Julia Pietilä

Objectively measured physical activity in Finnish employees: a cross-sectional study

Objectives To objectively measure the amount of intensity-specific physical activity by gender and age with respect to body mass index (BMI) during workdays and days off among Finnish employees. Design A cross-sectional study. Setting Primary care occupational healthcare units. Participants A sample of 9554 Finnish employees (4221 men and 5333 women; age range 18–65 years; BMI range 18.5–40 kg/m2) who participated in health assessments related to occupational health promotion. Main outcome measurements The amount of moderate-to-vigorous (MVPA) and vigorous (VPA) physical activity (≥3 and ≥6 metabolic equivalents, respectively) was assessed by estimating the minute-to-minute oxygen consumption from the recorded beat-to-beat R-R interval data. The estimation method used heart rate, respiration rate and on/off response information from R-R interval data calibrated by age, gender, height, weight and self-reported physical activity class. The proportion of participants fulfilling the aerobic physical activity recommendation of ≥150 min/week was calculated on the basis of ≥10 min bouts, by multiplying the VPA minutes by 2. Results Both MVPA and VPA were higher among men and during days off, and decreased with increasing age and BMI (p<0.001 for all). Similar results were observed when the probability of having a bout of MVPA or VPA lasting continuously for ≥10 min per measurement day was studied. The total amount of VPA was low among overweight (mean ≤2.6 min/day), obese (mean ≤0.6 min/day) and all women in the age group 51–65 years (mean ≤2.5 min/day) during both types of days. The proportion of participants fulfilling the aerobic physical activity recommendation was highest for normal weight men (65%; 95% CI 62% to 67%) and lowest for obese women (10%; 95% CI 8% to 12%). Conclusions Objectively measured physical activity is higher among men and during days off, and decreases with increasing age and BMI. The amount of VPA is very low among obese, overweight and older women.

Physical Activity: Absolute Intensity versus Relative-to-fitness-level Volumes

Purpose This study aimed to investigate in a real-life setting how moderate- and vigorous-intensity physical activity (PA) volumes differ according to absolute intensity recommendation and relative to individual fitness level by sex, age, and body mass index. Methods A total of 23,224 Finnish employees (10,201 men and 13,023 women; ages 18–65 yr; body mass index = 18.5–40.0 kg·m−2) participated in heart rate recording for 2+ d. We used heart rate and its variability, respiration rate, and on/off response information from R-R interval data calibrated by participant characteristics to objectively determine daily PA volume, as follows: daily minutes of absolute moderate (3–<6 METs) and vigorous (≥6 METs) PA and minutes relative to individual aerobic fitness for moderate (40%–<60% of oxygen uptake reserve) and vigorous (≥60%) PA. Results According to absolute intensity categorization, the volume of both moderate- and vigorous-intensity PA was higher in men compared with women (P < 0.001), in younger compared with older participants (P < 0.001), and in normal weight compared with overweight or obese participants (P < 0.001). When the volume of PA intensity was estimated relative to individual fitness level, the differences were much smaller. Mean daily minutes of absolute vigorous-intensity PA were higher than those of relative intensity minutes in normal weight men ages 18–40 yr (17.7, 95% confidence interval [CI] = 16.9–18.6, vs 8.6, 95% CI = 8.0–9.1; P < 0.001), but the reverse was the case for obese women ages 41–65 yr (0.3, 95% CI = 0.2–0.4, vs 7.8, 95% CI = 7.2–8.4; P < 0.001). Conclusion Compared with low-fit persons, high-fit persons more frequently reach an absolute target PA intensity, but reaching the target is more similar for relative intensity.

Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities

With the advances in sensor technology and the emergence of new sensor systems, it is important to assess the accuracy of these devices. In this paper, we describe an evaluation study for two wrist-worn devices, namely PulseOn (PO) and Empatica E4 (E4), measuring photoplethysmography – based heart rate (PPG HR) and inter-beat intervals (IBIs). The accuracy and reliability of PPG HR and beat-to-beat detection are evaluated with respect to electrocardiography (ECG) – based HR and IBIs during different daily activities, such as sitting, standing, household work and cycling. The evaluation study employed data from twenty male subjects. The absolute difference of PPG and ECG HR was less than 10 bpm for 90-99% and 81-97% of time for PO and E4, respectively. The accuracy and reliability of the devices were decreased during household work due to the excess hand movements. On average, the mean absolute error in HR was 2.5 bpm higher in PO and 3.7 bpm higher in E4 during household work than during sitting. The percentage of correctly detected heartbeats was 89% for PO and 68% for E4 during sitting but 76% for PO and only 9% for E4 during household work. PO showed better beat-to-beat detection accuracy than E4 in all activities. The errors in heart rate variability measure (HRV) of root mean square of successive inter-beat interval differences were 3.5±3.9 ms for PO and 10.2±6.7 ms for E4 during sitting, but 18.0±10.9 ms for PO and 48.7±21.8 ms for E4 during cycling. As a conclusion, PPG – based wrist-worn devices are accurate and reliable for HR and beat-to-beat detection when the amount of hand movements is not excess but HRV can be estimated from PPG IBI data reliably only during resting conditions. Moreover, there were significant differences in accuracy between different devices.

Exploratory analysis of associations between individual lifestyles and heart rate variability -based recovery during sleep

Sleep is the most important period for recovering from daily stress and load. Assessment of the stress recovery during sleep is therefore, an important metric for care and quality of life. Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system (ANS) activity, and HRV-based methods can be used to assess physiological recovery, characterized by parasympathetic domination of the ANS. HRV is affected by multiple factors of which some are unmodifiable (such as age and gender) but many are related to daily lifestyle choices (e.g. alcohol consumption, physical activity, sleeping times). The purpose of this study was to investigate the association of these aforementioned factors on HRV-based recovery during sleep on a large sample. Variable importance measures yielded by random forest were used for identifying the most relevant predictors of sleep-time recovery. The results emphasize the disturbing effects of alcohol consumption on sleep-time recovery. Good physical fitness is associated to good recovery, but acute physical activity seems to challenge or delay the recovery process for the next night. Longer sleeping time enables more recovery minutes, but the proportion of recovery (i.e. recovery efficiency) seems to peak around 7.0-7.25 hours of sleep.

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial Accelerometer Wrist-Band

Wrist-worn sensors have better compliance for activity monitoring compared to hip, waist, ankle or chest positions. However, wrist-worn activity monitoring is challenging due to the wide degree of freedom for the hand movements, as well as similarity of hand movements in different activities such as varying intensities of cycling. To strengthen the ability of wrist-worn sensors in detecting human activities more accurately, motion signals can be complemented by physiological signals such as optical heart rate (HR) based on photoplethysmography. In this paper, an activity monitoring framework using an optical HR sensor and a triaxial wrist-worn accelerometer is presented. We investigated a range of daily life activities including sitting, standing, household activities and stationary cycling with two intensities. A random forest (RF) classifier was exploited to detect these activities based on the wrist motions and optical HR. The highest overall accuracy of 89.6 ± 3.9% was achieved with a forest of a size of 64 trees and 13-s signal segments with 90% overlap. Removing the HR-derived features decreased the classification accuracy of high-intensity cycling by almost 7%, but did not affect the classification accuracies of other activities. A feature reduction utilizing the feature importance scores of RF was also carried out and resulted in a shrunken feature set of only 21 features. The overall accuracy of the classification utilizing the shrunken feature set was 89.4 ± 4.2%, which is almost equivalent to the above-mentioned peak overall accuracy.

Human Activity Recognition Using A Single Optical Heart Rate Monitoring Wristband Equipped with Triaxial Accelerometer

This paper investigates activity monitoring using a single wrist-worn optical heart rate monitoring sensor that is equipped with a triaxial accelerometer. Wearing accelerometers on the wrist provides more convenience and therefore improved wear-time compliance compared to other measurement sites. Reliability of wrist acceleration for activity monitoring has been addressed in former research. However, integration of wrist acceleration with physiological signals has not been comprehensively explored yet. We investigated a variety of home-specific activities (sitting, standing, household, and stationary cycling) performed by 20 male participants. Random Forest (RF) and Support Vector Machines (SVM) were applied for activity classification. Various features calculated from acceleration, heart rate (HR), and heart rate variability (HRV) were used as classified inputs. Results of leave-one-subject-out cross-validation showed \(89.2\%\) and \(85.6\%\) average recognition accuracies for RF and SVM, respectively. HR and HRV features improved the classification rates of high-intensity cycling by \(8\%\) for RF and \(7\%\) for SVM.

Acute Effect of Alcohol Intake on Cardiovascular Autonomic Regulation During the First Hours of Sleep in a Large Real-World Sample of Finnish Employees: Observational Study

Background Sleep is fundamental for good health, and poor sleep has been associated with negative health outcomes. Alcohol consumption is a universal health behavior associated with poor sleep. In controlled laboratory studies, alcohol intake has been shown to alter physiology and disturb sleep homeostasis and architecture. The association between acute alcohol intake and physiological changes has not yet been studied in noncontrolled real-world settings. Objective The aim of this study was to assess the effects of alcohol intake on the autonomic nervous system (ANS) during sleep in a large noncontrolled sample of Finnish employees. Methods From a larger cohort, this study included 4098 subjects (55.81%, 2287/4098 females; mean age 45.1 years) who had continuous beat-to-beat R-R interval recordings of good quality for at least 1 day with and for at least 1 day without alcohol intake. The participants underwent continuous beat-to-beat R-R interval recording during their normal everyday life and self-reported their alcohol intake as doses for each day. Heart rate (HR), HR variability (HRV), and HRV-derived indices of physiological state from the first 3 hours of sleep were used as outcomes. Within-subject analyses were conducted in a repeated measures manner by studying the differences in the outcomes between each participant’s days with and without alcohol intake. For repeated measures two-way analysis of variance, the participants were divided into three groups: low (≤0.25 g/kg), moderate (>0.25-0.75 g/kg), and high (>0.75 g/kg) intake of pure alcohol. Moreover, linear models studied the differences in outcomes with respect to the amount of alcohol intake and the participant’s background parameters (age; gender; body mass index, BMI; physical activity, PA; and baseline sleep HR). Results Alcohol intake was dose-dependently associated with increased sympathetic regulation, decreased parasympathetic regulation, and insufficient recovery. In addition to moderate and high alcohol doses, the intraindividual effects of alcohol intake on the ANS regulation were observed also with low alcohol intake (all P<.001). For example, HRV-derived physiological recovery state decreased on average by 9.3, 24.0, and 39.2 percentage units with low, moderate, and high alcohol intake, respectively. The effects of alcohol in suppressing recovery were similar for both genders and for physically active and sedentary subjects but stronger among young than older subjects and for participants with lower baseline sleep HR than with higher baseline sleep HR. Conclusions Alcohol intake disturbs cardiovascular relaxation during sleep in a dose-dependent manner in both genders. Regular PA or young age do not protect from these effects of alcohol. In health promotion, wearable HR monitoring and HRV-based analysis of recovery might be used to demonstrate the effects of alcohol on sleep on an individual level.

Työtapaturmaisten olka- ja polvivammojen hoitotoimenpiteet ja -kustannukset sekä hoidon ja sairauslomien kesto vakuutusyhtiön rekisteriaineistoon perustuen

Knee and shoulder injuries are typical for occupational accidents causing significant treatment expenses. This study employed an insurance company’s register considering treatments given for occupational accident injuries in private clinics in years 2010-2015. Treatments, treatment expenses, durations of treatments and sick-leaves were analyzed for 555 (men: 57 %, age: 40.9±11.8 yrs) knee injury diagnoses with ICD-10 code of class S83 and 377 (men: 56 %, age: 42.4±12.1 yrs) shoulder injury diagnoses with ICD-10 code of class S43 or S46. In surgical and rehabilitative treatments, the method of treatment and time-to-surgery were studied. Of the injuries, 66 % resulted from workplace and 33 % from commuting accidents. The injuries were mostly minor: 84 % needed only doctor appointments as treatment, and 54 % caused at maximum one-week sick-leave. Only 16 % of the injuries required rehabilitation and/or surgical treatment but they caused 61 % of all treatment expenses and 51 % caused over three-month sick-leave. In only 25 % and 33 % of knee and shoulder injuries, respectively, rehabilitation was given before surgical treatment. The times-to-surgeries followed the Finnish guidelines as the recommendation was more than two months. The time-to-surgery in 80 % of the rotator cuff injuries was at maximum four months but only in 5 % of the meniscus injuries was 1-2 weeks as recommended. Occupational accidents cause only a few serious knee and shoulder injuries but they cause high treatment expenses and long sick-leaves. To avoid extra expenses and days lost, right and early medical diagnosis and optimal treatments together with flexible and case-by-case discretion and practices from the insurance companies are needed.

Methods to Use Big Wearable Heart Rate Data for Estimation of Physical Activity in Population Level

Technologies for wearable health monitoring are becoming increasingly popular and affordable. As a result, large-scale health databases from a large number of individuals are becoming available. However, analysis of these databases requires special methodology to transform available parameters into more generic ones and to manage such non-balanced data characteristics as biases and sampling issues. In this paper, we introduce a methodology for studying physical activity from big wearable heart rate (HR) data on about 5 000 working-age individuals, each measured only for a few days. Physical activity was assessed by oxygen consumption (VO2) calculated from measured HR data using a neural network model. Minute-to-minute VO2 data was used to quantify various physical activities in a measurement day, as defined according to the health promoting physical activity minutes of the American College of Sports Medicine. We seta posteriori inclusion criteria for the data on the subjects’ personal background parameters and the quality of their HR data. The effect of different subjects being measured in different months and weekdays was removed by using a linear model. The linear model sought to estimate the physical activity minutes based on a subject’s background parameters. The results show that big data collected in real-life settings and originally for non-research purposes can with appropriate data management and analysis methodology provide unique knowledge of lifestyles and behavior.