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The adverse health effects of sedentary behavior and prolonged sedentary bouts are well-known. However, it is still unknown how physical activity can modify adverse health impacts related to sedentary behavior. The purpose of this study was to develop signal analysis methodology for sedentary behavior and physical activity classification from raw data of a hip-worn accelerometer and to investigate associations of patterns of sedentary behavior with lipid and glucose metabolism. A machine learning model was developed and validated using acceleration data, which included nine predefined and controlled typical daily activities ranging in intensity from sedentary to vigorous physical activity. Acceleration data was collected from 22 Finnish adults using a triaxial accelerometer attached to an elastic belt on a hip. The data were classified into five categories (lying down, sitting, and light, moderate, and vigorous physical activity). Thirty-six middle-aged Finnish adults wore an accelerometer for 14 days, and their sedentary behavior and sitting characteristics were determined. In addition, associations of sedentary behavior, sitting, and physical activity with glucose and lipid metabolism were investigated in the Northern Finland Birth Cohort 1966 46-year follow-up (n=5,832). Participants completed health and lifestyle questionnaires and attended clinical examinations and two weeks of sedentary behavior and physical activity measurements. Isotemporal substitution modeling was used for investigating time reallocations from sedentary to physical activities. The developed machine learning model provided acceptable accuracy for sedentary behavior and physical activity classifications. The method can be used for describing characteristics of sedentary behavior and sitting separately. Patterns of SB were more consistently associated with lipid metabolism than those of sitting. Associations between sedentary behavior and cardiometabolic health depended on moderate-t, Tiivistelmä Runsaan paikallaanolon ja pitkien paikallaanolojaksojen haitalliset vaikutukset terveyteen ovat hyvin tiedossa, mutta edelleen puuttuu tietoa, kuinka fyysinen aktiivisuus voi muokata paikallaanolon haitallisia vaikutuksia terveyteen. Työn tarkoituksena oli kehittää signaalinkäsittelymenetelmä paikallaanolon ja fyysisen aktiivisuuden luokittelemiseksi lantiolla pidettävän aktiivisuusmittarin raakakiihtyvyyksistä sekä tutkia paikallaanolon piirteiden yhteyksiä rasva- ja sokeriaineenvaihduntaan. Koneoppimismalli kehitettiin ja validoitiin käyttämällä kiihtyvyysdataa, joka sisälsi yhdeksän ennalta määriteltyä ja kontrolloitua tyypillistä arkiaktiviteettia, joiden intensiteetti vaihteli paikallaanolosta raskaaseen fyysiseen aktiivisuuteen. Kiihtyvyysdata kerättiin 22 suomalaisen aikuisen lantiolta elastiseen vyöhön kiinnitetyllä kolmiakselisella kiihtyvyysanturilla, jonka raakadata luokiteltiin viiteen eri luokkaan (makaaminen, istuminen sekä kevyt, keskiraskas ja raskas fyysinen aktiivisuus). Kolmekymmentäkuusi keski-ikäistä suomalaista käyttivät kiihtyvyysanturia 14 päivän ajan ja heidän paikallaanolonsa ja istumisensa piirteet määritettiin. Paikallaanolon, istumisen ja fyysisen aktiivisuuden yhteyksiä rasva- ja sokeriaineenvaihduntaan tutkittiin Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotistutkimuksessa (n = 5832). Tutkittavat täyttivät terveys- ja elämäntapakyselyitä ja osallistuivat kliinisiin tutkimuksiin sekä kahden viikon mittaisiin paikallaanolon ja fyysisen aktiivisuuden mittauksiin. Isotemporaalisella korvausmallilla tutkittiin paikallaanoloajan korvaamista fyysisellä aktiivisuudella. Kehitetyllä koneoppimismenetelmällä voidaan riittävällä tarkkuudella luokitella paikallaanoloa ja fyysistä aktiivisuutta. Menetelmää voidaan käyttää paikallaanolon ja istumisen piirteiden kuvailussa erikseen. Paikallaanolon piirteet olivat selvemmin yhteydessä rasva-aineenvaihduntaan kuin istumisen piirteet. Paikallaanolon yhteydet rasva-aineenvaihduntaan oliva

Objectives: Discriminating sleep period from accelerometer data remains a challenge despite many studies have adapted 24-h measurement protocols. We aimed to compare and examine the agreement among device-estimated and self-reported bedtime, wake-up time, and sleep periods in a sample of adults. Materials and methods: Participants (108 adults, 61 females) with an average age of 33.1 (SD 0.4) were asked to wear two wearable devices (Polar Active and Ōura ring) simultaneously and record their bedtime and wake up time using a sleep diary. Sleep periods from Polar Active were detected using an in-lab algorithm, which is openly available. Sleep periods from Ōura ring were generated by commercial Ōura system. Scatter plots, Bland–Altman plots, and intraclass correlation coefficients (ICCs) were used to evaluate the agreement between the methods. Results: Intraclass correlation coefficient values were above 0.81 for bedtimes and wake-up times between the three methods. In the estimation of sleep period, ICCs ranged from 0.67 (Polar Active vs. sleep diary) to 0.76 (Polar Active vs. Ōura ring). Average difference between Polar Active and Ōura ring was −1.8 min for bedtimes and −2.6 min for wake-up times. Corresponding values between Polar Active and sleep diary were −5.4 and −18.9 min, and between Ōura ring and sleep diary −3.6 min and −16.2 min, respectively. Conclusion: Results showed a high agreement between Polar Active activity monitor and Ōura ring for sleep period estimation. There was a moderate agreement between self-report and the two devices in estimating bedtime and wake-up time. These findings suggest that potentially wearable devices can be interchangeably used to detect sleep period, but their accuracy remains limited.

Introduction : Physical inactivity, excessive total time spent in sedentary behavior (SB) and prolonged sedentary bouts have been proposed to be risk factors for chronic disease morbidity and mortality worldwide. However, which patterns and postures of SB have the most negative impacts on health outcomes is still unclear. This population-based study aimed to investigate the independent associations of the patterns of accelerometer-based overall SB and sitting with serum lipid biomarkers at different moderate- to vigorous-intensity physical activity (MVPA) levels. Methods: Physical activity and SB were measured in a birth cohort sample (N = 3272) at 46 yr using a triaxial hip-worn accelerometer in free-living conditions for 14 d. Raw acceleration data were classified into SB and PA using a machine learning–based model, and the bouts of overall SB and sitting were identified from the classified data. The participants also answered health-related questionnaires and participated in clinical examinations. Associations of overall SB (lying and sitting) and sitting patterns with serum lipid biomarkers were investigated using linear regression. Results: The overall SB patterns were more consistently associated with serum lipid biomarkers than the sitting patterns after adjustments. Among the participants with the least and the most MVPA, high total time spent in SB and SB bouts of 15–29.99 and ≥30 min were associated with impaired lipid metabolism. Among those with moderate amount of MVPA, higher time spent in SB and SB bouts of 15–29.99 min was unfavorably associated with serum lipid biomarkers. Conclusions: The associations between SB patterns and serum lipid biomarkers were dependent on MVPA level, which should be considered when planning evidence-based interventions to decrease SB in midlife.

Neighbourhood socioeconomic status and physical access to amenities and greenness are factors that have been associated with mental, physical and perceived health. However, associations between long-time exposure to these circumstances and changes in perceived health in the middle-age population have remained a relatively underexamined area. This study aimed to examine the association between residential environmental history and changes in perceived health in the Northern Finland Birth Cohort 1966 (N = 5973) population encompassing the two latest data collections at 31 and 46 years of age. Longitudinal time-varying geographical data on the residential environment’s economic dependency ratio, population density, distance to local services and presence of green areas were derived from various spatial registers and linked to cohort members’ exact residential history. According to a multivariable logistic regression analysis, having a residential history in municipalities with higher-than-average (poor) economic dependency ratios was associated with higher odds of poor perceived health changing to good. Among men, living farther than 2 km away from local services was associated with a higher risk of change from good perceived health to poor, and living farther than 300 m away from green areas was associated with a lower risk of change from good perceived health to poor. The residential environments’s urban/rural context may be one factor contributing to the findings. The results point to the importance of considering local residential area characteristics and residence duration in certain areas as potential determinants of health. Finally, having long-term residential history in areas with poor access to services and amenities has the potential to undermine health during one’s lifetime.

The amount of physical activity decreases and sedentary time increases with aging, predisposing older adults to increased risk of type 2 diabetes and cardiovascular diseases. The aims of this study were to evaluate the associations between accelerometer-measured physical activity and sedentary time with glucose metabolism, cardiovascular risk and mortality in older adults. In 2013–2015, population-based sample of older adults (n = 714) participated in the Oulu45 cohort study at age of 67–70 years. Physical activity and sedentary time were measured with wrist-worn Polar Active accelerometer based activity monitor for two weeks. Participants’ underwent an oral glucose tolerance test and risk of cardiovascular diseases was estimated with Framingham risk score. The data for all-cause mortality were identified from the Digital and Population Data Services Agency, Finland after an average of 6.2 years follow-up. Four physical activity profiles were identified. In more active profiles, prevalence of glucose metabolism disorders, 120 min glucose and insulin values in oral glucose tolerance test were lower compared with the less active profiles. In older adults with central obesity, even light physical activity and avoiding sedentary time were associated with improved glucose metabolism and decreased insulin resistance. Both light and moderate to vigorous physical activity were associated with lower risk of cardiovascular diseases. High sedentary time was associated with increased cardiovascular risk. Low sedentary time and high time spent in light physical activity were associated with lower mortality. Based on these results, a higher amount of physical activity, at any intensity level, and avoidance of sedentary time were associated with reduced risk of type 2 diabetes, cardiovascular diseases and mortality in older adults. Especially overweight older adults should be encouraged to increase their physical activity and to avoid being sedentary., Tiivistelmä Vähäinen fyysisen aktiivisuuden määrä ja runsas paikallaanoloaika lisäävät riskiä sairastua tyypin 2 diabetekseen sekä sydän- ja verisuonisairauksiin. Tämän tutkimuksen tavoitteina oli selvittää kiihtyvyysanturilla mitatun kevyen, kohtuukuormitteisen ja raskaan fyysisen aktiivisuuden sekä paikallaanolon yhteyttä iäkkäiden henkilöiden sokeriaineenvaihduntaan sekä sydän- ja verisuonisairauksien ja kuoleman riskiin. Väestöpohjaisen Oulu45-kohorttitutkimuksen aineisto (n = 714) kerättiin vuosina 2013–2015, ja tutkittavat olivat iältään 67–70-vuotiaita. Tutkittavien fyysinen aktiivisuus ja paikallaanolo mitattiin kahden viikon ajan ranteessa pidettävällä kiihtyvyysanturiin perustuvalla Polar Active -aktiivisuusmittarilla. Tutkittaville tehtiin sokerirasituskoe ja heidän sydän- ja verisuonisairauksien riskiään arvioitiin Framinghamin riskipisteillä. Kuolleisuustiedot kerättiin Digi- ja väestötietovirastosta keskimäärin 6,2 vuoden seuranta-ajan jälkeen. Tutkimuksessa tunnistettiin iäkkäistä neljä liikkujaprofiilia. Profiililtaan fyysisesti aktiivisilla oli harvemmin sokeriaineenvaihdunnan häiriöitä ja matalammat 120 minuutin glukoosi- ja insuliiniarvot sokerirasituskokeessa. Kevyt fyysinen aktiivisuus ja vähäisempi paikallaanoloaika tehostivat etenkin keskivartalolihavien iäkkäiden sokeriaineenvaihduntaa ja vähensivät insuliiniresistenssiä. Sekä kevyt, että kohtuukuormitteinen ja raskas fyysinen aktiivisuus vähensivät sydän- ja verisuonisairauksien riskiä, kun taas runsas paikallaanoloaika lisäsi riskiä. Runsaampi kevyt fyysinen aktiivisuus ja vähäinen paikallaanoloaika pienensivät kuoleman riskiä. Tämän väitöskirjan tulosten perusteella vähäinenkin fyysinen aktiivisuus on paikallaanoloa parempi vaihtoehto iäkkäiden henkilöiden sokeriaineenvaihdunnan ja sydänterveyden edistämiseksi. Erityisesti keskivartalolihavia iäkkäitä tulisi rohkaista lisäämään liikkumista ja vähentämään paikallaanoloa.

Objective: To examine the role of physical activity (PA) and sedentary behavior (SED) for work engagement. Methods: We used data from Northern Finland Birth Cohort 1966 Study (n = 3046 to 4356) to analyze self-reported weekly leisure-time physical activity (LTPA), daily leisure-time sitting time (LTST) and work engagement. PA and SED 24-hour were also measured with accelerometer for 14 days. The data were analyzed using linear regression analyses. Results: High self-reported LTPA and sports participation were associated with higher work engagement and its subdimensions. High self-reported ST was associated with lower work engagement, vigor, and absorption. Accelerometer-measured light PA was associated with higher work engagement and vigor, and accelerometer-measured steps were linked to higher vigor. Accelerometer-measured SED was associated with lower work engagement, vigor, and dedication. Conclusions: Self-reported and accelerometer-measured PA and SED may play a role in people’s work engagement.

Aims: To examine the association of physical activity (PA) and sedentary time (ST) with glucose metabolism according to waist circumference (WC) in older people. Methods: A population-based sample of 702 individuals (aged 67–70 years) wore wrist-worn accelerometers for two weeks and underwent an oral glucose tolerance test. The associations between moderate-to-vigorous (MVPA) and light (LPA) PA, ST, and glucose metabolism across the tertiles of WC were analysed using general linear regression. Results: Among highest WC tertile, LPA negatively associated with fasting insulin (β = − 0.047, 95% CI − 0.082 to − 0.012), HOMA-IR (β = − 0.098, 95% CI − 0.184 to − 0.012), and HOMA-β (β = − 3.367, CI − 6.570 to − 0.783). ST associated with 120 min glucose (β = 0.140, CI 0.021 to 0.260). Among lowest WC tertile, MVPA negatively associated with 30 min insulin (β = − 0.086, 95% CI − 0.168 to − 0.004) and 120 min insulin (β = − 0.160, 95% CI − 0.257 to − 0.063) and positively associated with Matsuda index (β = 0.076, 95% CI 0.014 to 0.139). Light PA negatively associated with 120 min insulin (β = − 0.054, 95% CI − 0.104 to − 0.005). Conclusion: With the limitation of the cross-sectional study, reducing ST and increasing LPA may be beneficial for glucose metabolism among abdominally obese older adults. Lean older adults could benefit more from increasing MVPA.

Introduction: Physical inactivity is a global concern, especially among adolescent men. Little research has been done on the association between parental factors and young adults’ physical activity in the context of residential environment. We aimed to reveal what parental factors are associated with physical activity among adolescent men living in built and natural environments. Methods: A population-based sample of 1,904 men (mean age = 17.9, SD = 0.7 years) completed a questionnaire regarding physical activity, parental factors, and lifestyle in Northern Finland in 2012 and 2013. Geographical information system methods and dominant land-use type were used to define the residential environment in a 1-kilometer radius buffer zone surrounding each participant’s home address. If the residential area included more artificial surfaces, it was defined as a built environment, and areas including more nature were defined as natural environments. Results: According to multivariable analyses, a mother’s physical activity (OR = 1.9; 95% CI: 1.3–2.8) was positively associated with the physical activity of adolescent men living in built environments, and the father’s physical activity was positively associated with the physical activity of adolescent men living in natural environments (2.8; 1.7–4.8). Self-rated health (built 5.9 [4.0–8.7]; natural 5.2 [3.0–9.0]) was positively associated with physical activity level. Those with symptoms of depression were more likely to be physically inactive (built 0.5 [0.3–0.8]; natural 0.3 [0.1–0.6]). Adolescent men were equally physically active regardless of the living environment. Conclusions: The level of physical activity of parents, self-rated health, and depressive symptoms should be considered when designing physical activity promotions for adolescent men according to their residential environments.

The popularity of accelerometer-based activity monitors has been associated with several analytical challenges, including how to quantify accelerometer outputs in terms of sedentary behavior, light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Recently, machine learning (ML) approaches have been coupled with raw accelerometry to classify activities by intensity, but the generalizability of ML models outside of the development datasets remains poorly understood. Currently, the health benefits of meeting the recommended amounts of sleep and MVPA in adults are well documented, but the cardiometabolic health implications of sedentary time and LPA are still unclear. The present study reviewed studies calibrating and validating wearable accelerometers using ML approaches and preformed cross-dataset tests to evaluate the generalization performance of ML models for classifying activity intensities from raw acceleration data. Additionally, the latest follow-up in the Northern Finland Birth Cohort 1966 study (n = 5,840) at age 46 years included measurement of daily activities for two weeks with two accelerometers. This data was used to examine how the levels and patterns of accelerometer-estimated activity intensities (sedentary behavior, LPA, and MVPA) are associated with cardiometabolic health in this large sample of middle-aged adults, and to create a data-driven hierarchy predicting their activity behaviors. Based on the study, ML techniques can classify activities in terms of type, category, or intensity with acceptable accuracy irrespective of accelerometer placement. However, ML models developed with raw acceleration data for classifying activity intensities (sedentary behavior, LPA, and MVPA) are not generalizable to other populations monitored with different accelerometers, suggesting that further strategies are needed to enhance their generalizability. The study suggests that adults, in addition to MVPA, may also gain, Tiivistelmä Nykyään hyvin suosittujen kiihtyvyysanturiin perustuvien aktiivisuusmittareiden keräämän datan analysointiin liittyy monia haasteita, kuten paikallaanolon, kevyen liikunnan sekä keskiraskaan ja raskaan liikunnan tarkan määrän määrittäminen. Viime aikoina on otettu käyttöön koneoppimismenetelmiä kiihtyvyysanturin tuottaman raakasignaalin analysoinnissa luokittelemaan liikettä sen intensiteetin perusteella, mutta toistaiseksi näiden menetelmien yleistettävyys on huonosti tiedossa. Nykyisin tiedetään aika hyvin ne terveyshyödyt, joita saadaan, jos noudatetaan unen sekä keskiraskaan ja raskaan liikunnan suosituksia. Paikallaanolon ja kevyen liikunnan vaikutukset sydän- ja verisuoniterveyteen ovat kuitenkin heikommin tiedossa. Tässä tutkimuksessa tehtiin systemaattinen kirjallisuuskatsaus koneoppimismenetelmien käytöstä kannettavien kiihtyvyysanturien kalibroinnissa ja validoinnissa. Työssä testattiin koneoppimismenetelmien yleistettävyyttä fyysisen aktiivisuuden intensiteetin luokitteluun kiihtyvyysanturin antaman raakadatan perusteella yhdistäen useita toisistaan riippumattomia mittausaineistoja. Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotisaineistonkeruussa (n = 5,840) oli mitattu liikunta-aktiivisuutta kahdella kiihtyvyysanturilla. Tämän mittaustiedon avulla tutkittiin sitä, kuinka kiihtyvyysanturilla mitattu fyysisen aktiivisuuden intensiteetti (paikallaanolo, kevyt liikunta sekä keskiraskas ja raskas liikunta) ja eri intensiteetillä toteutetun aktiivisuuden jakautuminen vuorokauden sisällä ovat yhteydessä keski-ikäisten sydänterveyteen. Lisäksi luotiin aineiston perusteella hierarkinen malli ennustamaan liikuntakäyttäytymistä. Tutkimuksen perusteella koneoppimistekniikoiden avulla voidaan riittävällä tarkkuudella luokitella fyysistä aktiivisuutta liikuntamuodon, intensiteetin ja eri intensiteettien jakautumisen perusteella riippumatta kiihtyvyysanturin sijainnista. Kiihtyvyysanturin tuottamaan raakadataan perustuvat fyysisen aktiivisuuden intensi

Breaking up sedentary time with physical activity (PA) could modify the detrimental cardiometabolic health effects of sedentary time. Our aim was to identify profiles according to distinct accumulation patterns of sedentary time and breaks in adults, and to investigate how these profiles are associated with cardiometabolic outcomes. Participants (n = 4439) of the Northern Finland Birth Cohort 1966 at age 46 years wore a hip-worn accelerometer for 7 consecutive days during waking hours. Uninterrupted ≥1-min sedentary bouts were identified, and non-sedentary bouts in between two consecutive sedentary bouts were considered as sedentary breaks. K-means clustering was performed with 65 variables characterizing how sedentary time was accumulated and interrupted. Linear regression was used to determine the association of accumulation patterns with cardiometabolic health markers. Four distinct groups were formed as follows: “Couch potatoes” (n = 1222), “Prolonged sitters” (n = 1179), “Shortened sitters” (n = 1529), and “Breakers” (n = 509). Couch potatoes had the highest level of sedentariness and the shortest sedentary breaks. Prolonged sitters, accumulating sedentary time in bouts of ≥15–30 min, had no differences in cardiometabolic outcomes compared with Couch potatoes. Shortened sitters accumulated sedentary time in bouts lasting <15 min and performed more light-intensity PA in their sedentary breaks, and Breakers performed more light-intensity and moderate-to-vigorous PA. These latter two profiles had lower levels of adiposity, blood lipids, and insulin sensitivity, compared with Couch potatoes (1.1–25.0% lower values depending on the cardiometabolic health outcome, group, and adjustments for potential confounders). Avoiding uninterrupted sedentary time with any active behavior from light-intensity upwards could be beneficial for cardiometabolic health in adults.

Purpose: This study aimed to examine how compositions of 24-h time use and time reallocations between movement behaviors are associated with cardiometabolic health in a population-based sample of middle-age Finnish adults. Methods: Participants were 3443 adults 46 yr of age from the Northern Finland Birth Cohort 1966 study. Participants wore a hip-worn accelerometer for 14 d from which time spent in sedentary behavior (SB), light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) were determined. These data were combined with self-reported sleep to obtain the 24-h time-use composition. Cardiometabolic outcomes included adiposity markers, blood lipid levels, and markers of glucose control and insulin sensitivity. Multivariable-adjusted regression analysis, using a compositional data analysis approach based on isometric log-ratio transformation, was used to examine associations between movement behaviors with cardiometabolic outcomes. Results: More daily time in MVPA and LPA, relative to other movement behaviors, was consistently favorably associated with all cardiometabolic outcomes. For example, relative to time spent in other behaviors, 30 min·d−1 more MVPA and LPA were both associated with lower 2-h post–glucose load insulin level (−11.8% and −2.7%, respectively). Relative to other movement behaviors, more daily time in SB was adversely associated with adiposity measures, lipid levels, and markers of insulin sensitivity, and more daily time asleep was adversely associated with adiposity measures, blood lipid, fasting plasma glucose, and 2-h insulin. For example, 60 min·d−1 more SB and sleep relative to the remaining behaviors were both associated with higher 2-h insulin (3.5% and 5.7%, respectively). Conclusions: Altering daily movement behavior compositions to incorporate more MVPA at the expense of any other movement behavior, or more LPA at the expense of SB or sleep, could help to improve cardiometabolic he

Background: Recently, the importance of light physical activity (LPA) for health has been emphasized, and residential greenness has been positively linked to the level of LPA and a variety of positive health outcomes. However, people spend less time in green environments because of urbanization and modern sedentary leisure activities. Aims: In this population-based study, we investigated the association between objectively measured residential greenness and accelerometry measured physical activity (PA), with a special interest in LPA and gender differences. Methods: The study was based on the Northern Finland Birth Cohort 1966 (5433 members). Participants filled in a postal questionnaire and underwent clinical examinations and wore a continuous measurement of PA with wrist-worn Polar Active Activity Monitor accelerometers for two weeks. The volume of PA (metabolic equivalent of task or MET) was used to describe the participant’s total daily activity (light: 2–3.49 MET; moderate: 3.5–4.99 MET; vigorous: 5–7.99 MET; very vigorous: ≥8 MET). A geographic information system (GIS) was used to assess the features of each individual’s residential environment. The normalized difference vegetation index (NDVI) was used for the objective quantification of residential greenness. Multiple linear regression and a generalized additive model (GAM) were used to analyze the association between residential greenness and the amount of PA at different intensity levels. Results: Residential greenness (NDVI) was independently associated with LPA (unadjusted β = 174; CI = 140, 209) and moderate physical activity (MPA) (unadjusted β = 75; CI = 48, 101). In the adjusted model, residential greenness was positively and significantly associated with LPA (adjusted β = 70; CI = 26, 114). In men, residential greenness was positively and significantly associated with LPA (unadjusted β = 224; CI = 173, 275), MPA (unadjusted β = 75; CI = 48, 101), and moderate to vigorous physical activity (

Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results: Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B

Background/Objectives: Falls are a major problem for older people and recurrent fallers are especially prone to severe consequences due to falls. This study investigated the association between chronic conditions and falls. Methods: Responses from 872 older persons (age 65–98) to a health questionnaire were used in the analyses. Characteristics and disease prevalence between recurrent fallers, one-time fallers and non-fallers were compared. A hierarchical clustering method was applied to find combinations of chronic conditions that were associated with recent recurrent falling. Results: The results showed that recurrent fallers had a higher number of diseases (median 4, interquartile range, IQR = 2.0–5.0) compared to non-fallers (median 2, IQR = 1.0–3.0). Eight clusters were formed based on the data. The participants in the low chronic disease cluster were younger, more physically active, not frail, and had fewer geriatric conditions. Multiple chronic disease cluster participants were older, less physically active, overweight (body mass index, BMI > 30), at risk of malnutrition, and had more geriatric conditions. Significantly increased risk of recurrent falls relative to the low chronic cluster was found for respondents in the osteoporosis cluster and multiple chronic disease cluster (OR = 5.65, 95% confidence interval CI: 1.23–25.85, p = 0.026, and OR = 13.42, 95% CI: 2.47–72.96, = 0.002, respectively). None of the clusters were associated with increased risk of one-time falling. Conclusions: The results implicate that the number of chronic diseases is related with risk of recurrent falling. Furthermore, the results implicate the potential of identifying certain combinations of chronic diseases that increase fall risk by analyzing health record data, although further studies are needed with a larger population sample.

Purpose: Polygenic risk scores (PRS) summarize genome-wide genotype data into a single variable that produces an individual-level risk score for genetic liability. PRS has been used for prediction of chronic diseases and some risk factors. As PRS has been studied less for physical activity (PA), we constructed PRS for PA and studied how much variation in PA can be explained by this PRS in independent population samples. Methods: We calculated PRS for self-reported and objectively measured PA using UK Biobank genome-wide association study summary statistics, and analyzed how much of the variation in self-reported (MET-hours per day) and measured (steps and moderate-to-vigorous PA minutes per day) PA could be accounted for by the PRS in the Finnish Twin Cohorts (FTC; N = 759–11,528) and the Northern Finland Birth Cohort 1966 (NFBC1966; N = 3263–4061). Objective measurement of PA was done with wrist-worn accelerometer in UK Biobank and NFBC1966 studies, and with hip-worn accelerometer in the FTC. Results: The PRS accounted from 0.07% to 1.44% of the variation (R2) in the self-reported and objectively measured PA volumes (P value range = 0.023 to <0.0001) in the FTC and NFBC1966. For both self-reported and objectively measured PA, individuals in the highest PRS deciles had significantly (11%–28%) higher PA volumes compared with the lowest PRS deciles (P value range = 0.017 to <0.0001). Conclusions: PA is a multifactorial phenotype, and the PRS constructed based on UK Biobank results accounted for statistically significant but overall small proportion of the variation in PA in the Finnish cohorts. Using identical methods to assess PA and including less common and rare variants in the construction of PRS may increase the proportion of PA explained by the PRS.

Promoting a healthy lifestyle has attracted a significant amount of attention in recent years. This is also the case in workplaces, where the focus on wellbeing at work and changes in personal health behaviours has been raised. Technology-enhanced interventions are a possible solution for motivating people towards healthy lifestyle promotion, such as healthy eating and physical activity (PA). Persuasive applications have been proposed as a promising technique for fostering behavioural change and promoting healthy lifestyles. The major limitation of most existing mobile health (mHealth) applications is that they are not grounded in theoretical concepts. On the other hand, value propositions (VPs) might increase the effectiveness of persuasive applications. This study aimed to develop mHealth behavioural change interventions, that is, persuasive mHealth applications based on psychological theories to promote healthy eating and PA in the workplace. Four VPs were proposed and implemented to enhance the efficacy of the persuasive mHealth application. Elaborated Intrusion (EI) theory was applied to develop a healthy eating application (iCrave) and self-determination theory (SDT) was used to develop a PA application (iGO). iCrave allowed users to track their snack cravings and record whether they chose to eat healthily or unhealthily. iGO allowed users to promote their PA, and to decide whether to select PA after breakfast or lunch. The design of the applications was performed by an iterative User-centered design (UCD) process. The feasibility of the applications was assessed by experimental studies with quantitative and qualitative surveys. The results of the study demonstrate that the design process of the applications was a successful approach and can be used in future persuasive mHealth applications., Tiivistelmä Terveellisten elämäntapojen edistäminen on viime vuosina saanut kasvavaa huomiota. Myös työpaikoilla keskitytään yhä enemmän työhyvinvointiin ja henkilökohtaiseen terveyskäyttäytymiseen. Teknologian avulla tuetut interventiot ovat mahdollisia ratkaisuja motivoida ihmisiä edistämään terveellisiä elämäntapoja, kuten terveellistä ruokailua ja fyysistä aktiivisuutta. Suostuttelevia sovelluksia on ehdotettu lupaavaksi ratkaisuksi käyttäytymisen muutoksen edistämiseksi ja terveellisten elämäntapojen parantamiseksi. Suurin rajoite useimmissa olemassa olevissa terveyteen liittyvissä mobiilisovelluksissa (mHealth) on se, että niillä ei ole teoreettista perustaa. Toisaalta arvoehdotukset voivat lisätä suostuttelevien sovellusten tehokkuutta. Tämän tutkimuksen tavoitteena oli kehittää käyttäytymisteorioihin perustuvia suostuttelevia mHealth-sovelluksia terveellisen ruokavalion ja fyysisen aktiivisuuden edistämiseksi työpaikoilla. Työssä toteutettiin neljä arvoehdotusta suostuttelevan mHealth-sovelluksen tehokkuuden parantamiseksi. Elaborated Intrusion (EI) -teoriaa sovellettiin terveellistä syömistä edistävän sovelluksen (iCrave) kehittämiseen ja itsemääritysteoriaa fyysistä aktiivisuutta edistävän sovelluksen (iGO) kehittämiseen. iCrave antoi käyttäjille mahdollisuuden seurata heidän välipalatoiveitaan ja valita terveellisiä tai epäterveellisiä vaihtoehtoja. iGO edisti käyttäjän fyysistä aktiivisuutta kannustamalla liikkumaan joko aamiaisen tai lounaan jälkeen. Sovellukset toteutettiin käyttäen iteratiivista käyttäjäkeskeistä suunnittelua. Sovellusten toimivuus arvioitiin kokeellisissa tutkimusasetelmissa määrällisillä ja laadullisilla kyselytutkimuksilla. Tutkimuksen tulokset osoittavat, että sovellusten suunnitteluprosessi oli onnistunut lähestymistapa, ja sitä voidaan käyttää tulevaisuuden suostuttelevissa mHealth-sovelluksissa.

Purpose: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors. Method: Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within-dataset (leave-one-subject-out) cross-validation, and then cross-tested to other datasets with different accelerometers. To enhance the models’ generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation). Results: The datasets showed high performance in within-dataset cross-validation (accuracy 71.9–95.4%, Kappa K=0.63–0.94). The performance of the within-dataset validated models decreased when applied to datasets with different accelerometers (41.2–59.9%, K=0.21–0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9–83.7%, K=0.61–0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4–90.7%, K=0.68–0.89). Conclusions: Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within-dataset validation is not sufficient to understand the models’ performance on other populations with different accelerometers.

Excessive sedentary time (SED) and long SED bouts are associated with cardiovascular diseases (CVD) and increased mortality. Low heart rate variability (HRV), indicating autonomic dysfunction, increases mortality and CVD morbidity. Information about the association between prolonged SED and HRV is lacking. The aim was to assess the relationship between SED bouts and HRV. Physical activity (PA), SED, HRV, and cardiorespiratory fitness (CRF) were collected from a birth‐cohort sample (n = 4150) at 46 years. PA and SED were measured for 14 days with an activity monitor (Polar Active, Polar Electro, Finland). SED accumulating in bouts of at least 30 and 60 minutes (SED30, SED60) was calculated. Linear regression was used to study the relationship between prolonged SED and HRV accounting for CRF, PA, and health covariates. Higher SED60 and in women SED30 were associated with higher root mean square of differences in R‐R intervals (rMSSD) after adjustments (β = .082‐.104). In women, higher SED60 was associated with lower ratio between low‐ and high‐frequency powers (β = −.060). Sedentary bouts were not associated with resting HR or post‐exercise HR recovery. A positive relationship between SED bouts and rMSSD independent of PA and CRF was found, prolonged SED being positively associated with cardiac parasympathetic activity in midlife.

The aim of this population-based cross-sectional study was to describe and compare the prevalence and features of perceived loneliness among home-dwelling older adults with (n = 129) and without (n = 244) memory disorder. The latter group was randomly resampled from 789 respondents stratified by age to obtain a standardized control group. Loneliness was assessed using the six-item De Jong Gierveld Loneliness Scale, and perceived stress using Cohen, Kamarck and Mermelstein’s 10-item Perceived Stress Scale. Results show that severe loneliness was common among the home-dwelling older adults, especially those with memory disorder, who also perceived stress more frequently than those without memory disorder. Both groups, but again more frequently those with memory disorder, were more likely to be emotionally than socially lonely. Thus, when planning social and healthcare services and interventions to mitigate loneliness among older adults living at home, memory problems and emotional loneliness require particular consideration.

The purpose of this study was to describe the accelerometry-based characteristics of overall sedentary behavior (SB) and sitting among adults under free-living conditions. Thirty-six (mean age 47.6 years) volunteers carried a waist-worn accelerometer for ≥4 days with data ≥600 min/d during 14 consecutive days. A machine learning (ML) based method was used to classify the patterns of SB and sitting from raw 3D acceleration. The participants spent most (69.3%) of their waking time in SB, and a half (52.2%) of the SB was performed in a sitting posture. Men broke their overall sedentary time less often (4.1 vs. 6.1 bouts/h), but women sat more; however, women broke their sitting time as often as men (7.6 bouts/h). This study confirms that SB and sitting can be distinguished using ML methods, and more information about SB can be achieved when overall SB and sitting are analyzed separately in free-living conditions.

Physical activity reduces mortality and morbidity and improves physical and psychological health. Lately, the detrimental health associations of excess sedentary time have also been acknowledged. It is still unknown how temporal patterns of physical activity and sedentary time are associated with health, as previous studies have mainly focused on summary metrics of these behaviors; for example, the weekly duration of moderate to vigorous physical activity. This study aimed to investigate the associations between the amount and temporal patterns of physical activity and sedentary time and health at mid-life. Physical activity and sedentary time were objectively measured for two weeks using an accelerometer-based activity monitor in the Northern Finland Birth Cohort 1966 46-year follow-up (n=5,621). Participants attended clinical examinations and completed health and behaviour questionnaires. A machine learning method (X-means cluster analysis) was used to identify distinct groups of participants with different patterns of physical activity and sedentary behaviour based on the activity data. A positive, dose-response association was found with perceived health and self-reported leisure time and objectively measured moderate to vigorous physical activity. Higher prolonged sedentary time was associated with better heart rate variability but not with resting heart rate or post-exercise heart rate recovery. Four distinct physical activity clusters (inactive, evening active, moderately active and very active) were recognised. The risk of developing cardiovascular disease was significantly lower in the very active cluster compared to the inactive, and in women also in the moderately active cluster compared to the inactive and evening active clusters. On average, the cardiovascular disease risk was low, indicating good cardiovascular health in the study population. Prolonged sedentary time was associated with cardiac autonomic function, which in this study was not e, Tiivistelmä Fyysinen aktiivisuus vähentää sairastavuutta, kuolleisuutta sekä parantaa fyysistä ja psyykkistä terveyttä. Viime aikoina on lisäksi tunnistettu liiallisen paikallaanolon terveyshaitat. Vielä ei tiedetä, miten fyysisen aktiivisuuden ja paikallaanolon ajallinen jakautuminen päivän aikana vaikuttaa terveyteen, koska aiemmat tutkimukset ovat keskittyneet enimmäkseen tiettyihin summamuuttujiin kuten kohtuullisesti kuormittavan liikkumisen määrään viikossa. Työn tarkoituksena oli tutkia fyysisen aktiivisuuden ja paikallaanolon määrän ja ajallisen jakautumisen terveysyhteyksiä keski-iässä. Fyysinen aktiivisuus ja paikallaanolo mitattiin kiihtyvyysanturipohjaisella aktiivisuusmittarilla kahden viikon ajan Pohjois-Suomen vuoden 1966 syntymäkohortin 46-vuotistutkimuksessa (n=5621). Tutkittavat osallistuivat kliinisiin tutkimuksiin ja täyttivät kyselyitä terveydentilastaan ja käyttäytymisestään. Koneoppimismenetelmällä (X-means cluster analysis) tutkittavat luokiteltiin aktiivisuusdatan perusteella ryhmiin, joissa fyysisen aktiivisuuden määrä ja ajallinen jakautuminen päivän aikana poikkesi mahdollisimman paljon ryhmien välillä. Positiivinen annos-vasteyhteys löydettiin koetun terveyden ja itseraportoidun vapaa-ajan liikunnan sekä mitatun kohtuullisesti kuormittavan liikkumisen väliltä. Suurempi pitkittynyt paikallaanoloaika oli yhteydessä parempaan sykevälivaihteluun mutta ei leposykkeeseen tai harjoituksen jälkeiseen sykkeen palautumiseen. Neljä aktiivisuusryhmää tunnistettiin (inaktiiviset, ilta-aktiiviset, kohtuullisen aktiiviset ja erittäin aktiiviset). Sydän- ja verisuonitautien sairastumisriski oli merkitsevästi pienempi erittäin aktiivisessa ryhmässä verrattuna inaktiiviseen ryhmään ja lisäksi naisilla kohtuullisen aktiivisessa ryhmässä verrattuna inaktiiviseen ja ilta-aktiiviseen ryhmään. Sairastumisriski oli keskimäärin matala viitaten hyvään sydänterveyteen tutkimusjoukossa. Pitkillä paikallaanolojaksoilla oli yhteys sydämen autonomiseen säätelyyn, jota

Physical activity (PA) and sedentary time (SED) are associated with the risk of cardiovascular disease (CVD), but the temporal patterns of these behaviors most beneficial for cardiovascular health remain unknown. We aimed to identify the intensity and temporal patterns of PA and SED measured continuously by an accelerometer and their relationship with CVD risk. At the age of 46 years, 4582 members (1916 men; 2666 women) of the Northern Finland Birth Cohort 1966 study underwent continuous measurement of PA with Polar Active (Polar Electro, Finland) accelerometers for one week. X-means clustering was applied based on 10 min average MET (metabolic equivalent) values during the measurement period. Ten-year risk of CVD was estimated using the Framingham risk model. Most of the participants had low risk for CVD. Four distinct PA clusters were identified that were well differentiable by the intensity and temporal patterns of activity (inactive, evening active, moderately active, very active). A significant difference in 10-year CVD risk across the clusters was found in men (p = 0.028) and women (p < 0.001). Higher levels of HDL cholesterol were found in more active clusters compared to less active clusters (p < 0.001) in both genders. In women total cholesterol was lower in the moderately active cluster compared to the inactive and evening active clusters (p = 0.001). Four distinct PA clusters were recognized based on accelerometer data and X-means clustering. A significant difference in CVD risk across the clusters was found in both genders. These results can be used in developing and promoting CVD prevention strategies.

Background: Regular physical activity (PA) promotes health and decreases mortality. The positive relationship between PA and perceived health (PH) is well known. However, previous research in the field has often used self-reported PA measures. The aim of this population-based NFBC1966 birth cohort study was to assess the relationship between both self-reported and objectively measured PA and PH in midlife. Methods: A sample group of 6384 participants (2878 men, 3506 women, response rate 62%) aged 46 completed a questionnaire on PH and health behaviors, including items on weekly leisure time physical activity (LTPA) and daily sitting time (ST). PH was dichotomized as good (very good or good) and other (fair, poor, or very poor). PA was measured with a wrist-worn Polar Active (Polar Electro, Finland) accelerometer for 14 days (n = 5481, 98%) and expressed as daily average time spent in moderate to vigorous intensity PA (MVPA). Odds ratios (OR) and 95% confidence intervals for good PH were calculated using binary logistic regression and adjusted for relevant demographic, socioeconomic, and health characteristics, and ST. Results: The level of PA was positively associated with PH after adjustments with covariates and ST. There was a dose-response relationship across the PA quartiles according to the adjusted multivariable models. Self-reported LTPA was more strongly associated with good PH (OR from 1.72 to 4.33 compared to lowest PA quartile) than objectively measured PA (OR from 1.37 to 1.66 compared to lowest PA quartile). Conclusions: In this large population-based birth cohort study, we for the first time show a positive dose-response relationship of both self-reported and objectively measured PA to PH, the relationship being stronger for self-reported LTPA. Despite the cross-sectional design of this study, the results from this large sample suggest that both self-reported and objectively measured physical activity are strongly associated with PH, which is

Background: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions. Method: We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles. Results: A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure. Conclusions: It appears that various ML-based techniques together with raw acceleration data sampled at 20–30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals.

Background: In general, nature relatedness is positively associated with physical activity, health, and subjective well-being. However, increased residence in urban areas, and the decrease in natural spaces, may affect the younger generation most adversely. The associated environmental changes can increase youths’ risk of spending most of their time indoors, and weaken their nature relatedness, making them less likely to enjoy nature’s health benefits. This is a serious public health issue, since inadequate physical activity, combined with minimum time spent in green space, can affect health across the whole lifespan. Thus, to develop effective interventions for physical activation and promote health and well-being among young men, further knowledge of the determinants of their nature relatedness is necessary. Aims: To explore factors related to nature relatedness, including physical activity, physical activity with parents, and residential environment. Methods: The study population consisted of all 914 young men (mean—17.8 years; SD—0.5) who participated in mandatory call-ups for military service and completed the study questionnaire in 2013. The questionnaire inquired about their nature relatedness, demographic characteristics, socioeconomic status, physical activity, health, and subjective well-being. A geographic information system (GIS) was used to assess the features of their residential environments. Multivariable linear regression was used to analyze the data. Results: Physical activity (p = 0.021) and physical activity with parents at primary school age (p = 0.007), and currently (p = 0.001) as well as good self-rated health (p = 0.001), and father’s higher socioeconomic status (p = 0.041), were positively connected to nature relatedness. Conclusions: Physical activity in general, physical activity with parents, and nature relatedness were positively related. This knowledge can be utilized in promoting physical activity and health among young men.

The field of emergency medical services (EMS) is a challenging environment for ensuring fluent information exchange between stakeholders because several different kinds of organizations are involved in EMS missions. Solutions for information and communication technology can vary significantly depending on the organization. This study aims to identify current communication bottlenecks between EMS professionals, understand the technological challenges behind them, and describe technologies that can improve EMS communication in the future. Information for the study about current EMS processes, technologies, and technology needs was collected from EMS professionals during three workshops, five personal interviews, and one email questionnaire. All surveyed health care professionals were working in the county of Northern Ostrobothnia. Information about proposed technologies for EMS was obtained from literature and interviews with five technology companies. The principal problem in EMS communication is scattered health data. This leads to a lack of common situational awareness for professionals and incomplete medical histories for patients. The reasons behind those problems are different information systems which do not communicate with each other and the lack of a common electronic patient care record (ePCR) for use by stakeholders. Personal health measurements, sensors, telemedicine, and artificial intelligence will create opportunities for further improving the flow of communication in EMS, provided those tools can be integrated into decision-making systems.

Mobile technology has been increasingly adopted in promotion of mental health among older people. This study assessed the feasibility of a mobile mental wellness training application for individual use and for group work from the perspectives of older adults and social care professionals. The older individuals recruited for the study were participants in a Circle of Friends group and family caregivers’ peer support group offered by the communal senior services. The qualitative and quantitative results of interviews, questionnaires, observation, and application usage were reported. Seven older adults started using the application independently at home in parallel with the group activity. This study revealed new information regarding the barriers to the older adults’ full adoption of such mobile technologies. The results indicated that there may be potential in the incorporation of mobile technologies in promotion of mental health of older people at group settings.

In order to benefit from advanced mobile information technology (AMIT) in e-health services, people need competencies in finding, evaluating, and understanding health-related information in varying everyday life situations, that is, everyday health information literacy (EHIL). This study focused on the relationship between EHIL and AMIT use and attitudes towards it among older adults. A paper questionnaire was posted to a random sample of 1,500 home dwelling subjects aged 65 or more living in Northern Finland. A variety of themes were addressed in the questionnaire including a 4-item version of an EHIL screening tool. The response rate was 61% (n = 918). Older adults with negative attitudes towards or having less experience with mobile information technology were likely to have poor self-estimated EHIL skills. Older people are at risk of marginalization in regard to m-services, and these results should be utilized by decision-makers and software designers in digitalization of services.

The aim of this study was to assess in practice whether assistive technologies support and facilitate the work of a family caregiver or care staff, and whether these technologies support the independence of a person with a memory disorder. A comprehensive set of supportive devices and alarm systems were experimentally tested in the care of five test subjects in an assisted living facility by eight nurses, and in the care of four test subjects in a home environment by three family caregivers and one care team. Questionnaires, diaries and logged data were used to evaluate the benefits of the devices. Simple aids and alarm systems that did not need much adjusting were considered most useful by caregivers and nurses, though multiple false alarms occurred during the test period. Technical connection problems, complex user interface, and inadequate sound quality were the primary factors reducing the utility of the tested devices. Further experimental research is needed to evaluate the utility of assistive technologies in different stages of a memory disorder.

A fundamental challenge for employees in the office environment is the difficulty of being physically active. The long-term effect of physical inactivity can lower work effectiveness and cause health problems. The non-substantial indication from recent research suggests that persuasive techniques can create a significant impact on motivating people. This study investigates the overall influence of using a persuasive application in promoting physical activity in the workplace, such as office environment. To motivate individuals for healthier behaviour, we implemented and tested an application incorporating Self Determination Theory (SDT). We conducted an eight week long usability evaluation of the application, using the UTAUT model. The questionnaires were based on the factors: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioural Intention, and Use Behaviour. We found that our persuasive application was satisfactory to motivate users for physical activity promotion.

The Nordic eHealth Research Network, a subgroup of the Nordic Council of Ministers eHealth group, is working on developing indicators to monitor progress in availability, use and outcome of eHealth applications in the Nordic countries. This paper reports on the consecutive analysis of National eHealth policies in the Nordic countries from 2012 to 2016. Furthermore, it discusses the consequences for the development of indicators that can measure changes in the eHealth environment arising from the policies. The main change in policies is reflected in a shift towards more stakeholder involvement and intensified focus on clinical infrastructure. This change suggests developing indicators that can monitor understandability and usability of eHealth systems, and the use and utility of shared information infrastructure from the perspective of the end-users — citizens/patients and clinicians in particular.

People spend a lot of time at their workplace, for example in office environment. After consuming breakfast or lunch during the recess periods, one may feel lazy both physically and mentally. Thus, gaining extra calories may lower the working progress. To motivate individuals for healthier behavior, we designed, executed and tested an application for improving physical activity behavior at workplace. Our health and wellbeing application delivers a physical activity promotion intervention that motivates user performing activities, for example by walking after breakfast or lunch. Our physical activity promotion is based on recent research showing that one can be motivated extrinsically through satisfaction of three basic psychological needs (autonomy, competence and relatedness) that are influenced by autonomy support. We conducted a one week long usability evaluation on users for the health and wellbeing application. We found that the application works well for physical activity promotion.

Physical activity (PA) is important to prevent and lessen the risks of various diseases as well as progress in physical and mental health. Employees expend a lot of time at their workplace such as office environment. Working efficiency may be harmfully affected if the worker is physically inactive. The purpose of this study was to design a system model to guide employees at their workplace on their PA promotion. We propose a system model integrating the psychological theory known as Self-determination Theory (SDT) which indicates that people can be motivated extrinsically through the fulfilment of three psychological needs: autonomy, competence, and relatedness. To do this, game elements such as points, badges and leaderboard were applied into the system model. We utilized the system model to design a gamified persuasive application. We developed a prototype of PA promotion app using the User-Centered Design (UCD) process. We conducted UCD iteration and pilot-tested the prototype of an app with end users. They used the prototype of an app to perform some activities e.g. walking after breakfast and lunch hour. We found that it brings a positive impact on the employees in promoting their PA in their workplace. The result of the study will be used to build an actual persuasive application with gamification techniques.

This study examined the use of ICT in caregiving as assessed by family caregivers of disabled or elderly subjects (N = 53). The results showed limited current use of ICT-based technologies either for on-site or remote assistance. However, perceived usefulness was evaluated as high when these technologies were used. The factors for the use of ICT were different for on-site assistance or remote assistance. The data obtained can be used in the development of future ICT and IoT-based assistive technologies.

Background: The majority of young people do not meet the recommendations on physical activity for health. New innovative ways to motivate young people to adopt a physically active lifestyle are needed. Objective: The study aimed to study the feasibility of an automated, gamified, tailored Web-based mobile service aimed at physical and social activation among young men. Methods: A population-based sample of 496 young men (mean age 17.8 years [standard deviation 0.6]) participated in a 6-month randomized controlled trial (MOPO study). Participants were randomized to an intervention (n=250) and a control group (n=246). The intervention group was given a wrist-worn physical activity monitor (Polar Active) with physical activity feedback and access to a gamified Web-based mobile service, providing fitness guidelines, tailored health information, advice of youth services, social networking, and feedback on physical activity. Through the trial, the physical activity of the men in the control group was measured continuously with an otherwise similar monitor but providing only the time of day and no feedback. The primary outcome was the feasibility of the service based on log data and questionnaires. Among completers, we also analyzed the change in anthropometry and fitness between baseline and 6 months and the change over time in weekly time spent in moderate to vigorous physical activity. Results: Mobile service users considered the various functionalities related to physical activity important. However, compliance of the service was limited, with 161 (64.4%, 161/250) participants visiting the service, 118 (47.2%, 118/250) logging in more than once, and 41 (16.4%, 41/250) more than 5 times. Baseline sedentary time was higher in those who uploaded physical activity data until the end of the trial (P=.02). A total of 187 (74.8%, 187/250) participants in the intervention and 167 (67.9%, 167/246) in the control group participated in the final measurements. There were

Background: Use of information and communication technologies (ICT) among seniors is increasing; however, studies on the use of ICT by seniors at the highest risk of health impairment are lacking. Frail and prefrail seniors are a group that would likely benefit from preventive nutrition and exercise interventions, both of which can take advantage of ICT. Objective: The objective of the study was to quantify the differences in ICT use, attitudes, and reasons for nonuse among physically frail, prefrail, and nonfrail home-dwelling seniors. Methods: This was a population-based questionnaire study on people aged 65–98 years living in Northern Finland. A total of 794 eligible individuals responded out of a contacted random sample of 1500. Results: In this study, 29.8% (237/794) of the respondents were classified as frail or prefrail. The ICT use of frail persons was lower than that of the nonfrail ones. In multivariable logistic regression analysis, age and education level were associated with both the use of Internet and advanced mobile ICT such as smartphones or tablets. Controlling for age and education, frailty or prefrailty was independently related to the nonuse of advanced mobile ICT (odds ratio, OR=0.61, P=.01), and frailty with use of the Internet (OR=0.45, P=.03). The frail or prefrail ICT nonusers also held the most negative opinions on the usefulness or usability of mobile ICT. When opinion variables were included in the model, frailty status remained a significant predictor of ICT use. Conclusions: Physical frailty status is associated with older peoples’ ICT use independent of age, education, and opinions on ICT use. This should be taken into consideration when designing preventive and assistive technologies and interventions for older people at risk of health impairment.

About one third of home-dwelling older people suffer a fall ech year. The most consuming falls occur when the person is alone and unable to get up, resulting in long lies which are associated with institutionalisation and high morbidity-mortality rate. Even though personal emergency response systems provide applications to call for help, older people are not always able or willing to activate them. Hence, an automatic fall detection system is an important setting. Even though pilot applications and commercial fall detection systems exist, the real-life validation of these systems is scant. The aim of this study was to develop a validated acceleration-based method for fall detection to be adapted for real-life applications among older people. Methods capable of discriminating between falls and activities of daily living (ADL) were determined based on laboratory tests. The threshold-based algorithms were validated with intentional falls in 20 middle-aged test persons and ADL in 20 middle-aged and 21 older people. The algorithm for the waist with impact and end posture detection was able to discriminate falls from ADL with 97% sensitivity and 100% specificity. In order to validate the fall detection system, a field test was performed with 16 residents in elderly care units wearing a wireless sensor. During the 6-month test period, acceleration data from five real-life falls were collected. One of the falls resulted in a hip fracture. These falls showed similar features as intentional falls. However, high pre impact velocity was detected in the case with a fracture, but not in all falls with preventative actions. The system had a fall detection sensitivity of 71.4% with a false alarm rate of 1.1 alarms over a 24-hour time period in this real-life pilot test. The data from real-life falls provide important material for further development of fall detection and studies on fall mechanism and fall prevention., Tiivistelmä Kotona asuvista yli 65-vuotiaista kaatuu vuosittain kolmannes. Mikäli kaatunut ei kykene nousemaan omin neuvoin, avun saaminen saattaa viivästyä. Tämä suurentaa sekä laitoshoitoon joutumisen todennäköisyyttä että kuoleman riskiä. Erilaisia hälytysjärjestelmiä on kyllä saatavilla, mutta ikääntyneet eivät aina kykene käyttämään niitä tai eivät jostain syystä halua tehdä hälytystä. Tämän vuoksi automaattiselle kaatumishälyttimelle on tarvetta. Tässä tutkimuksessa kehitettiin ja testattiin ikääntyneiden tarpeisiin soveltuva kiihtyvyysanturiin perustuva kaatumisen tunnistumenetelmä. Aineisto koottiin laboratorio-olosuhteissa kokeilla, joihin osallistui sekä nuoria että keski-ikäisiä. Raja-arvoon perustuvia tunnistusalgoritmeja testattiin 20 keski-ikäisen ohjeistettujen testikaatumisten sekä 20 keski-ikäisen ja 21 ikääntyneen arkisten askareiden tuottamalla datalla. Kaatumistapahtuman impaktin ja loppuasennon tunnistaminen vyötäröltä mitatuista kiihtyvyysarvoista erotteli kaatumisen muusta liikkeestä 95 % sensitiivisyydellä ja 100 % spesifisyydellä. Tunnistusmenetelmää testattiin kenttäkokeessa, jossa 16 ikääntynyttä hoitokodin asukasta piti vyötäröllään mittauslaitetta. Kuuden kuukauden aikana kiihtyvyyssignaali saatiin viidestä kaatumisesta. Yksi niistä aiheutti lonkkamurtuman. Analyysin mukaan näiden todellisten kaatumisten kiihtyvyyssignaalit muistuttivat testikaatumisia. Lonkkamurtumatapauksessa ennen impaktia mitattu nopeus oli erittäin korkea. Vastaavaa ei havaittu tapauksissa, joissa oli merkkejä siitä, että kaatumista oli yritetty estää. Kenttäkokeessa kaatumishälytysjärjestelmän sensitiivisyys oli 71.4 % ja vääriä hälytyksiä oli 1.1 vuorokaudessa. Tutkimuksessa saatua tietoa tosielämän kaatumistapahtumista voidaan käyttää hyväksi kehitettäessä kaatumisten ehkäisyä, niiden mekanismin tutkimista sekä kaatumisen tunnistusta kiihtyvyysanturien avulla.