assessed in the current analysis will be defunct in the near future. The strengths of our review include a comprehensive analysis and discussion concerning the opportunities that apps provide for the objective and remote assessment of CRF and their usefulness for clinical and sports/training purposes [ 1 – 6 , 8 – 10 ]. Specifically, our review contributes to the field by providing (1) information on the validity and reliability of apps currently available in the scientific literature; (2) a comprehensive list of apps currently available in app markets, including a qualitative rating of each in order to assist readers with selection of the best apps (Supplemental Tables 6–9 in the ESM, which include a direct link to each app); (3) a list of the key characteristics that a fitness app should have in order to assist readers with the selection of apps, as well as app developers to design better apps in the future; and (4) a list of recommendations for future research directions based on knowledge gaps identified during this systematic review.
There is no doubt that we are witnessing the beginning of a new technologic era in healthcare and sports; however, the validity/reliability of the CRF assessment should be improved in a manner consistent with technological development. In fact, the results from this review demonstrate that few presently available apps have been empirically evaluated and among those that have, not all are valid or reliable. In addition, commercially available apps are mostly of low-to-moderate quality, suggesting that the potential of apps for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations for the future development of apps that objectively and remotely assess CRF as a complementary tool to traditional methods for estimating lifetime CVD risk and for improving athletes’ performance. Likewise, sports practitioners will be able to take advantage of the opportunities that fitness apps offer to evaluate the CRF level of clients remotely and to monitor fitness changes. Collectively, we believe that expanding digitalization is a key component of the future of healthcare and sports, and in turn capitalizing on digitalization for the refinement of CRF assessment, now considered a vital sign [ 37 ], is an important objective that requires continued inquiry.
Below is the link to the electronic supplementary material.
We are grateful to Ms Carmen Sainz-Quinn for assistance with the English language.
The views expressed are those of the authors and do not reflect the official policy or position of the institutions they belong to.
FBO research activity was supported by the Spanish Ministry of Economy and Competitiveness—MINECO/FEDER DEP2016-79512-R; by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement (No. 667302); by the University of Granada, Plan Propio de Investigación 2016, Unit of Excellence on Exercise and Health (UCEES); by the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and European Regional Development Fund (ERDF), ref. SOMM17/6107/UGR; and by the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005-00046/ACTI); the SAMID III network, RETICS, funded by the PN I+D+I 2017-2021 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/002). AMN was supported by the Ministry of Economy and Competitiveness and the Instituto de Salud Carlos III through the CIBERFES (CB16/10/00239) and by the Seneca Foundation through the unit of excellence Grant 19899/GERM/15.
Adrià Muntaner-Mas, Antonio Martinez-Nicolas, Carl J. Lavie, Steven N. Blair, Robert Ross, Ross Arena, and Francisco B. Ortega declare that they have no conflict of interest.
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In conclusion, despite the recent systematic review conducted by Angosto et al. on research that examined the intentions to use and implement apps in the fitness and health sector, or a recent ...
4.1. Publication frequency per year. The first article on fitness apps was published in 2011, and until 2014, the intensity of research was very low. 95.2% of the articles are published from 2014 onwards. In 2014, there was a significant increase in the number of publications, doubling the number of 2013 (Table 3 ).
Our paper adds to the scarcely researched area of training behaviour in fitness app users. There is still no consensus as to the exact definition of fitness app adherence, and there would seem not to be any previous research work that uses a deep learning approach to predict fitness app adherence over time.
A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis. Results: Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps.
The aim of this systematic review was to update research that has analysed the intention to use or adopt fitness apps from 2020 to May 2023, following the study conducted by Angosto et al. (2020 ...
The study focused on scientific research related to personal care applications of fitness, using the keywords "fitness app" and its plural form in English for searching through titles, abstracts, keywords, or topics. Our search criteria are detailed in Table 1.These two keywords represent the technological concept (app) associated with the lifestyle (fitness), whose specific relationship ...
Author summary Technologies such as mobile apps or fitness trackers may play a key role in supporting healthy behaviors and deliver public health interventions during the COVID-19 pandemic. We conducted an international survey that asked people about their health behaviors, and their use of technologies before and during the pandemic. Sixty percent reported using a mobile app for health ...
Despite the flourishing fitness apps industry, existing reviews on fitness apps primarily originate from the healthcare and sports fields. For example, Sama et al. [15] reported that fitness or training applications are the most popular mobile apps in the health and wellness category of the Apple App Store.They further noted that self-monitoring and progress tracking were the two most ...
This paper enriches the research on fitness apps. It is of reference significance to improve users' wellbeing and promote the long-term development of fitness apps. 2 Theoretical background and research hypothesis 2.1 The influence of fitness app use on users' wellbeing.
Considering that mobile fitness applications are one of the necessities in our lives, the user perspective toward the application is a prominent research topic in both academia and industry with ...
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 ...
A growing body of literature has paid attention to fitness apps and self-tracking. The studies range from the socio-political analysis that draws upon Foucauldian theories of self-surveillance and governmentality, socio-psychological and behavioral studies that focus on the effectiveness of health promotion, to Science, Technology and Society (STS) studies that focus on human-tech interactions.
Methods. Usage of three fitness apps was examined over 5 months to assess adherence and effectiveness. Initially, 64 participants downloaded three free apps available on Android and iOS and 47 remained in the study until posttest. With a one group pre-posttest design and checkpoints at months 1, 3, and 5, exercise and exercise with fitness apps ...
Background Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility ...
Fitness app can be useful in managing my daily health..91.86.92.73: Fitness app can be advantageous in better managing my health..87: Fitness app could improve the quality of my healthcare..85: Fitness app improves my capability of managing my health..84: Effort expectancy 31,72: It will be easy to get accustomed to using the fitness app..93.84 ...
The aim of this study is to develop a research model that can broaden understanding of the factors that influence the user acceptance of mobile fitness apps. Drawing from Unified Theory of Acceptance and Use of Technology (UTAUT) and Elaboration Likelihood Model (ELM), we conceptualize the antecedents and moderating factors of fitness app use.
Mobile apps or fitness trackers can deliver these behavior change techniques, such as by enabling users to set their own goals, or to self-monitor some behaviors, as demonstrated in prior reviews [15,16]. During the pandemic, mobile apps and fitness trackers can offer unique benefits, by allowing people to access health support remotely and ...
4 Research Group of Management and Innovation in Sports Science, Leisure and Recreation (GISDOR), Universidad de Sevilla, 41013 Seville, Spain. ... it is shown that the use of the fitness app, as a single download or use element, is not enough to improve habits, satisfaction or the intention to stay in the fitness center. Keywords: ...
Abstract. Sport and fitness mobile applications (SFMAs) have led to significant changes in how people engage in sport and physical activity. This development is part of a broader trend of self-tracking (the 'quantified self') and gamification, whose effects are discussed in an increasing number of publications in the humanities and social sciences.
Mobile health (mHealth) technology enables real-time monitoring and tracking of health and fitness parameters. Despite the rapid proliferation of health and fitness apps, their adoption by smartphone users has been sparsely studied. The present study uses Interactive Qualitative Analysis (IQA), a systems method, to investigate the factors influencing the adoption of health and fitness mobile ...
In the CG, self-monitoring was carried out in the traditional way, with the assignment of manual routines (on paper). ... Liu Y., Avello M. Status of the research in fitness apps: A bibliometric analysis. Telemat. Inform. 2021; 57:101506. doi: 10.1016/j.tele.2020.101506.
This paper reflects on 7 years of experience in mobile health and fitness app development. It analyzes the uptake of a health and fitness app, myFitnessCompanion®, by the healthcare industry and ...
Also, CRF measurements with fitness apps could advance the research field, making CRF assessment more affordable and potentially self-administered by the athletes. Purpose. The purpose of this review article is to facilitate a scientific discussion about the new opportunities that advances in apps offer, such as the ability to objectively and ...