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. 2017 Sep 19;7(4):891–901. doi: 10.1007/s13142-017-0525-x

Table 6.

Predicting frequency of app use by type of app

Healthy eating Keeping a diet Losing weight Maintain weight Sport/exercise Improve health
β p β p β p β p β p β p
Gender .11/.08 .034/.151 .09/.02 .105/.712 .18/.08 .001/.144 .01/− .01 .847/.889 .03/.03 .553/.640 − .13/− .13 .015/.021
Age − .07/− .06 .234/.291 − .12/− .08 .034/.143 − .11/− .05 .052/.311 − .03/− .03 .641/.579 .09/.11 .114/.047 .01/− .01 .896/.852
Income − .05/− .06 .359/.251 .03/.02 .543/.726 .01/.01 .785/.831 − .05/− .06 .371/.265 − .02/− .04 .747/.456 − .14/− .15 .007/.005
Education − .08/− .07 .159/.195 .01/.03 .802/.511 − .07/− .04 .204/.448 − .01/− .00 .919/.978 − .06/− .05 .288/.377 .08/.08 .124/.117
BMI − .01/− .03 .855/.667 .12/.11 .035/.054 .17/.13 .002/.014 − .07/− .08 .223/.179 − .07/− .04 .181/.521 .11/.12 .039/.040
Excessive exercise .09 .084 .18 .000 .12 .019 .10 .066 .28 .000 .08 .146
Drive for thinness .14 .018 .27 .000 .33 .000 .08 .186 .09 .117 .02 .734
Sensation seeking − .08 .155 − .02 .679 − .01 .924 − .13 .020 − .04 .411 − .06 .241
Neuroticism − .07 .195 − .08 .156 − .00 .944 − .04 .501 − .05 .344 − .02 .753
R 2 .03/.06 .02/.12 .06/.18 .01/.04 .01/.10 .05/.06

Note. Each model run separately by type of app. Presented are standardized regression coefficients and p level; the coefficients from steps 1 and 2 are divided by slash; all analyses were conducted on data from respondents with no missing data on all variables (n = 369), significant associations (p < .05) are in bold