Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study

Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study

Accelerometers are frequently used to assess physical activity in large epidemiological studies. They can monitor movement patterns and cycles over several days under free-living conditions and are usually either worn on the wrist or the hip. While wrist-worn accelerometers have been frequently used to additionally assess sleep and time in bed behavior, hip-worn accelerometers have been widely neglected for this task due to their primary focus on physical activity. Here, we present a new method with the objective to identify the time in bed to enable further analysis options for large-scale studies using hip-placement like time in bed or sedentary time analyses. We introduced new and accelerometer-specific data augmentation methods, such as mimicking a wrongly worn accelerometer, additional noise, and random croping, to improve training and generalization performance. Subsequently, we trained a neural network model on a sample from the population-based Tromsø Study and evaluated it on two additional datasets. Our algorithm achieved an accuracy of 94% on the training data, 92% on unseen data from the same population and comparable results to consumer-wearable data obtained from a demographically different population. Generalization performance was overall good, however, we found that on a few particular days or participants, the trained model fundamentally over- or underestimated time in bed (e.g., predicted all or nothing as time in bed). Despite these limitations, we anticipate our approach to be a starting point for more sophisticated methods to identify time in bed or at some point even sleep from hip-worn acceleration signals. This can enable the re-use of already collected data, for example, for longitudinal analyses where sleep-related research questions only recently got into focus or sedentary time needs to be estimated in 24 h wear protocols.

The future of healthy ageing: Wearables in public health, disease prevention and healthcare

Wearables have evolved into accessible tools for sports, research, and interventions. Their use has expanded to real-time monitoring of behavioural parameters related to ageing and health. This paper provides an overview of the literature on wearables in disease prevention and healthcare over the life course (not only in the older population), based on insights from the Future of Diagnostics Workshop (Leiden, January 2024). Wearable-generated parameters include blood glucose, heart rate, step count, energy expenditure, and oxygen saturation. Integrating wearables in healthcare is protracted and far from mainstream implementation, but promises better diagnosis, biomonitoring, and assessment of medical interventions. The main lifestyle factors monitored directly with wearables or through smartphone applications for disease prevention include physical activity, energy expenditure, gait, sleep, and sedentary behaviour. Insights on dietary consumption and nutrition have resulted from continuous glucose monitors. These factors are important for healthy ageing due to their effect on underlying disease pathways. Inclusivity and engagement, data quality and ease of interpretation, privacy and ethics, user autonomy in decision making, and efficacy present challenges to but also opportunities for their use, especially by older people. These need to be addressed before wearables can be integrated into mainstream medical and public health strategies. Furthermore, six key considerations need to be tackled: 1) engagement, health literacy, and compliance with personalised feedback, 2) technical and standardisation requirements for scalability, 3) accountability, data safety/security, and ethical concerns, 4) technological considerations, access, and capacity building, 5) clinical relevance and risk of overdiagnosis/overmedicalisation, and 6) the clinician's perspective in implementation.

Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints

Sleep trackers are used widely by patients with sleep complaints, however their metrological validation is often poor and relies on healthy subjects. We assessed the metrological validity of two commercially available sleep trackers (Withings Activité/Fitbit Alta HR) through a prospective observational monocentric study, in adult patients referred for polysomnography (PSG). We compared the total sleep time (TST), REM time, REM latency, nonREM1 + 2 time, nonREM3 time, and wake after sleep onset (WASO). We report absolute and relative errors, Bland-Altman representations, and a contingency table of times spent in sleep stages with respect to PSG. Sixty-five patients were included (final sample size 58 for Withings and 52 for Fitbit). Both devices gave a relatively accurate sleep start time with a median absolute error of 5 (IQR -43; 27) min for Withings and -2.0 (-12.5; 4.2) min for Fitbit but both overestimated TST. Withings tended to underestimate WASO with a median absolute error of -25.0 (-61.5; -8.5) min, while Fitbit tended to overestimate it (median absolute error 10 (-18; 43) min. Withings underestimated light sleep and overestimated deep sleep, while Fitbit overestimated light and REM sleep and underestimated deep sleep. The overall kappas for concordance of each epoch between PSG and devices were low: 0.12 (95%CI 0.117-0.121) for Withings and VPSG indications 0.07 (95%CI 0.067-0.071) for Fitbit, as well as kappas for each VPSG indication 0.07 (95%CI 0.067-0.071). Thus, commercially available sleep trackers are not reliable for sleep architecture in patients with sleep complaints/pathologies and should not replace actigraphy and/or PSG.

Wearable-derived short sleep duration is associated with higher C-reactive protein in a placebo-controlled vaccine trial among young adults

Inadequate sleep has been associated with an increased risk of mortality and various health issues. We previously conducted a placebo-controlled vaccination trial of healthy adults who were monitored by blood samples, questionnaires, and wearable devices. C-reactive protein (CRP), a systemic marker of inflammation, has been linked to numerous health outcomes, and was found to significantly increase post-vaccination in the trial. In this retrospective study, we investigated that if sleep was associated with an inflammation response triggered by perturbations from vaccine and placebo injections. Plasma hs-CRP levels were measured on the same day as the intervention, prior to the vaccine/placebo administration and two days after the intervention. Associations of sleep duration and CRP levels after vaccine/placebo administration in 188 trial participants were investigated by regression models adjusting for age, sex, body mass index (BMI), comorbidities, vaccination status (vaccination or placebo), and averaged daily steps. We found that shorter wearable-derived Total Sleep Time (TST) and Total Time in Bed (TIB), as well as subjectively assessed sleep duration from the Pittsburgh Sleep Quality Index (PSQI), were independently associated with higher incidence of CRP elevation after vaccine/placebo administration. Our study suggests that sleep deprivation could be a predictor for an increased inflammatory response and highlights a potential application of wearable-derived sleep metrics in public health.

Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring

Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura's PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min.

The behavioral and physiological correlates of affective mood switching in premenstrual dysphoric disorder

Premenstrual dysphoric disorder (PMDD), a more severe manifestation of premenstrual syndrome (PMS), is characterized by emotional, behavioral, and physical symptoms that begin in the mid-to-late luteal phase of the menstrual cycle, when estradiol and progesterone levels precipitously decline, and remit after the onset of menses. Remotely monitoring physiologic variables associated with PMDD depression symptoms, such as heart rate variability (HRV), sleep, and physical activity, holds promise for developing an affective state prediction model. Switching into and out of depressive states is associated with an increased risk of suicide, and therefore, monitoring periods of affective switching may help mitigate risk. Management of other chronic health conditions, including cardiovascular disease and diabetes, has benefited from remote digital monitoring paradigms that enable patients and physicians to monitor symptoms in real-time and make behavioral and medication adjustments. PMDD is a chronic condition that may benefit from real-time, remote monitoring. However, clinical practice has not advanced to monitoring affective states in real-time. Identifying remote monitoring paradigms that can detect within-person affective state change may help facilitate later research on timely and efficacious interventions for individuals with PMDD. This narrative review synthesizes the current literature on behavioral and physiological correlates of PMDD suitable for remote monitoring during the menstrual cycle. The reliable measurement of heart rate variability (HRV), sleep, and physical activity, with existing wearable technology, suggests the potential of a remote monitoring paradigm in PMDD and other depressive disorders.

Menstrual Cycle Variations in Wearable-Detected Finger Temperature and Heart Rate, But Not in Sleep Metrics, in Young and Midlife Individuals

Most studies about the menstrual cycle are laboratory-based, in small samples, with infrequent sampling, and limited to young individuals. Here, we use wearable and diary-based data to investigate menstrual phase and age effects on finger temperature, sleep, heart rate (HR), physical activity, physical symptoms, and mood. A total of 116 healthy females, without menstrual disorders, were enrolled: 67 young (18-35 years, reproductive stage) and 53 midlife (42-55 years, late reproductive to menopause transition). Over one menstrual cycle, participants wore Oura ring Gen2 to detect finger temperature, HR, heart rate variability (root mean square of successive differences between normal heartbeats [RMSSD]), steps, and sleep. They used luteinizing hormone (LH) kits and daily rated sleep, mood, and physical symptoms. A cosinor rhythm analysis was applied to detect menstrual oscillations in temperature. The effect of menstrual cycle phase and group on all other variables was assessed using hierarchical linear models. Finger temperature followed an oscillatory trend indicative of ovulatory cycles in 96 participants. In the midlife group, the temperature rhythm's mesor was higher, but period, amplitude, and number of days between menses and acrophase were similar in both groups. In those with oscillatory temperatures, HR was lowest during menses in both groups. In the young group only, RMSSD was lower in the late-luteal phase than during menses. Overall, RMSSD was lower, and number of daily steps was higher, in the midlife group. No significant menstrual cycle changes were detected in wearable-derived or self-reported measures of sleep efficiency, duration, wake-after-sleep onset, sleep onset latency, or sleep quality. Mood positivity was higher around ovulation, and physical symptoms manifested during menses. Temperature and HR changed across the menstrual cycle; however, sleep measures remained stable in these healthy young and midlife individuals. Further work should investigate over longer periods whether individual- or cluster-specific sleep changes exist, and if a buffering mechanism protects sleep from physiological changes across the menstrual cycle.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study

BACKGROUND

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

OBJECTIVE

We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

METHODS

Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

RESULTS

All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

CONCLUSIONS

Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.3390/clockssleep6010010.

The activity advantage: Objective measurement of preoperative activity is associated with postoperative recovery and outcomes in patients undergoing surgery with gynecologic oncologists

OBJECTIVE

To examine the association between objectively-measured preoperative physical activity with postoperative outcomes and recovery milestones in patients undergoing gynecologic oncology surgeries.

METHODS

Prospective cohort study of patients undergoing surgery with gynecologic oncologists who wore wearable actigraphy rings before and after surgery from 03/2021-11/2023. Exposures encompassed preoperative activity intensity (moderate- and vigorous-intensity metabolic equivalent of task-minutes [MAVI MET-mins] over seven days) and level (average daily steps over seven days). Intensity was categorized as <500, 500-1000, and >1000 MAVI MET-mins; level categorized as <8000 and ≥8000 steps/day. Primary outcome was 30-day complications. Secondary outcomes included reaching postoperative goal (≥70% of recommended preoperative intensity and level thresholds) and return to baseline (≥70% of individual preoperative intensity and level).

RESULTS

Among 96 enrolled, 87 met inclusion criteria, which constituted 39% (n = 34) with <500 MET-mins and 56.3% (n = 49) with <8000 steps preoperatively. Those with <500 MET-mins and <8000 steps had higher ECOG scores (p = 0.042 & 0.037) and BMI (p = 0.049 & 0.002) vs those with higher activity; all other perioperative characteristics were similar between groups. Overall, 29.9% experienced a 30-day complication, 29.9% reached postoperative goal, and 64.4% returned to baseline. On multivariable models, higher activity was associated with lower odds of complications: 500-1000 MET-mins (OR = 0.26,95%CI = 0.07-0.92) and >1000 MET-mins (OR = 0.25,95%CI = 0.07-0.94) vs <500 MET-mins; ≥8000 steps (OR = 0.25,95%CI = 0.08-0.73) vs <8000 steps. Higher preoperative activity was associated fewer days to reach postoperative goal.

CONCLUSION

Patients with high preoperative activity are associated with fewer postoperative complications and faster attainment of recovery milestones. Physical activity may be considered a modifiable risk factor for adverse postoperative outcomes.

United States Marine Corps Recruit Training Demands Associated With Performance Outcomes

INTRODUCTION

United States Marine Corps' (USMC) recruit training is a 13-week program designed to maximize physical and mental performance adaptations. The purpose of this study was to evaluate the training demands and characteristics that are associated with performance outcomes during USMC recruit training.

MATERIALS AND METHODS

A total of 196 recruits (M = 97 and W = 99) were monitored and tested throughout training. Laboratory-based performance testing occurred at the start of weeks 2 and 11 and consisted of body mass assessments, countermovement vertical jump, and isometric mid-thigh pull. Military-specific performance testing occurred twice within the first 8 weeks of training and included the physical fitness test (PFT) and combat fitness test (CFT) implemented by the USMC. Resilience data were collected at week 2 using the Connor-Davidson Resilience Scale. Workload, sleep, and stress responses were monitored at weeks 2, 7, and 11. Recruits were provided with a wearable tracking device which utilized heart rate and accelerometry-based technology to determine energy expenditure (EE), distances (DIS), and sleep metrics. Data were averaged over a 3-day period. Salivary cortisol testing occurred at the start of each monitoring week. Change scores were calculated for performance tests, and body mass was calculated from data obtained at week 2 to week 11. Area under the curve was calculated for the workload, sleep metrics, and cortisol responses using the trapezoidal method. Pearson product-moment correlations (r) were used to assess the relationships between training demands and performance. An α level of 0.05 was used to establish significance.

RESULTS

A moderate positive correlation was found between changes in body mass and peak power (P < .001; r = 0.43). Weak positive correlations were found between changes in body mass and peak force (P = .002; r = 0.28), as well as body mass and resilience (P = .03; r = 0.19). A moderate negative correlation was observed between changes in body mass and PFT (P < .001; r = -0.49). A weak negative correlation was found between changes in body mass and EE (P = .003; r = -0.24). A weak negative correlation was found between changes in peak power and EE (P = .001; r = -0.29). A weak positive correlation was found between changes in peak power and changes in CFT (P = .05; r = 0.19) A weak negative correlation was found between changes in sleep continuity and CFT (P = .02; r = -0.20). A weak negative correlation was found between cortisol and changes in PFT (P = .05; r = -0.20). A weak negative correlation was found between cortisol and both EE (P = .001; r = -0.27) and DIS (P = .045; r = -0.16). A weak negative correlation was found between EE and sleep continuity (P < .001; r = -0.34). Weak negative correlations were found between sleep duration and both DIS (P = .01; r = -0.18) and steps (P = .003; r = -0.21).

CONCLUSIONS

Increases in body mass throughout training were positively associated with strength and power changes, but negatively related to PFT scores. Changes in peak power related to improvements in CFT scores; however, higher workloads (i.e., EE) were negatively associated with peak power. The identification of the USMC physical and physiological training demands that are associated with performance outcomes may be a valuable resource to guide conditioning efforts to boost military readiness.

TWINGEN: protocol for an observational clinical biobank recall and biomarker cohort study to identify Finnish individuals with high risk of Alzheimer's disease

INTRODUCTION

A better understanding of the earliest stages of Alzheimer's disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among THL Biobank donors according to TWINGEN study criteria.

METHODS AND ANALYSIS

A multi-centre study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behaviour and sleep. A subcohort is being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. The data collected in TWINGEN will be returned to THL Biobank from where it can later be requested for other biobank studies such as FinnGen that supported TWINGEN.

ETHICS AND DISSEMINATION

This recall study consists of FTC/THL Biobank/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Wearable Activity Trackers That Motivate Women to Increase Physical Activity: Mixed Methods Study

BACKGROUND

Physical inactivity is a significant public health concern, particularly among women in the United States. Wearable activity trackers (WATs) have been proposed as a potential solution to increase awareness of and engagement in physical activity (PA). However, to be effective, WATs must include features and designs that encourage daily use.

OBJECTIVE

This study aims to explore the features and designs of WATs that appeal to women and determine whether devices with these attributes are effective motivators for women to be physically active.

METHODS

A mixed methods study guided by the self-determination theory was conducted among 15 women. Participants trialed 3 WATs with influence in their respective accessory domains: Apple Watch for the wrist; Oura Ring for the finger; and Bellabeat Leaf Urban for multiple sites (it can be worn as a bracelet, necklace, or clip). Participants documented their daily PA levels and rated their satisfaction with each device's comfort, features, and motivational effect. Focus groups were also conducted to gather additional feedback and experiences within the a priori areas of comfort, features, and motivation.

RESULTS

Behavioral Regulation in Exercise Questionnaire-2 scores indicated that most participants (14/15, 93%) were motivated at baseline (amotivation score: mean 0.13, SD 0.45), but on average, participants did not meet the national minimum PA guidelines according to the self-reported Physical Activity Vital Sign questionnaire (moderate to vigorous PA score: mean 144, SD 97.5 min/wk). Mean WAT wear time was 16.9 (SD 4.4) hours, 19.4 (SD 5.3) hours, and 20.4 (SD 4.7) hours for Apple Watch, Bellabeat Leaf Urban, and Oura Ring, respectively. During focus groups, participants reinforced their quantitative ratings and rankings of the WATs based on personal experiences. Participants shared a variety of both activity-related and non-activity-related features that they look for in a motivating device. When considering what the ideal WAT would be for a woman, participants suggested features of (1) comfort, (2) extended battery life, (3) durability, (4) immediate PA feedback, (5) intuitive PA sensing, and (6) programmability.

CONCLUSIONS

This study is the first to specifically address women's experiences with and preferences for different types of WATs. Those who work with women should realize how they view WATs and the role they play in motivation to be active.

Clinical utility of smartphone applications for sleep physicians

PURPOSE

To review various smartphone applications (apps) for sleep architecture and screening of obstructive sleep apnea (OSA) and to outline their utility for sleep physicians.

METHODS

Mobile application stores (Google Play and Apple iOS App Store) were searched for sleep analysis applications (apps) that are targeted for consumer use. Apps were identified by two independent investigators for apps published through July 2022. App information including parameters obtained for sleep analysis were extracted from each app.

RESULTS

The search identified 50 apps that reported sufficient outcome measures to be considered for assessment. Half of the apps tracked sleep with phone-only technology, while 19 utilized sleep and fitness trackers, three utilized sleep-only wearable devices, and three utilized nearable devices. Seven apps provided data useful for tracking users for signs and symptoms of obstructive sleep apnea.

CONCLUSION

There are a variety of sleep analysis apps available on the market to consumers currently. Though the sleep analysis of these apps may not be validated, sleep physicians should be aware of these apps to improve understanding and education of their patients.

TWINGEN - protocol for an observational clinical biobank recall and biomarker study to identify individuals with high risk of Alzheimer's disease

Introduction

A better understanding of the earliest stages of Alzheimer's disease (AD) could expedite the development or administration of treatments. Large population biobanks hold the promise to identify individuals at an elevated risk of AD and related dementias based on health registry information. Here, we establish the protocol for an observational clinical recall and biomarker study called TWINGEN with the aim to identify individuals at high risk of AD by assessing cognition, health and AD-related biomarkers. Suitable candidates were identified and invited to participate in the new study among Finnish biobank donors according to TWINGEN study criteria.

Methods and analysis

A multi-center study (n=800) to obtain blood-based biomarkers, telephone-administered and web-based memory and cognitive parameters, questionnaire information on lifestyle, health and psychological factors, and accelerometer data for measures of physical activity, sedentary behavior and sleep. A sub-cohort are being asked to participate in an in-person neuropsychological assessment (n=200) and wear an Oura ring (n=50). All participants in the TWINGEN study have genome-wide genotyping data and up to 48 years of follow-up data from the population-based older Finnish Twin Cohort (FTC) study of the University of Helsinki. TWINGEN data will be transferred to Finnish Institute of Health and Welfare (THL) biobank and we aim to further to transfer it to the FinnGen study where it will be combined with health registry data for prediction of AD.

Ethics and dissemination

This recall study consists of FTC/THL/FinnGen participants whose data were acquired in accordance with the Finnish Biobank Act. The recruitment protocols followed the biobank protocols approved by Finnish Medicines Agency. The TWINGEN study plan was approved by the Ethics Committee of Hospital District of Helsinki and Uusimaa (number 16831/2022). THL Biobank approved the research plan with the permission no: THLBB2022_83.

Overview of the impact of sleep monitoring on optimal performance, immune system function and injury risk reduction in athletes: A narrative review

Sleep is essential for a range of physiological and mental functions in professional athletes. There is proof that athletes may experience lower quality and quantity of sleep. While adequate sleep has been shown to have restorative effects on the immune system and endocrine system, facilitate nervous system recovery and the metabolic cost of wakefulness, and play a significant role in learning, memory, and synaptic plasticity, which can affect sports recovery, injury risk reduction, and performance. Sports performance may suffer significantly from a lack of sleep, especially under maximal and long-term exercise. Due to the potential harm, these factors may do to an athlete's endocrine, metabolic, and nutritional health, sports performance is impacted by reduced sleep quality or quantity. There are several neurotransmitters associated with the sleep-wake cycle that have been discovered. They comprise cholinergic hormone, orexin, melanin, galanin, serotonin, gamma-aminobutyric acid, histamine, and serotonin. Therefore, dietary modifications that affect the neurotransmitters in the brain also may affect sleep; particularly for athletes who require more physical and psychological recovery owing to the tremendous physiological and psychological demands placed on them during training and performance. This review explores the variables that influence the quantity and quality of sleep-in populations of athletes and assesses their possible effects. In addition, several recommendations for improving sleep are presented. Even though there has been much research on variables that impact sleep, future studies may highlight the significance of these aspects for athletes.

Validation of Oura ring energy expenditure and steps in laboratory and free-living

BACKGROUND

Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living.

METHODS

Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count.

RESULTS

Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits.

CONCLUSION

This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study

The effects of stress may be alleviated when its impact or a decreased stress-resilience are detected early. This study explores whether wearable-measured sleep and resting HRV in police officers can be predicted by stress-related Ecological Momentary Assessment (EMA) measures in preceding days and predict stress-related EMA outcomes in subsequent days. Eight police officers used an Oura ring to collect daily Total Sleep Time (TST) and resting Heart Rate Variability (HRV) and an EMA app for measuring demands, stress, mental exhaustion, and vigor during 15-55 weeks. Vector Autoregression (VAR) models were created and complemented by Granger causation tests and Impulse Response Function visualizations. Demands negatively predicted TST and HRV in one participant. TST negatively predicted demands, stress, and mental exhaustion in two, three, and five participants, respectively, and positively predicted vigor in five participants. HRV negatively predicted demands in two participants, and stress and mental exhaustion in one participant. Changes in HRV lasted longer than those in TST. Bidirectional associations of TST and resting HRV with stress-related outcomes were observed at a weak-to-moderate strength, but not consistently across participants. TST and resting HRV are more consistent predictors of stress-resilience in upcoming days than indicators of stress-related measures in prior days.

Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.

Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia

INTRODUCTION

Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined.

AIMS

To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities.

METHODS

Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots.

RESULTS

Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA.

CONCLUSION

The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study

BACKGROUND

Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson's disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD.

METHODS

ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques.

DISCUSSION

We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.