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Physical activity trackers (PATs) such as apps and wearable devices (eg, sports watches, heart rate monitors) are increasingly being used by young adolescents. Despite the potential of PATs to help monitor and improve moderate-to-vigorous physical activity (MVPA) behaviors, there is a lack of research that confirms an association between PAT ownership or use and physical activity behaviors at the population level.
The purpose of this study was to examine the ownership and use of PATs in youth and their associations with physical activity behaviors, including daily MVPA, sports club membership, and active travel, in 2 nationally representative samples of young adolescent males and females in Finland and Ireland.
Comparable data were gathered in the 2018 Finnish School-aged Physical Activity (F-SPA 2018, n=3311) and the 2018 Irish Children’s Sport Participation and Physical Activity (CSPPA 2018, n=4797) studies. A cluster analysis was performed to obtain the patterns of PAT ownership and usage by adolescents (age, 11-15 years). Four similar clusters were identified across Finnish and Irish adolescents: (1) no PATs, (2) PAT owners, (3) app users, and (4) wearable device users. Adjusted binary logistic regression analyses were used to evaluate how PAT clusters were associated with physical activity behaviors, including daily MVPA, membership of sports clubs, and active travel, after stratification by gender.
The proportion of app ownership among Finnish adolescents (2038/3311, 61.6%) was almost double that of their Irish counterparts (1738/4797, 36.2%). Despite these differences, the clustering patterns of PATs were similar between the 2 countries. App users were more likely to take part in daily MVPA (males, odds ratio [OR] 1.27, 95% CI 1.04-1.55; females, OR 1.49, 95% CI 1.20-1.85) and be members of sports clubs (males, OR 1.37, 95% CI 1.15-1.62; females, OR 1.25, 95% CI 1.07-1.50) compared to the no PATs cluster, after adjusting for country, age, family affluence, and disabilities. These associations, after the same adjustments, were even stronger for wearable device users to participate in daily MVPA (males, OR 1.83, 95% CI 1.49-2.23; females, OR 2.25, 95% CI 1.80-2.82) and be members of sports clubs (males, OR 1.88, 95% CI 1.55-2.88; females, OR 2.07, 95% CI 1.71-2.52). Significant associations were observed between male users of wearable devices and taking part in active travel behavior (OR 1.39, 95% CI 1.04-1.86).
Although Finnish adolescents report more ownership of PATs than Irish adolescents, the patterns of use and ownership remain similar among the cohorts. The findings of our study show that physical activity behaviors were positively associated with wearable device users and app users. These findings were similar between males and females. Given the cross-sectional nature of this data, the relationship between using apps or wearable devices and enhancing physical activity behaviors requires further investigation.
Physical inactivity is one of the leading causes of worldwide mortality. There is an urgent need to understand how to increase physical activity levels among young adolescents (typically aged between 11 years and 15 years). The habits developed during early adolescence will continue through adulthood [
The use of apps requires the use of smartphones and consistent internet connectivity. In Ireland, the prevalence of mobile phone use among 13-year-old adolescents has been reported to be 98% [
In Finland, wearable devices are used in 22% of the households [
Both countries perform highly in terms of progression in making societies mediated by digital technology. For examples, out of all the OECD (Organization for Economic Cooperation and Development) countries, Finns use the most amount of mobile data per subscriber (OECD, 2018), and Finland has been ranked the highest for digital services in all of Europe [
Emerging evidence suggests that PATs have a positive effect on physical activity behaviors, particularly as facilitators, rather than as drivers of health behavior change [
Differences have been observed in the way that males and females use the multiple functions of PATs. For example, male adolescents prefer to socialize with their PATs through “banter” and other friendly conversations [
Data for this cross-sectional study were collected in Finland and Ireland during the first half of 2018. Both the Finnish and Irish data were collected from national representative cross-sectional studies. In Finland, the Finnish School-aged Physical Activity (F-SPA) study [
The F-SPA 2018 was based on 2-level cluster analyses [
The CSPPA 2018 was a follow-up and extension to the original 2010 study [
All surveys were completed on either a tablet, laptop, or personal computer in the students’ own classroom and under the supervision of teachers in F-SPA 2018 or specifically trained research assistants in CSPPA 2018. Students who were given permissions by their parents or guardians had the right to withdraw from the study at any time. The completion of the survey was done anonymously and voluntarily. The Finnish study was approved by the ethics committee of the University of Jyvaskyla, Finland, where no number was provided, and the Irish study was approved by the ethics committee of the University of Limerick, Ireland.
For the purpose of comparisons between the studies, only responses from young adolescents aged between 11 years and 15 years were included (Finland, n=3311; Ireland, n=4797) in the final data set. Variables for the country data files were relabeled to allow for merging in SPSS 25.0 (IBM Corp). The details of the measures in both surveys used for this study are reported in the table in
Both surveys collected demographic information on gender, age, disability status, and self-reported socioeconomic status via the Family Affluence Scale (FAS) [
Items of PATs had slight variation (
The Irish version had separate questions on ownership, use, and frequency of use for (1) physical activity apps, (2) smartwatches, (3) heart rate monitors, (4) pedometers, and (5) other devices. For comparison purposes, individuals who responded to only having a pedometer or other device (528/4797; 11.0%) were recoded as not having a PAT since this was not compatible between the 2 studies. There was a slight variation in the frequency of use of the PATs, because the question was, “How often do you use your physical activity tracking device during a typical week” with response options (1) Never, (2) Once, (3) Sometimes, (4) Almost every day, and (5) Every day. Responses of Never were grouped into “own but do not use” and 2-5 were grouped into “own and use.” Null responses to the ownership were deemed as “do not have.”
The other survey responses used included the self-reported number of days of at least 60 minutes of moderate-to-vigorous physical activity (MVPA) participation in both 2018 F-SPA and CSPPA studies. The CSPPA 2018 study included 2 items based on the past 7 days and the usual week. The 2 items were summed and divided by 2 and rounded up, whereas the F-SPA 2018 item included 1 item based on the past 7 days. Previous studies suggest that an average between the previous week and the usual week can provide a more accurate recall of physical activity behaviors [
Respondents provided details of their mode of transport to school with walking or cycling categorized as “active commuters.” Motorized transport included options such as getting a lift by parents or taking the bus. The distance between the primary home and school was also asked. To ensure that the distances were plausible for active transport, the Finnish legislation for the provision of free transport costs were set at distances over 5 km as the cut-off point to differentiate between people who were close (within 5 km) and far (over 5 km). For the inferential statistics regarding active commuting, only respondents who lived within the close range (5 km) of the school were included. Therefore, living beyond 5 km was an exclusion criterion for the analyses in relation to active commuting.
Descriptive statistics of the population characteristics were produced by chi-square tests of independence for gender, after stratifying for country. The test of independence between the countries was also tested through chi-square tests after considering the gender. A two-step approach was used to describe the phenomenon of PAT ownership and use among young adolescents in Finland and Ireland. The number of possible combinations of PAT habits was investigated using cluster analysis to the fewest number of clusters, yet attempting to retain a meaningful structure (ie, values of the average silhouette width defining the cluster quality as “good” [exceeding 0.5]) [
Chi-square tests were used to assess the statistical significance of gender, age groups, FAS, and disability for the clusters, and the Kruskal-Wallis test with pairwise comparisons was used to assess the statistical significance of the differences in the average number of days reporting 60 minutes of MVPA for each country.
The binary logistic associations of meeting the physical activity guidelines (7 days vs <7 days, reference category), being an active traveler (cyclist and walker vs motorized transport who live within 5 km of the school, reference category), and organized sport participant (sports club member vs not active in sports clubs, reference category) with no ownership of PATs as the reference category were investigated. The crude associations for each indicator (Model 1) were assessed before adjusting for age, gender, FAS, and disability (Model 2). All statistics were run using SPSS 25.0 for Windows (released 2017).
The descriptive statistics are provided in
Descriptive statistics of the samples by country and gender.
Characteristics | Finland (n=3311) | Ireland (n=4797) | Totala (N=8108) | ||||||||||||||||
|
Males, n= |
Females, n=1701, n (%) | Males, n=2370, n (%) | Females, n=2427, n (%) | Males, n= |
Females, n= |
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|
.81 |
|
|
.003 |
|
|
.01 | ||||||||||
|
11 | 628 (39.0) | 680 (39.9) |
|
402 (16.9) | 468 (19.3) |
|
1030 (25.9) | 1148 (27.8) |
|
|||||||||
|
13 | 452 (28.1) | 477 (28.0) |
|
1090 (45.9) | 1168 (48.1) |
|
1542 (38.7) | 1645 (39.8) |
|
|||||||||
|
15 | 530 (32.9) | 544 (31.9) |
|
878 (37.0) | 791 (32.6) |
|
1408 (35.4) | 1335 (32.3) |
|
|||||||||
|
|
|
.81 |
|
|
.05 |
|
|
.09 | ||||||||||
|
Low | 357 (24.8) | 374 (24.4) |
|
495 (20.9) | 529 (21.8) |
|
852 (22.4) | 903 (22.8) |
|
|||||||||
|
Middle | 827 (57.4) | 874 (56.9) |
|
1398 (58.9) | 1351 (55.7) |
|
2225 (58.4) | 2225 (56.2) |
|
|||||||||
|
High | 256 (17.8) | 287 (18.7) |
|
477 (20.1) | 546 (22.5) |
|
733 (19.2) | 833 (20.2) |
|
|||||||||
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|
|
.07 |
|
|
.66 |
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|
.17 | ||||||||||
|
None | 1439 (89.9) | 1493 (87.8) |
|
2035 (85.9) | 2073 (85.4) |
|
3474 (87.5) | 3566 (86.5) |
|
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|
Disabled | 161 (10.06) | 204 (11.9) |
|
335 (14.1) | 354 (14.6) |
|
496 (12.5) | 558 (13.5) |
|
|||||||||
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|
|
<.001 |
|
|
<.001 |
|
|
<.001 | ||||||||||
|
Inactived | 1041 (64.8) | 1211 (71.3) |
|
1955 (82.5) | 2157 (88.9) |
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2996 (75.3) | 3368 (81.6) |
|
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Activee | 566 (35.2) | 488 (28.7) |
|
415 (17.5) | 270 (11.1) |
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981 (24.7) | 758 (18.4) |
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|
.15 |
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|
<.001 |
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|
.02 | ||||||||||
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Motorized, Close | 213 (13.5) | 213 (12.7) |
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504 (28.7) | 583 (33.1) |
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717 (21.5) | 796 (23.1) |
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Active, Close | 916 (58.1) | 1005 (59.8) |
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430 (24.5) | 335 (19) |
|
1346 (40.4) | 1340 (39) |
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Active, Far | 80 (5.07) | 60 (3.6) |
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44 (2.5) | 30 (1.7) |
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124 (3.7) | 90 (2.6) |
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Motorized, Far | 368 (23.3) | 403 (24) |
|
779 (44.3) | 811 (46.1) |
|
1147 (34.4) | 1214 (35.3) |
|
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|
.64 |
|
|
.04 |
|
|
.06 | ||||||||||
|
Nonmember | 629 (40.5) | 687 (41.31) |
|
852 (35.95) | 941 (38.77) |
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1481 (38.8) | 1628 (39.8) |
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Member | 924 (59.5) | 976 (58.69) |
|
1518 (64.05) | 1486 (61.23) |
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2442 (61.2) | 2462 (60.2) |
|
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|
<.001 |
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|
<.001 |
|
|
<.001 | ||||||||||
|
Not owned | 624 (38.7) | 649 (38.2) |
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1602 (67.6) | 1457 (60) |
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2226 (55.9) | 2106 (51.0) |
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Do not use | 342 (21.2) | 244 (14.3) |
|
274 (11.6) | 347 (14.3) |
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616 (15.5) | 591 (14.3) |
|
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Use | 644 (40) | 808 (47.5) |
|
494 (20.8) | 623 (25.7) |
|
1138 (28.6) | 1431 (34.7) |
|
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|
|
<.001 |
|
|
.56 |
|
|
<.001 | ||||||||||
|
Not owned | 1080 (68.6) | 1240 (74.1) |
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1836 (77.5) | 1903 (78.4) |
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2916 (73.9) | 3143 (76.6) |
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|
Do not use | 269 (17.1) | 182 (10.9) |
|
169 (7.1) | 155 (6.4) |
|
438 (11.0) | 337 (8.2) |
|
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Use | 225 (14.3) | 252 (15.1) |
|
365 (15.4) | 369 (15.2) |
|
590 (15.0) | 621 (15.1) |
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|
|
<.001 |
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|
.19 |
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|
<.001 | ||||||||||
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Not owned | 1119 (71.4) | 1331 (79.6) |
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2146 (90.5) | 2230 (91.9) |
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3265 (82.9) | 3561 (86.9) |
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Do not use | 285 (18.2) | 201 (12) |
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55 (2.3) | 55 (2.3) |
|
340 (8.6) | 256 (6.2) |
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Use | 164 (10.5) | 141 (8.4) |
|
169 (7.1) | 142 (5.9) |
|
333 (8.5) | 283 (6.9) |
|
aThe percentages in this column are the actual percentages and not of the total population because some data on the variables of the total population were missing.
bFAS: Family Affluence Scale.
cMVPA: moderate-to-vigorous physical activity.
d0-6 days of MVPA.
e7 days of MVPA.
There were statistical differences between the characteristics of Finnish and Irish young adolescents between the 2 surveys. The CSPPA 2018 study had fewer 11-year-old adolescents than those in the F-SPA 2018, as the participants were more evenly distributed across the varying age groups in the F-SPA 2018 (chi-square
The estimates of ownership and usage of apps (
The 4 clusters were ”no PATs,” “PAT owners,” “app users,” and “wearable device users” (
Features of the four clusters from pooled data and crude estimates of the behaviors.
Features | Cluster 1 (No PATs), n=3523, n (%) | Cluster 2 (PAT owners), n=677, n (%) | Cluster 3 (app users), n=2200, n (%) | Cluster 4 (wearable device users), n=1631, n (%) | |||||
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None | 3523 (100.0) | 265 (39.1) | 0 (0) | 531 (32.6) | ||||
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Own | 0 (0) | 412 (60.9) | 701 (31.9) | 85 (5.2) | ||||
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Use | 0 (0) | 0 (0) | 1499 (68.1) | 1015 (62.2) | ||||
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None | 3523 (100.0) | 72 (10.6) | 2200 (100) | 256 (15.7) | ||||
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Own | 0 (0) | 605 (89.4) | 0 (0) | 167 (10.2) | ||||
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Use | 0 (0) | 0 (0) | 0 (0) | 1208 (74.1) | ||||
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None | 3523 (100.0) | 286 (42.2) | 2200 (100) | 814 (49.9) | ||||
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Own | 0 (0) | 391 (57.8) | 0 (0) | 204 (12.5) | ||||
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Use | 0 (0) | 0 (0) | 0 (0) | 613 (37.6) | ||||
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Daily MVPAa | 576 (16.3) | 130 (19.2) | 503 (22.9) | 498 (30.6) | ||||
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Active Transportb | 1033c (59.6) | 236d (63.3) | 804e (66.1) | 571f (69.1) | ||||
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Sports club | 1957 (56.2) | 399 (59.6) | 1333 (61.4) | 1178 (73.1) |
aMVPA: moderate-to-vigorous physical activity.
bFewer people in this subcategory met the criteria of living within 5 km; therefore, the sample population for this row is different in each cluster as shown in the following footnotes.
cn=1732.
dn=373.
en=1217.
fn=826.
In the unadjusted model (Model 1,
Male-adjusted odds ratios and 95% confidence intervals without Model 1 and with Model 2 confounders for each cluster. Italics represents statistically significant associations.
Variables | Moderate-to-vigorous physical activitya, ORb (95% CI) | Active travelc, OR (95% CI) | Sports clubd, OR (95% CI) | ||
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No PATs | Reference (1.0) | Reference (1.0) | Reference (1.0) | |
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PAT owners | 1.17 (0.89-1.53) |
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1.03 (0.82-1.29) | |
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App users |
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Wearable device users |
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No PATs | Reference (1.0) | Reference (1.0) | Reference (1.0) | |
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PAT owners | 1.07 (0.81-1.42) | 1.19 (0.84-1.69) | 1.08 (0.85-1.37) | |
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App users |
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1.06 (0.82-1.36) |
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Wearable device users |
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Finland | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Ireland |
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Young | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Older |
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Lower | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Higher |
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Without | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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With |
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1.00 (0.74-1.40) |
|
aReference=not daily, Nagelkerke
bOR: odds ratio.
cReference=motorized, Nagelkerke
dReference=not member, Nagelkerke
In the unadjusted model (Model 3,
Female-adjusted odds ratios and 95% confidence intervals without Model 3 and with Model 4 confounders for each cluster. Italics represents statistically significant associations.
Variables | Moderate-to-vigorous physical activitya, ORb (95% CI) | Active travelc, OR (95% CI) | Sports clubd, OR (95% CI) | ||
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None | Reference (1.0) | Reference (1.0) | Reference (1.0) | |
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Owners | 1.21 (0.86-1.72) | 0.92 (0.65-1.30) |
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App user |
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Wearable device user |
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None | Reference (1.0) | Reference (1.0) | Reference (1.0) | |
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Owners | 1.27 (0.87-1.84) | 1.14 (0.76-1.72) | 1.27 (0.97-1.66) | |
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App user |
|
0.98 (0.76-1.25) |
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Wearable device user |
|
0.91 (0,68-1.21) |
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Finland | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Ireland |
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Young | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Older |
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Lower | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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Higher |
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Without | Reference (1.0) | Reference (1.0) | Reference (1.0) |
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With | 1.00 (0.76-1.31) | 1.19 (0.89-1.58) |
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aReference=not daily, Nagelkerke
bOR: odds ratio.
cReference=motorized, Nagelkerke
dReference=not member, Nagelkerke
Apps were owned by approximately two-thirds of the Finnish adolescents and by one-third of the Irish adolescents, with more females in both countries owning apps than males. The estimates of sports watch ownership or use is 28.6% (928/3311) among young Finns and 22.1% (1058/4797) among young Irish adolescents. Approximately 9.2% (305/3311) of the Finnish adolescents and 6.5% (311/4797) of the Irish adolescents use heart rate monitors. Despite these differences, the clustering patterns of PATs were similar between both the countries.
Four cluster patterns for PATs were identified: (1) no PATs, (2) PAT owners, (3) app users, and (4) wearable device users. Compared to individuals in the no PATs cluster, wearable device users had stronger association with physical activity behaviors (daily MVPA, sports club member, active travel). The likelihood of taking part in daily MVPA, being a member of a sports club, or travelling to school by foot or bike among females was higher than that in males, thereby indicating strong positive associations between PAT usage and physical activity behaviors.
More males than females reported meeting the physical activity guidelines of daily MVPA for at least 60 minutes per day [
The majority of the reports from the adult surveys suggest that there are similarities in the use of PATs between Finland and Ireland [
Both male and female users of apps and wearable devices had positive associations with daily MVPA compared to adolescents with no PATs. However, the physical activity behaviors of young adolescents who merely own but reported to not use PATs were not statistically different from that of individuals in the no PATs cluster. Some of the underlying reasons for these results can be related to the ownership and usage of PATs as a proxy for readiness for the behavior [
Similar to the users’ associations with MVPA, app users and wearable device users were more likely to report memberships in sports clubs, when compared to individuals with no PATs. Depending on the features and functionality of the specific PATs that individuals use, young adolescents can share data with other members of the sports club. This may increase motivation among males, as males are known to boast about their achievements with their peers [
After adjusting for country, age, family affluence, and disabilities, the only significant association observed was between the male users of wearable devices and active travel. Although there are studies that suggest that PATs can help support more walking [
Research on active travel is limited in terms of PATs; however, there have been some initiatives to promote active travel directly or indirectly through gamification [
Other initiatives for promoting active school travel and physical activity in general in schools may be created by using step challenges [
Other innovative ways to increase active transport require the combination of technology with the Internet of Things, relying upon multiple sensors such as gyroscopes, GPS, and connectivity sensors so that students can interact more with each other [
Despite the differences in the levels of ownership and usage of PATs, this study found similarities in the clusters between Finnish and Irish adolescents. One of the limitations of the cluster analysis is the data-driven approach, which may lack representativeness outside of the population studied [
In both Finland and Ireland, there is a clear association between affluence and frequency in taking part in organized sports [
The data in this study were collected through self-report surveys, and reporting bias from this type of measurement tool is a common limitation in cross-sectional survey-based studies. The data in this study were collected from national representative samples, and such inconsistencies would be typically eradicated by using larger representative samples. Although we attempted to harmonize our data as much as possible, not all items were the same, specifically when translated into the English language. However, the cultural translation, rather than the literal translation, was used in the study to make comparisons possible. This process was carried out by a researcher (KN) with competences in both languages and cultures. Other study limitations are that some residual confounders may be more relevant in one country when compared to the other and therefore were not comparable although stratification by gender and controlling for country, age, family affluence, and disability were included in the adjusted models. Finally, the survey and data collection only gave the options for the respondents to report 3 main types of PATs, and as the market continues to grow, the researchers may have missed some information related to the behaviors from other types of PATs, and the time during which the individual has owned the PATs. The results of this study were cross-sectional, and the length of time that the individuals have been using PATs has not been reported. Increased understanding about the PAT use of young adolescents is needed to not only consider it as a useful tool for promoting physical activity during the adolescent years but also to use it as a part of the daily life at a later stage in adulthood.
The growing pervasiveness of PAT use across both Finland and Ireland is evident in our study, with similar clustering properties. The association between PAT usage and MVPA provides very useful information for both researchers and practitioners. Evidence from this study highlights the positive physical activity behaviors in adolescents who regularly use and wear PATs, particularly with regards to males. The emergence, pervasiveness, and reducing cost of wearable PATs presents opportunities for researchers to incorporate these into interventions to promote physical activity among young adolescents. Moreover, the application of evidence emerging from physical activity behavior change studies could inform the design and function of future PATs. National efforts in Finland and Ireland should consider using effective dissemination strategies seeking to increase the prevalence of youth gaining access to these wearable devices, while of course acknowledging the feasibility and cost constraints in existence. Advances in technology coupled with reductions in the cost of PATs offer researchers a more viable opportunity to target adolescent-specific physical activity interventions to increase the number of individuals meeting the physical activity guidelines.
Details of the measures in both surveys.
Children’s Sport Participation and Physical Activity
Family Affluence Scale
Finnish School-aged Physical Activity
moderate-to-vigorous physical activity
Organization for Economic Cooperation and Development
odds ratio
physical activity tracker
We would like to acknowledge the F-SPA and CSPPA study research teams involved in the data collection. Data collection of F-SPA 2018 was funded by the Ministry of Education and Culture (grant number: OKM/23/626/2018) involving researchers from University of Jyvaskyla, LIKES research center for physical activity, and the UKK institute. The CSPPA 2018 study was funded by Department of Transport Tourism and Sport, Sport Ireland; Department of Health, Healthy Ireland; and Sport Northern Ireland involving researchers from the University of Limerick, University College Cork, Dublin City University, and Ulster University.
None declared.