Pauliina Husu

Light physical activity (LPA) has typically been defined as any physical activity that increases the energy expenditure from the resting state to 1.5–2.9 metabolic equivalents (METs). Examples of L...

Background: Physical activity (PA) has an important role in the prevention and treatment of type 2 diabetes (T2D). Interventions with mobile-based technology (mobile health [mHealth]) seem promising in PA promotion, but their behavioral framework is often vague, and the implementation is seldom reported. Objective: This paper examines perceived behavior change needs and implementation of an mHealth approach in increasing nonexercise PA in patients with T2D. Methods: A 3-arm mHealth intervention was conducted in primary care. Information on perceived behavior change needs was collected with a modified capability, opportunity, motivation—behavior (COM-B) questionnaire before the intervention from a separate sample of patients with T2D (n=25) and at the intervention baseline (n=119). Implementation evaluation focused on the fidelity and acceptability of the main arm of the intervention (n=39), which included 24-hour accelerometer use, a smartphone app with personal feedback, a PA leaflet, a YouTube video on walking, and individual counseling with 3 face-to-face sessions and 4 telephone contacts. Data on fidelity were accumulated during the intervention through counseling cards and cloud computing. Data on acceptability were collected with a questionnaire at the end of the intervention (Likert scale from 1 to 5). Data analysis was mainly descriptive. Results: The participants’ responses revealed 3 items in capability and 2 in motivation, which stood out as perceived behavior change needs. Moreover, the main intervention arm showed good fidelity (eg, face-to-face sessions: 112/117, 96% and telephone contacts completed: 145/156, 93%; mean weekly accelerometer use 54%; ranging from 80% to 17% during the intervention) and acceptability (mean score ranging from 3.8 to 4.8), although some challenges were also experienced, especially in cloud-computed feedback and accelerometer-app use. Conclusions: The findings on behavior change needs call for additional research since no comparable studies were found. In addition, the explanatory value of the COM-B model and the psychometric properties of the COM-B questionnaire deserve further attention. The main intervention arm seemed applicable to clinical practice. However, the challenges discovered underscore the importance of pretesting technology-based approaches in patients with T2D.

Although physical activity (PA) is an established pastime, digital gaming (DG) has created a new social world for young people. Scales to measure digital gaming relationships (DGRs) and physical activity relationships (PARs) are in their infancy. Therefore, the aim of this study was to investigate the properties and differences of the DGR and PAR scales used by adolescents. A convenience sample of high school students was recruited for the study. Participants completed the same questionnaire twice, with a 3‐week interval between administrations. After merging and matching the two sets of responses, data from 116 students were analysed using intraclass correlation coefficients to assess intrarater reliability and principal component analyses to evaluate convergent validity. T ‐tests were used to determine gender differences in the DGR dimensions and linear associations with gaming and PA behaviour. After determining reliable items, five dimensions were found (competitiveness, social aspects, self‐development, mental health and functional features). Cronbach′s alphas for each dimension ranged from 0.74 to 0.94. Scores from males in four of the five dimensions were statistically significantly higher than females, and there were linear associations with gaming behaviour in four dimensions. To conclude, the DGR and PAR scales seem to be suitable for use by adolescents in surveys.

Background : Finland’s 2026 Report Card on Physical Activity for Children and Adolescents was prepared as a part of the Global Matrix 5.0 coordinated by the Active Healthy Kids Global Alliance. This paper summarizes the results gathered for the Report Card and provides grades for 10 indicators concerning the status and promotion of physical activity (PA) among Finnish school-aged children and adolescents. Methods : The most representative data of 10 indicators describing PA of 7–15-year-olds and its promotion in Finland was gathered. Based on the data, each indicator was assigned a letter grade (A–F) by a group of specialists using the harmonized grading rubric developed by Active Healthy Kids Global Alliance. Subgroup analyses were conducted for age, sex, gender identity, socioeconomic status, residence (urban vs rural), ethnicity, language of instruction at school, and children and adolescents with disabilities or chronic conditions. Results : The grades ranged from A+ to F. The highest grades (80%–100%) were assigned to Government (A+), Overall Physical Activity (A−), and School (A−). Active Transportation (B+), Community and Environment (B+), and Organized Sport (B−) received the second-highest grades (60%–79%). Family and Peers (C+), Active Play (C−), and Physical Fitness (C−) were classified within the midrange (40%–59%). Sedentary behavior was assigned the lowest grade (F, Conclusions : Children’s and adolescents’ PA is strongly promoted in Finland based on government, community, and school indicators. Although overall PA level is excellent, the notable existing disparities between subgroups highlight the need for actions that reach the least active individuals.

Objectives : This study applied a robust predictive machine learning framework to a comprehensive set of individual background data to detect variables that are predictive of extremely low and high amounts of moderate to vigorous physical activity (MVPA). The main aim was to detect predictive variables, and as a secondary aim, the model accuracy was also assessed. Additionally, the analysis framework is presented for further use in physical activity research. Methods : Comprehensive data on health, physical functioning, education, work, and the living environment, represented by 130 variables, from 1536 participants aged from 20 to 69 years were used for analysis. The 2.5th and 97.5th quantiles of MVPA were chosen to represent the extreme physical behaviors in the population. Random forest classifiers were trained separately to predict the low and high MVPA groups. Results were confirmed and predictive variables detected with permutation tests and the Wilcoxon signed-rank test, Bonferroni-corrected for P < .05. Results : Nine significant predictors were found for the extremely low MVPA and 6 for the extremely high MVPA. These variables were related to the individual’s mobility function, living environment, and occupation. The areas under the receiver operating characteristic curve were 0.73 and 0.59 for the participants with extremely low and high amounts of MVPA, respectively. Conclusions : Several significant predictive variables for extremely low and high MVPA were discovered that may help characterize factors associated with PA behavior at the population level. However, it is not possible to accurately identify physically highly inactive or highly active individuals only from comprehensive background data.

Aims: Receiving or lacking support can be decisive in how children engage in and continue with sports hobbies or physical activity (PA) in general. The topic is timely, since the PA levels of children are currently insufficient. However, children’s own experiences concerning the support they receive are under-researched. The purpose of this study is to explore from whom, to what extent and what kind of support children experience receiving for PA. Methods: This mixed-methods study collected empirical data among 11-year-old children, regarding children’s own perceptions of PA by local interviews ( n = 36) as the main primary material, complemented with a local survey ( n = 114), and national survey ( n = 1765) conducted in Finland. In addition, access to the local respondents’ accelerometer measurements conducted in five schools in a city in Southern Finland was utilised to characterise the interviewees in terms of their PA. The various data were gathered in 2021 and 2022. Results: According to data from both local and national surveys, children experience receiving support for PA from multiple actors (particularly from parents, teachers, hobby instructors/coaches, friends). Support styles vary from coercive to enabling, encouraging and participatory support. The combination of support from different actors can be reinforcing, remedial or destructive, depending on the type and amount of support and a child’s experiences regarding the support. Conclusions: To reinforce positive support experiences and to avoid destructive support combinations, shared responsibility and congruence regarding the provision of support for children’s PA is called for.

Background: Physical functioning is traditionally assessed and presented as self-reported measures, which have shown either improvement or stability during the last decades at population level. On the other hand, objectively measured, performance-based measures of physical functioning have indicated increased body weight and decreased physical fitness. Therefore, there is a need for objective, measured total physical functioning index (MePFIX). Methods: The MePFIX was developed from the data of 5238 working-aged adults (60% women) who took part in the FINFIT studies from years 2017 and 2021. The outcomes of measured body composition, cardiorespiratory fitness, muscular fitness, physical activity, stationary behavior, and time in bed were used to create the MePFIX. Results: The best performing index contained the fol-lowing measures: waist circumference, estimated maximal oxygen consumption, modified push-up, mean of daily 1-minute metabolic equivalent, high-movement time in bed, lying and reclining during waking hours >20-min bouts, standing, daily step count, moderate-to-vigorous physical activity in absolute terms, and light physical activity relative to fitness. Conclusions: The developed MePFIX is based on population-based data, containing measured outcomes of body composition, cardiorespiratory fitness, muscular fitness, physical activity, sedentary behavior and time in bed. The index could serve as an objective indicator of physical functioning among the target population. The MePFIX can be used as a figure of merit of measured physical functioning for research community and policy makers both at cross-sectional and at longitudinal analyses.

Background: This study aimed to examine the adherence to selected health‐related fitness (HRF) tests in adults. In addition, we investigated how excluded participants or those who did not attend HRF tests differed in their background characteristics from those who attended.Methods: Two population study samples comprising 3867 (1594 males) 20─69‐year‐old participants performing HRF tests, or 1249 (456 males) participants answering only the study questionnaire, were pooled for the analyses. The selected HRF tests were: one‐leg stand, neck‐shoulder mobility, jump‐and‐reach, modified push‐ups, and 6‐min walking test. The exclusion rate was analyzed separately for each test.Results: In total, 14.9% of the participants were excluded from at least one test. Failure to meet the health criteria and unwillingness to perform the test were the most typical reasons for the exclusion. The exclusion rate was highest in the modified push‐up test (13.2%), while in all other tests, the rate was less than 5%. Excluded participants were more likely (p < 0.001) older (46–69 years) (OR = 4.59), not meeting physical activity recommendations for endurance (OR = 2.28) and perceiving their health (OR = 3.69) and fitness (OR = 3.26) as poor. Similarly, participants who answered only the questionnaire were more likely (p < 0.01) to perceive their health (OR = 1.56) and fitness (OR = 1.41) as poor and not meeting physical activity recommendations for endurance (OR = 1.48).Conclusions: The assessed HRF tests were feasible in terms of low exclusion rates. Individuals that were excluded or not participating the tests differed in their background characteristics from those who attended highlighting the importance of feasible testing methods to achieve a representative population sample of participants.

Reliable and valid data on physical activity (PA) and sedentary behavior (SB) are needed for implementing evidence-based interventions and policies. Monitoring of these behaviors is based on PA questionnaires (PAQs) and device-based measurements, but their comparability is challenging. The present study aimed to investigate the test–retest reliability and concurrent validity of Finnish versions of the widely used PAQs (IPAQ-SF, EHIS-PAQ, GPAQ, Eurobarometer) and to compare their data with accelerometer data. This study is based on the Finnish data of the European Union Physical Activity and Sport Monitoring project (EUPASMOS). Participants (n = 62 adults, 62% women) answered the PAQs twice, one week apart, and wore an accelerometer for these seven consecutive days. Intraclass correlations, Spearman’s rank correlations, t-tests, and Cohen’s kappa with bootstrap confidence intervals were used to analyze the data. The PAQs had typically moderate-to-good test–retest reliability (ICC 0.22–0.78), GPAQ, EHIS-PAQ, and Eurobarometer showing the highest reliability. The PAQs correlated with each other when assessing sitting and vigorous PA (R = 0.70–0.97) and had a fair-to-substantial agreement when analyzing adherence to the PA recommendations (74–97%, Cohen’s kappa 0.25–0.73). All the PAQs had a poor criterion validity against the accelerometry data. The Finnish versions of the PAQs are moderately reliable and valid for assessing PA, adherence to PA recommendations and sitting among adult participants. However, the poor criterion validity against accelerometer data indicates that PAQs assess different aspects of PA constructs compared to accelerometry.