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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: Up to 70% of women with polycystic ovary syndrome (PCOS) have pre-obesity or obesity. The aim of this study was to investigate whether women with PCOS have more weight-loss attempts than women without PCOS, regardless of BMI. Moreover, women's weight perceptions in relation to previous weight-loss attempts were evaluated. Methods: A population-based birth cohort study included women with (n = 278) and without PCOS (control individuals, n = 1560) who were examined at ages 31 and 46 years with questionnaires and clinical examinations. Results: Women with PCOS had more weight-loss attempts compared with control individuals at age 31 (47% vs. 34%, p <0.001) and 46 years (63% vs. 47%, p < 0.001). At age 46 years, PCOS was associated with multiple weight-loss attempts in the adjusted model (odds ratio: 1.43 [95% CI: 1.00‐2.03], p = 0.05). The perception of having overweight was more prevalent in those with PCOS, even among participants with normal weight, at age 31 (PCOS 47% vs. control 34%, p = 0.014) and 46 years (PCOS 60% vs. control 39%, p = 0.001). Conclusions: Women with PCOS were more likely to have experienced multiple weight-loss attempts and a perception of having overweight compared with control individuals, regardless of obesity status.

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 h of movement and non-movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4585 members of the Northern Finland Birth Cohort 1966, who wore a hip-worn accelerometer at the age of 46 years for 14 consecutive days. Time spent in sedentary behaviors, light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) were determined from the accelerometer and combined with self-reported sleep duration to obtain the 24-h time use. CRF was estimated from the peak heart rate in a submaximal step test. An isotemporal substitution paradigm was used to examine how sedentary time and substituting sedentary time with an equal amount of LPA, MVPA, or sleep were associated with adiposity markers, blood lipid levels, and fasting glucose and insulin. Sedentary time was independently and adversely associated with the markers of cardiometabolic health, even after adjustment for CRF, but not in partition models including LPA, MVPA, sleep, and CRF. Substituting 60, 45, 30, and 15 min/day of sedentary time with LPA or MVPA was associated with 0.2%–13.7% favorable differences in the cardiometabolic health markers after accounting for LPA, MVPA, sleep, CRF, and other confounders. After adjustment for movement and non-movement behaviors within the 24-h cycle, reallocating additional time to both LPA and MVPA was beneficially associated with markers of cardiometabolic health in middle-aged adults regardless of their CRF level.

Cardiovascular disease (CVD) causes a high disease burden. Physical activity (PA) reduces CVD morbidity and mortality. We aimed to determine the relationship between the composition of moderate-to-vigorous PA (MVPA), light PA (LPA), sedentary behavior (SB), and sleep during midlife to the incidence of major adverse cardiac events (MACE) and all-cause mortality at a 7-year follow-up. The study population consisted of Northern Finland Birth Cohort 1966 members who participated in the 46-year follow-up in 2012 and were free of MACE (N = 4147). Time spent in MVPA, LPA, and SB was determined from accelerometer data. Sleep time was self-reported. Hospital visits and deaths were obtained from national registers. Participants were followed until December 31, 2019, or first MACE occurrence (acute myocardial infarction, unstable angina pectoris, stroke, hospitalization due to heart failure, or death due to CVD), death from another cause, or censoring. Cox proportional hazards model was used to estimate hazard ratios of MACE incidence and all-cause mortality. Isotemporal time reallocations were used to demonstrate the dose–response association between time spent in behaviors and outcome. The 24-h time composition was significantly associated with incident MACE and all-cause mortality. More time in MVPA relative to other behaviors was associated with a lower risk of events. Isotemporal time reallocations indicated that the greatest risk reduction occurred when MVPA replaced sleep. Higher MVPA associates with a reduced risk of incident MACE and all-cause mortality after accounting for the 24-h movement composition and confounders. Regular engagement in MVPA should be encouraged in midlife.

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.

Objectives: To investigate the effects of 4 months of customised, home-based exergaming on physical function and pain after total knee replacement (TKR) compared with standard exercise protocol. Methods: In this non-blinded randomised controlled trial, 52 individuals aged 60–75 years undergoing TKR were randomised into an exergaming (intervention group, IG) or a standard exercising group (control group, CG). Primary outcomes were physical function and pain measured before and after (2 months and 4 months) surgery using the Oxford Knee Score (OKS) and Timed Up and Go (TUG) test. Secondary outcomes included measures of the Visual Analogue Scale, 10m walking, short physical performance battery, isometric knee extension and flexion force, knee range of movement and satisfaction with the operated knee. Results: Improvement in mobility measured by TUG was greater in the IG (n=21) at 2 (p=0.019) and 4 months (p=0.040) than in the CG (n=25). The TUG improved in the IG by −1.9 s (95% CI, −2.9 to −1.0), while it changed by −0.6 s (95% CI −1.4 to 0.3) in the CG. There were no differences between the groups in the OKS or secondary outcomes over 4 months. 100% of patients in the IG and 74% in the CG were satisfied with the operated knee. Conclusion: In patients who have undergone TKR, training at home with customised exergames was more effective in mobility and early satisfaction and as effective as standard exercise in pain and other physical functions. In both groups, knee-related function and pain improvement can be considered clinically meaningful. Trial registration number: NCT03717727.

Purpose: This study aimed to identify and characterize joint profiles of sedentary time and physical activity among adults and to investigate how these profiles are associated with markers of cardiometabolic health. Methods: The participants included 3702 of the Northern Finland Birth Cohort 1966 at age 46 yr, who wore a hip-worn accelerometer during waking hours and provided seven consecutive days of valid data. Sedentary time, light-intensity physical activity, and moderate- to vigorous-intensity physical activity on each valid day were obtained, and a data-driven clustering approach (“KmL3D”) was used to characterize distinct joint profiles of sedentary time and physical activity intensities. Participants self-reported their sleep duration and performed a submaximal step test with continuous heart rate measurement to estimate their cardiorespiratory fitness (peak heart rate). Linear regression was used to determine the association between joint profiles of sedentary time and physical activities with cardiometabolic health markers, including adiposity markers and blood lipid, glucose, and insulin levels. Results: Four distinct groups were identified: “active couch potatoes” (n = 1173), “sedentary light movers” (n = 1199), “sedentary exercisers” (n = 694), and “movers” (n = 636). Although sufficiently active, active couch potatoes had the highest daily sedentary time (>10 h) and lowest light-intensity physical activity. Compared with active couch potatoes, sedentary light movers, sedentary exercisers, and movers spent less time in sedentary by performing more physical activity at light-intensity upward and had favorable differences in their cardiometabolic health markers after accounting for potential confounders (1.1%–25.0% lower values depending on the health marker and profile). Conclusions: After accounting for sleep duration and cardiorespiratory fitness, waking activity profiles characterized by performing more physical activity at light-intensity up

Introduction: There is limited understanding of how older adults can reach kinematic goals in rehabilitation while performing exergames and conventional exercises, and how similar or different the kinematics during exergaming are when compared with conventional therapeutic exercise with similar movement. The aim of this study was to describe the movement characteristics performed during exercise in custom-designed exergames and conventional therapeutic exercises among patients who have undergone unilateral total knee replacement (TKR). In addition, the secondary aim was to assess the relation of these exercise methods, and to assess participants’ perceived exertion and knee pain during exergaming and exercising. Materials and methods: Patients up to 4 months after the TKR surgery were invited in a single-visit exercise laboratory session. A 2D motion analysis and force plates were employed to evaluate movement characteristics as the volume, range, and intensity of movement performed during custom-designed knee extension-flexion and weight shifting exergames and conventional therapeutic exercises post TKR. The perceived exertion and knee pain were assessed using the Borg Rating of Perceived Exertion and Visual Analog Scale, respectively. Results: Evaluation of seven patients with TKR [age median (IQR), 65 (10) years] revealed that the volume and intensity of movement were mostly higher during exergames. Individual goniometer-measured knee range of motion were achieved either with exergames and conventional therapeutic exercises, especially in knee extension exercises. The perceived exertion and knee pain were similar after exergames and conventional therapeutic exercises. Conclusions: During custom-designed exergaming the patients with TKR achieve the movement characteristics appropriate for post-TKR rehabilitation without increasing the stress and pain experienced even though the movement characteristics might be partly different from conventional therapeut

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.

Background: Articular surface size is traditionally considered to be a relatively stable trait throughout adulthood. Increased joint size reduces bone and cartilage tissue strains. Although physical activity (PA) has a clear association with diaphyseal morphology, the association between PA and articular surface size is yet to be confirmed. This cross-sectional study aimed to clarify the role of moderate-to-vigorous PA (MVPA) in knee morphology in terms of tibiofemoral joint size. Methods: A sample of 1508 individuals from the population-based Northern Finland Birth Cohort 1966 was used. At the age of 46, wrist-worn accelerometers were used to monitor MVPA (≥3.5 METs) during a period of two weeks, and knee radiographs were used to obtain three knee breadth measurements (femoral biepicondylar breadth, mediolateral breadth of femoral condyles, mediolateral breadth of the tibial plateau). The association between MVPA and knee breadth was analyzed using general linear models with adjustments for body mass index, smoking, education years, and accelerometer weartime. Results: Of the sample, 54.8% were women. Most individuals were non-smokers (54.6%) and had 9—12 years of education (69.6%). Mean body mass index was 26.2 (standard deviation 4.3) kg/m2. MVPA was uniformly associated with all three knee breadth measurements among both women and men. For each 60 minutes/day of MVPA, the knee breadth dimensions were 1.8—2.0% (or 1.26—1.42 mm) larger among women (p < 0.001) and 1.4—1.6% (or 1.21—1.28 mm) larger among men (p < 0.001). Conclusions: Higher MVPA is associated with larger tibiofemoral joint size. Our findings indicate that MVPA could potentially increase knee dimensions through similar biomechanical mechanisms it affects diaphyseal morphology, thus offering a potential target in reducing tissue strains and preventing knee problems. Further studies are needed to confirm and investigate the association between articulation area and musculoskeletal health

Physical inactivity leads to a high risk of medical complications and triggering substantial health care expenses. The goal of the project within which the research is conducted is to explore the effect of the use of nudges to dissuade individuals from physical inactivity. This study is aimed to design and develop a zero/low-cost nudging mHealth intervention that allows users to check their time with normative messages when walking. This intervention is then utilised for individuals to investigate whether it stimulates physical activity. The design of the nudging intervention is followed by an iterative Design Thinking process. The result of the pilot study has shown us that participants highlighted personal normative message installed as a screensaver in a smartphone as a zero-cost solution to dissuade their physical inactivity. Our future effort is to access this intervention by experimental design studies with quantitative and qualitative surveys, which will be carried out with students to measure physical activity behavioural change.

Background: Exergames can provide encouraging exercise options. Currently, there is limited evidence regarding home-based exergaming in the postoperative phase of total knee replacement (TKR). Objective: This study aimed to investigate the effects of a 4-month postoperative home-based exergame intervention with an 8-month follow-up on physical function and symptoms among older persons undergoing TKR compared with home exercise using a standard protocol. In addition, a concurrent embedded design of a mixed methods study was used by including a qualitative component within a quantitative study of exergame effects. Methods: This was a dual-center, nonblinded, two-arm, parallel group randomized controlled trial with an embedded qualitative approach. This study aimed to recruit 100 patients who underwent their first unilateral TKR (aged 60–75 years). Participants were randomized to the exergame or standard home exercise arms. Participants followed a custom-made exergame program independently at their homes daily for 4 months. The primary outcomes at 4 months were function and pain related to the knee using the Oxford Knee Score questionnaire and mobility using the Timed Up and Go test. Other outcomes, in addition to physical function, symptoms, and disability, were game user experience, exercise adherence, physical activity, and satisfaction with the operated knee. Assessments were performed at the preoperative baseline and at 2, 4, and 12 months postoperatively. Exergame adherence was followed from game computers and using a structured diary. Self-reported standard exercise was followed for 4 months of intervention and physical activity was followed for 12 months using a structured diary. Qualitative data on patients’ perspectives on rehabilitation and exergames were collected through laddering interviews at 4 and 12 months. Results: This study was funded in 2018. Data collection began in 2019 and was completed in January 2022. The COVID-19 pandemic caused an u

Purpose: The incidence of acetabular fractures due to low-energy falls is increasing among the geriatric population. Studies have shown that several biomechanical factors such as body configuration, impact velocity, and trochanteric soft-tissue thickness contribute to the severity and type of acetabular fracture. The effect of reduction in apparent density and elastic modulus of bone as well as other bone mechanical properties due to osteoporosis on low-energy acetabular fractures has not been investigated. Methods: The current comprehensive finite element study aimed to study the effect of reduction in bone mechanical properties (trabecular, cortical, and trabecular + cortical) on the risk and type of acetabular fracture. Also, the effect of reduction in the mechanical properties of bone on the load-transferring mechanism within the pelvic girdle was examined. Results: We observed that while the reduction in the mechanical properties of trabecular bone considerably affects the severity and area of trabecular bone failure, reduction in mechanical properties of cortical bone moderately influences both cortical and trabecular bone failure. The results also indicated that by reducing bone mechanical properties, the type of acetabular fracture turns from elementary to associated, which requires a more extensive intervention and rehabilitation period. Finally, we observed that the cortical bone plays a substantial role in load transfer, and by increasing reduction in the mechanical properties of cortical bone, a greater share of load is transmitted toward the pubic symphysis. Conclusions: This study increases our understanding of the effect of osteoporosis progression on the incidence of low-energy acetabular fractures. The osteoporosis-related reduction in the mechanical properties of cortical bone appears to affect both the cortical and trabecular bones. Also, during the extreme reduction in the mechanical properties of bone, the acetabular fracture type will

Supporting the expansion of best practices in Biomedical Engineering (BME) can facilitate pathway toward the providing universal health coverage and more equitable and accessible healthcare technologies, especially in low- and middle-income (LMI) settings. These best practices can act as drivers of change and may involve scientific-technological issues, human intervention during technology development, educational aspects, social performance management for improved interactions along the medical technology life cycle, methods for managing resources and approaches for the establishment of regulatory frameworks. The aim of our study was to identify weaknesses and strengths of the scientific, technological, socio-political, regulatory and educational landscape in BME in LMI resource settings. We thus analysed the current state-of-the-art through six dimensions considered fundamental for advancing quality and equity in healthcare: 1) relevant and 2) emergent technologies, 3) new paradigms in medical technology development, 4) innovative BME education, 5) regulation and standardization for novel approaches, and 6) policy making. In order to evaluate and compare their relevance, maturity and implementation challenges, they were assessed through a questionnaire to which 100 professionals from 35 countries with recognized experience in the field of BME and its application to LMI settings responded. The results are presented and discussed, highlighting the main challenges and pinpointing relevant areas where intervention, including local lobbying and international promotion of best practices is necessary. We were also able to identify areas where minimal effort is required to make big changes in global health.

Summary: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75–0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67–0.97]. Conclusion: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Osteoporosis significantly increases the risk of bone fracture. Falls are the leading cause of osteoporotic fracture among elderly people. The incidence of low-energy acetabular fracture among the elderly population has been increasing in developed countries in recent decades. Low-energy acetabular fractures mostly happen due to falling from a standing height. While the mechanism of proximal femur fracture due to low-energy falls has been investigated widely owing to higher incidence and the associated morbidities and mortalities, the biomechanical mechanism responsible for acetabular fracture has remained underexplored. Through developing comprehensive finite element models as a reliable and cost-effective method of study, this thesis aims to evaluate the effects of various biomechanical factors contributing to the occurrence, severity, and type of acetabular fracture during low-energy sideways falls. The finite element models were based on abdominal computed tomography (CT) images of a median male without acetabular fracture history, retrieved from a large clinical dataset. While the bony parts and trochanteric soft tissue were reconstructed directly from the CT images, cartilages and ligaments were added manually according to anatomy and literature. The material models able to simulate the behavior of tissues during the impact loading condition were considered. Initial/boundary conditions, loads, contacts, interactions, and controlling parameters were applied according to the literature. By varying the corresponding variables in the model, the effects of body configuration, impact velocity, flooring material, trochanteric soft tissue stiffness, and bone loss on the risk the acetabular fracture were investigated. Results of the current study showed that the effects of body configuration, impact velocity, and bone loss on the risk of acetabular fracture are substantial. However, the effects of flooring material and trochanteric soft tissue stiffness remain, Tiivistelmä Luukato eli osteoporoosi lisää merkittävästi luunmurtuman riskiä. Kaatuminen on tärkein osteoporoottisten murtumien syy ikääntyvillä. Matalaenergisten lonkkamaljan murtumien esiintyvyys on kasvanut kehittyneissä maissa viime vuosikymmeninä. Useimmiten matalaenergiset lonkkamaljan murtumat ovat seurausta kaatumisesta. Kaatumisen aiheuttamien lonkkamurtumien mekamismia on tutkittu runsaasti, koska niitä esiintyy enemmän ja niihin liittyy korkea sairastavuus ja kuolleisuus. Sen sijaan lonkkamaljan biomekaanista mekanismia on toistaiseksi tutkittu vain vähän. Tämän väitöstutkimuksen tavoitteena oli arvioida erilaisten biomekaanisten tekijöiden vaikutusta matalaenergisen kaatumisen aiheuttaman lonkkamaljan murtuman esiintymiseen, vakavuuteen ja murtumatyyppiin, kehittäen siihen soveltuvia luotettavia, kustannustehokkaita elementtimalleja. Elementtimallit perustuivat tietokonetomografiakuviin (TT), jotka oli valikoitu laajasta kliinisestä aineistosta edustamaan sellaisten miesten mediaania, joilla ei ole lonkkamaljan murtumaa. Luut ja pehmytkudos rekonstruoitiin suoraan TT-kuvista. Rustokudos ja nivelsiteet lisättiin manuaalisesti niiden anatomian ja kirjallisuuden perusteella. Materiaalimallit valittiin niin, että niillä voidaan simuloida kudosten käyttäytymistä törmäyskuormitustilanteessa. Alku- ja reunaehdot, kuormitukset, rajakontaktit, vuorovaikutukset ja säätöparametrit valittiin kirjallisuuden perusteella. Kehon asennon, törmäysnopeuden, lattiamateriaalin, pehmytkudoksen jäykkyyden ja luukadon vaikutusta lonkkamaljan murtumariskiin tutkittiin vaihtelemalla kyseisiä muuttujia. Tutkimuksen tulokset osoittivat, että kehon asento, törmäysnopeus ja luukato vaikuttavat oleellisesti lonkkamaljan murtumariskiin. Sen sijaan lattiamateriaalin ja pehmytkudoksen jäykkyyden vaikutus jäi vähäiseksi. Tutkimuksen perusteella törmäyshetkellä vaakasuorassa olevat vartalo ja reisi, yli 3,17 m/s törmäysnopeus sekä merkittävä luukato lisäävät huomattavasti lonkkamaljan murt

Summary: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75–0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67–0.97]. Conclusion: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.

Purpose: Machine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data. Methods: The hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set. Results: The appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods. Conclusions: A subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.

Physical activity (PA) influences bone tissue throughout the lifespan and is believed to have a positive effect on vertebral size also during adulthood, thus potentially improving vertebral strength and reducing fracture risk. Modic changes (MC), divided in three subgroups (MC1, MC2, MC3), are vertebral bone marrow changes visible in magnetic resonance imaging (MRI) that are associated with non-specific low back pain (LBP). This thesis includes three population-based cohort studies (I, II, III) which aim was to investigate the association between device-measured moderate to vigorous intensity PA (MVPA, ≥ 3.5 METs), vertebral cross-sectional area (CSA) (I), and lumbar MC (II), and to determine the relationship between vertebral dimensions and lumbar MC (III), using a subsample (n = 1202–1374) of the Northern Finland Birth Cohort 1966. Participants aged 46 to 48 underwent lumbar MRI from which vertebral dimensions and lumbar MC were assessed, and their daily MVPA was determined by a wrist-worn activity monitor. The results showed that MVPA was positively associated with vertebral CSA among both sexes. Among men, a higher amount of MVPA was associated with increased odds of any lumbar MC, particularly MC2, whereas among women no such association was found. Exploration of the relationship between vertebral dimensions and lumbar MC revealed that vertebral height was positively associated with the presence of lumbar MC2, whereas vertebral CSA and volume were not associated with MC. The results of this thesis propose that MVPA has a positive impact on vertebral size among both sexes at midlife, thus lifelong MVPA might be a preventive action for reducing future vertebral fractures. Lumbar MC seem to be associated with higher vertebrae. MVPA seems to be associated with an increased likelihood of lumbar MC, but only among men and especially with MC2, strengthening the hypothesis that changed mechanical conditions due to hyperloading are connected to MC2. Further stu, Tiivistelmä Alaselkäkipu on yleisin työikäisten toimintakykyä heikentävä sairaus, ja nikamamurtumat ovat merkittävä ikääntyvän väestön liikuntakykyä ja elämänlaatua heikentävä tekijä maailmanlaajuisesti. Fyysinen aktiivisuus vaikuttaa luustoon läpi elämän, ja sillä on mahdollisesti positiivinen vaikutus nikaman kokoon vielä aikuisiässäkin vahvistaen nikamaa ja ennaltaehkäisten murtumia. Modic-muutokset (MC:t, alaryhmät MC1, MC2, MC3) ovat magneettikuvauksessa nähtäviä selkänikamien luuydinmuutoksia, jotka ovat yhteydessä pitkittyneeseen alaselkäkipuun. Väitöstutkimuksen (osatyöt I, II, III) tavoitteena oli selvittää kiihtyvyysanturiin perustuvalla ranteessa pidettävällä aktiivisuusmittarilla mitatun vähintään kohtuullisesti kuormittavan fyysisen aktiivisuuden (≥ 3.5 METs) yhteyttä nikaman poikkipinta-alaan (I) ja lannerangan MC:n riskiin (II). Lisäksi selvitettiin nikaman koon ja MC:n välistä yhteyttä (III). Tutkimusaineistona oli väestöpohjainen Pohjois-Suomen syntymäkohortti 1966. Tutkittavien (n = 1202–1374) ollessa 46–48-vuotiaita, he osallistuivat fyysisen aktiivisuuden mittauksiin ja lannerangan magneettikuvaukseen. Tulokset osoittivat, että vähintään kohtuullisesti kuormittava fyysinen aktiivisuus oli positiivisesti yhteydessä nikaman poikkipinta-alaan molemmilla sukupuolilla. Miehillä suurempi määrä päivittäistä vähintään kohtuukuormitteista fyysistä aktiivisuutta oli positiivisesti yhteydessä lannerangan MC:n, erityisesti MC2:n, naisilla yhteyttä ei havaittu. Nikaman korkeus oli positiivisesti yhteydessä lannerangan MC2:n, nikaman poikkipinta-ala ja tilavuus eivät sen sijaan olleet yhteydessä MC:n. Tutkimustulosten perusteella voidaan todeta, että vähintään kohtuukuormitteisella fyysisellä aktiivisuudella on positiivinen vaikutus nikaman kokoon keski-iässä molemmilla sukupuolilla ja sitä voidaan pitää yhtenä varteenotettavana nikamamurtumien ennaltaehkäisykeinona. Lannerangan MC:t saattavat olla yhteydessä korkeampaan nikamaan, ja miehillä vähintään kohtuuk

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.

Low-energy acetabular fractures (LEAFs) are osteoporotic pelvic fractures. The clinical diagnosis of acetabular fractures is conducted using computed tomography (CT) imaging. A LEAF is typically a result of a sideways fall and the common fracture patterns are fractures of the anterior column, anterior wall, anterior with a posterior hemi-transverse and an anteromedial dislocation of the femoral head. The fall mechanisms and the recurrent fracture patterns of LEAFs have previously been investigated. However, the etiology of LEAFs still remains unclear. Hence, the aim of this thesis was to elucidate the biomechanical and structural risk factors associated with low-energy acetabular fractures in the elderly. The study data consisted of clinical CT images of subjects with acetabular fractures (n=121) and their gender-age matched controls. Measurements of the proximal femur and acetabular geometry variables were made on 3-D pelvic models. 3-D regions of interest were extracted at the femoral head and the acetabulum to assess the trabecular architecture and to discriminate cases from controls using machine learning methods. Furthermore, the presence of radiographic hip osteoarthritis (rHOA) was assessed by optimizing a deep learning model. The fracture cases showed a significantly lower neck shaft angle, longer femoral neck axis length, and a lower bone volume fraction and mean gray value (GV) at the femoral head, and a lower mean GV at the acetabulum compared to their controls. Although significant differences in rHOA distributions were not found, fracture subjects showed a higher prevalence of rHOA than the controls. In addition, the machine learning and deep learning models were able to discriminate fractures from controls as well as to classify rHOA from the CT data. In conclusion, potential biomechanical and structural risk factors associated with low-energy acetabular fractures in the elderly were identified, using clinical CT to measure the pelvic geometry, Tiivistelmä Matalaenergiset lonkkamaljan murtumat ovat osteoporoottisia lantiomurtumia, jotka kliinisesti diagnosoidaan tietokonetomografiakuvista. Matalaenerginen lonkkamaljan murtuma tyypillisesti johtuu kyljelleen kaatumisesta. Kaatumismekanismia ja murtumatyyppejä on aiemmin tutkittu, mutta muutoin matalaenergisiä lonkkamaljan murtumia on tutkittu vain vähän. Toistaiseksi niiden syntymekanismia ei tiedetä. Tämän väitöskirjatyön tarkoituksena oli selvittää matalaenergisten lonkkamaljan murtumien biomekaanisia ja rakenteellisia riskitekijöitä ikääntyvillä henkilöillä. Tutkimusainesto koostui tietokonetomografiakuvista, jotka oli otettu henkilöistä, joiden lonkkamalja oli murtunut ja heidän ikä- ja sukupuolikontrolloiduista verrokeista. Reisiluun yläosan ja lonkkamaljan geometria mitattiin lantion kolmiulotteisista malleista. Hohkaluun rakenne arvioitiin reisiluun pään ja lonkkamaljan alueelta. Laskettuja muuttujia käytettiin koneoppimismenetelmän avulla erottamaan murtumatapaukset verrokeista. Lonkkanivelrikko arvioitiin syväoppimismenetelmällä. Murtumatapauksilla oli pienempi reisiluun kaulan kulma, pidempi reisiluun kaula ja alhaisempi luun tiheys ja keskimääräinen harmaasävyarvo reisiluun pään alueella sekä matalampi keskimääräinen harmaasävyarvo lonkkamaljan alueella. Murtumatapauksien keskuudessa esiintyi enemmän lonkan nivelrikkoa kuin verrokeilla, mutta ero ei ollut tilastollisesti merkitsevä. Koneoppimismenetelmät onnistuivat luokittelemaan murtumatapaukset verrokeista ja syväoppimismenetelmä onnistui tunnistamaan lonkan nivelrikon tietokonetomografiakuvista. Loppupäätelmänä voidaan todeta, että tässä väitöskirjatyössä tunnistettiin mahdollisia biomekaanisia ja rakenteellisia riskitekijöitä matalaenergisille lonkkamaljan murtumille ikääntyvien keskuudessa mittaamalla lantion geometriaa ja hohkaluun rakennetta sekä tunnistamalla lonkan nivelrikko tietokonetomografiakuvista.

While the incidence of hip fractures has declined during the last decades, the incidence of acetabular fractures resulting from low-energy sideways falls has increased, and the mechanisms responsible for this trend remain unknown. Previous studies have suggested that body configuration during the impact plays an important role in a hip fracture. Thus, the aim of this study was to investigate the effect of body configuration angles (trunk tilt angle, trunk flexion angle, femur horizontal rotation angle, and femur diaphysis angle) on low-energy acetabular fractures via a parametric analysis. A computed tomography–based (CT) finite element model of the ground–proximal femur–pelvis complex was created, and strain magnitude, time-history response, and distribution within the acetabulum were evaluated. Results showed that while the trunk tilt angle and femur diaphysis angle have the greatest effect on strain magnitude, the direction of the fall (lateral vs. posterolateral) contributes to strain distribution within the acetabulum. The results also suggest that strain level and distribution within the proximal femur and acetabulum resulting from a sideways fall are not similar and, in some cases, even opposite. Taken together, our simulations suggest that a more horizontal trunk and femoral shaft at the impact phase can increase the risk of low-energy acetabular fractures.

Although the health benefits of physical activity (PA) are well known, the majority of adolescents are not sufficiently physically active. New innovative ways to promote active lifestyles are needed. This study aimed to evaluate the effectiveness of a wrist-worn activity monitor (Polar Active) and a gamified web-based mobile service in promoting PA in young men. The study also examined the convergent validity between three different accelerometer-based PA measurement methods. In this study, two randomized controlled trials (RCT) were conducted in Oulu, Finland among 18-year-old men. The three-month RCT (n=276) was conducted in fall 2012 and the six-month population-based RCT (n=496) between September 2013 and March 2014. In both trials, participants were randomized to an intervention and a control group. The intervention group was given the wrist-worn Polar Active monitor with PA feedback. In the six-month trial, the intervention group also got access to a mobile service developed in this study. During both trials, PA was continuously measured in both study groups. In the control group, PA was measured with an otherwise similar monitor but which provided only the time of day and no feedback. The convergent validity was examined between the agreement in time spent at different PA levels using Polar Active, mean amplitude deviation (MAD) of raw acceleration, and Actigraph with the Freedson thresholds. In the validation study, all three activity monitors were continuously used for two weeks by 41 volunteers. The three-month trial had a short-term positive effect on daily moderate-to-vigorous PA (MVPA) and sedentary time in the intervention group. The positive change in sedentary time was sustained for longer. During the six-month trial, a positive trend in favor of the intervention group was observed in daily MVPA. Low amount of daily vigorous PA at baseline was associated with the increase in MVPA. The functionalities of the mobile service related to PA were, Tiivistelmä Vaikka fyysisen aktiivisuuden terveysvaikutukset ovat hyvin tiedossa, valtaosa nuorista ei liiku riittävästi. Tarvitaan uusia menetelmiä edistämään nuorten aktiivista elämäntapaa. Tämän tutkimuksen tarkoituksena oli selvittää ranteessa pidettävän aktiivisuusmittarin ja pelillistetyn mobiilipalvelun vaikutus nuorten miesten fyysiseen aktiivisuuteen. Lisäksi tutkittiin kolmen eri kiihtyvyysmittaukseen perustuvan aktiivisuusmittausmenetelmän yhtenevyyttä. Tutkimuksessa toteutettiin kaksi satunnaistettua, kontrolloitua interventiota, oululaisilla 18-vuotiailla miehillä. Kolmen kuukauden interventio (n=276) toteutettiin syksyllä 2012 ja 6 kuukauden väestöpohjainen interventio (n=496) syyskuun 2013 ja maaliskuun 2014 välillä. Molemmissa interventioissa osallistujat satunnaistettiin interventio- ja kontrolliryhmään. Interventioryhmä sai käyttöönsä aktiivisuusmittarin, joka antoi palautetta aktiivisuudesta. Lisäksi 6 kuukauden interventiossa interventioryhmä sai käyttöönsä tutkimuksessa kehitetyn mobiilipalvelun. Molemmissa interventioissa kaikkien tutkittavien fyysistä aktiivisuutta mitattiin jatkuvasti. Kontrolliryhmä ei saanut aktiivisuuspalautetta. Aktiivisuusmittausmenetelmien yhtenevyyttä fyysisen aktiivisuuden eri tasoilla vietetyn ajan mittaamisessa tutkittiin vertaamalla Polar Active -mittaria, kiihtyvyyssignaalin perustuvaa menetelmää (mean amplitude deviation, MAD) ja Actigraph-mittaria Freedsonin raja-arvoilla. Neljäkymmentäyksi vapaaehtoista käytti kaikkia kolmea mittaria samanaikaisesti kahden viikon ajan. Kolmen kuukauden interventiolla oli lyhytaikainen positiivinen vaikutus kohtuukuormitteisen tai raskaan fyysisen aktiivisuuden ja paikallaanolon määrään. Paikallaanolon muutos säilyi pidempään. Kuuden kuukauden interventiossa havaittiin interventioryhmässä pieni, ei-merkitsevä nousu kohtuukuormitteisessa tai raskaassa aktiivisuudessa. Aktiivisuus lisääntyi intervention aikana niillä miehillä, joilla alussa oli vähän raskasta fyysistä aktiivisuutt

A low-energy acetabular fracture, as a result of falling from standing height, is common among elderly patients and the number of cases is increasing rapidly in developed countries. Several biomechanical factors contribute to the incidence, severity, and type of acetabular fractures, such as body configuration at the impact moment or bone and soft-tissue quality. The current parametric study developed a comprehensive finite element model of the pelvic girdle and simple representation of the whole body and investigated the effects of impact velocity, conventional indoor/outdoor flooring material, and trochanteric soft-tissue stiffness on an acetabular fracture. Our results show that whereas the impact velocity has a substantial influence on the incidence and type of acetabular fracture, the effects of conventional flooring materials and trochanteric soft-tissue quality are not remarkable. It seems that other factors such as the quality of bone (healthy vs. osteoporotic), the thickness of trochanteric soft-tissue, and body configuration at the impact are more critical in the occurrence and type of the acetabular fracture. These results can be valuable in the prevention of acetabular fractures and the design of protective measures such as hip pads or novel flooring materials.

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

The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures.

The incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures.