An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices

Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire

Background

Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists.

Objective

We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders.

Methods

Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire.

Results

A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder.

Conclusions

Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

The Impact of Wearable Technologies in Health Research: Scoping Review

Background

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

Objective

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

Methods

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

Results

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

A Technology-Based Pregnancy Health and Wellness Intervention (Two Happy Hearts): Case Study

BACKGROUND

The physical and emotional well-being of women is critical for healthy pregnancy and birth outcomes. The Two Happy Hearts intervention is a personalized mind-body program coached by community health workers that includes monitoring and reflecting on personal health, as well as practicing stress management strategies such as mindful breathing and movement.

OBJECTIVE

The aims of this study are to (1) test the daily use of a wearable device to objectively measure physical and emotional well-being along with subjective assessments during pregnancy, and (2) explore the user's engagement with the Two Happy Hearts intervention prototype, as well as understand their experiences with various intervention components.

METHODS

A case study with a mixed design was used. We recruited a 29-year-old woman at 33 weeks of gestation with a singleton pregnancy. She had no medical complications or physical restrictions, and she was enrolled in the Medi-Cal public health insurance plan. The participant engaged in the Two Happy Hearts intervention prototype from her third trimester until delivery. The Oura smart ring was used to continuously monitor objective physical and emotional states, such as resting heart rate, resting heart rate variability, sleep, and physical activity. In addition, the participant self-reported her physical and emotional health using the Two Happy Hearts mobile app-based 24-hour recall surveys (sleep quality and level of physical activity) and ecological momentary assessment (positive and negative emotions), as well as the Perceived Stress Scale, Center for Epidemiologic Studies Depression Scale, and State-Trait Anxiety Inventory. Engagement with the Two Happy Hearts intervention was recorded via both the smart ring and phone app, and user experiences were collected via Research Electronic Data Capture satisfaction surveys. Objective data from the Oura ring and subjective data on physical and emotional health were described. Regression plots and Pearson correlations between the objective and subjective data were presented, and content analysis was performed for the qualitative data.

RESULTS

Decreased resting heart rate was significantly correlated with increased heart rate variability (r=-0.92, P<.001). We found significant associations between self-reported responses and Oura ring measures: (1) positive emotions and heart rate variability (r=0.54, P<.001), (2) sleep quality and sleep score (r=0.52, P<.001), and (3) physical activity and step count (r=0.77, P<.001). In addition, deep sleep appeared to increase as light and rapid eye movement sleep decreased. The psychological measures of stress, depression, and anxiety appeared to decrease from baseline to post intervention. Furthermore, the participant had a high completion rate of the components of the Two Happy Hearts intervention prototype and shared several positive experiences, such as an increased self-efficacy and a normal delivery.

CONCLUSIONS

The Two Happy Hearts intervention prototype shows promise for potential use by underserved pregnant women.

Feasibility, acceptability and validation of wearable devices for climate change and health research in the low-resource contexts of Burkina Faso and Kenya: Study protocol

As the epidemiological transition progresses throughout sub-Saharan Africa, life lived with diseases is an increasingly important part of a population's burden of disease. The burden of disease of climate-sensitive health outcomes is projected to increase considerably within the next decades. Objectively measured, reliable population health data is still limited and is primarily based on perceived illness from recall. Technological advances like non-invasive, consumer-grade wearable devices may play a vital role in alleviating this data gap and in obtaining insights on the disease burden in vulnerable populations, such as heat stress on human cardiovascular response. The overall goal of this study is to investigate whether consumer-grade wearable devices are an acceptable, feasible and valid means to generate data on the individual level in low-resource contexts. Three hundred individuals are recruited from the two study locations in the Nouna health and demographic surveillance system (HDSS), Burkina Faso, and the Siaya HDSS, Kenya. Participants complete a structured questionnaire that comprises question items on acceptability and feasibility under the supervision of trained data collectors. Validity will be evaluated by comparing consumer-grade wearable devices to research-grade devices. Furthermore, we will collect demographic data as well as the data generated by wearable devices. This study will provide insights into the usage of consumer-grade wearable devices to measure individual vital signs in low-resource contexts, such as Burkina Faso and Kenya. Vital signs comprising activity (steps), sleep (duration, quality) and heart rate (hr) are important measures to gain insights on individual behavior and activity patterns in low-resource contexts. These vital signs may be associated with weather variables-as we gather them from weather stations that we have setup as part of this study to cover the whole Nouna and Siaya HDSSs-in order to explore changes in behavior and other variables, such as activity, sleep, hr, during extreme weather events like heat stress exposure. Furthermore, wearable data could be linked to health outcomes and weather events. As a result, consumer-grade wearables may serve as a supporting technology for generating reliable measurements in low-resource contexts and investigating key links between weather occurrences and health outcomes. Thus, wearable devices may provide insights to better inform mitigation and adaptation interventions in these low-resource settings that are direly faced by climate change-induced changes, such as extreme weather events.

Recent advances in wearable sensors and portable electronics for sleep monitoring

Despite the increasing awareness of the importance of sleep, the number of people suffering from insufficient sleep has increased every year. The gold-standard sleep assessment uses polysomnography (PSG) with various sensors to identify sleep patterns and disorders. However, due to the high cost of PSG and limited availability, many people with sleep disorders are left undiagnosed. Recent wearable sensors and electronics enable portable, continuous monitoring of sleep at home, overcoming the limitations of PSG. This report reviews the advances in wearable sensors, miniaturized electronics, and system packaging for home sleep monitoring. New devices available in the market and systems are collectively summarized based on their overall structure, form factor, materials, and sleep assessment method. It is expected that this review provides a comprehensive view of newly developed technologies and broad insights on wearable sensors and portable electronics toward advanced sleep monitoring as well as at-home sleep assessment.

Gender differences in nighttime sleep patterns and variability across the adult lifespan: a global-scale wearables study

STUDY OBJECTIVES

Previous research on sleep patterns across the lifespan have largely been limited to self-report measures and constrained to certain geographic regions. Using a global sleep dataset of in situ observations from wearable activity trackers, we examine how sleep duration, timing, misalignment, and variability develop with age and vary by gender and BMI for nonshift workers.

METHODS

We analyze 11.14 million nights from 69,650 adult nonshift workers aged 19-67 from 47 countries. We use mixed effects models to examine age-related trends in naturalistic sleep patterns and assess gender and BMI differences in these trends while controlling for user and country-level variation.

RESULTS

Our results confirm that sleep duration decreases, the prevalence of nighttime awakenings increases, while sleep onset and offset advance to become earlier with age. Although men tend to sleep less than women across the lifespan, nighttime awakenings are more prevalent for women, with the greatest disparity found from early to middle adulthood, a life stage associated with child-rearing. Sleep onset and duration variability are nearly fixed across the lifespan with higher values on weekends than weekdays. Sleep offset variability declines relatively rapidly through early adulthood until age 35-39, then plateaus on weekdays, but continues to decrease on weekends. The weekend-weekday contrast in sleep patterns changes as people age with small to negligible differences between genders.

CONCLUSIONS

A massive dataset generated by pervasive consumer wearable devices confirms age-related changes in sleep and affirms that there are both persistent and life-stage dependent differences in sleep patterns between genders.

A multitrait, multimethod matrix approach for a consumer-grade wrist-worn watch measuring sleep duration and continuity

Study Objectives

We examined associations between self-reports about typical sleep patterns and sleep data derived from a wearable device worn on a nightly basis for a prolonged period (mean = 214 nights). We hypothesized that sleep characteristics would correlate better across different methods of assessment (self-report versus wearable) than they would correlate within the same method, a classic psychometric approach (multitrait, multimethod matrix).

Methods

A cross-national sample of 6,230 adult wearable users completed a brief sleep questionnaire collecting data on sleep duration and number of awakenings (NAW) and provided informed consent to link their responses to data from their wearable watches. The data collection for the wearable occurred over 12 months and the sleep questionnaire was completed subsequent to that.

Results

Results indicated a large (r = .615) correlation between sleep duration as assessed with the wearable and by self-report. A medium-to-large correlation (r = .406) was also seen for NAW. The multitrait, multimethod matrix suggested minimal method variance, i.e. similar “traits” (sleep duration and NAW) correlated across methods but within a given method, and such “traits” were generally unrelated.

Conclusions

The results suggest that the longer period of data collection with the wearable generates more stable estimates of sleep than have been reported in most studies of actigraphy. Alternatively, the data might imply that individuals modify their self-reports about sleep via daily feedback to align their perceptions to the output of the wearable.

Opportunities and Challenges Surrounding the Use of Data From Wearable Sensor Devices in Health Care: Qualitative Interview Study

BACKGROUND

Wearable sensors connected via networked devices have the potential to generate data that may help to automate processes of care, engage patients, and increase health care efficiency. The evidence of effectiveness of such technologies is, however, nascent and little is known about unintended consequences.

OBJECTIVE

Our objective was to explore the opportunities and challenges surrounding the use of data from wearable sensor devices in health care.

METHODS

We conducted a qualitative, theoretically informed, interview-based study to purposefully sample international experts in health care, technology, business, innovation, and social sciences, drawing on sociotechnical systems theory. We used in-depth interviews to capture perspectives on development, design, and use of data from wearable sensor devices in health care, and employed thematic analysis of interview transcripts with NVivo to facilitate coding.

RESULTS

We interviewed 16 experts. Although the use of data from wearable sensor devices in health and care has significant potential in improving patient engagement, there are a number of issues that stakeholders need to negotiate to realize these benefits. These issues include the current gap between data created and meaningful interpretation in health and care contexts, integration of data into health care professional decision making, negotiation of blurring lines between consumer and medical care, and pervasive monitoring of health across previously disconnected contexts.

CONCLUSIONS

Stakeholders need to actively negotiate existing challenges to realize the integration of data from wearable sensor devices into electronic health records. Viewing wearables as active parts of a connected digital health and care infrastructure, in which various business, personal, professional, and health system interests align, may help to achieve this.

Monitoring behavioral symptoms of dementia using activity trackers

Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) and their caregivers is hampered by the presence of behavioral and psychological symptoms of dementia (BPSD), such as anxiety and depression. Non-pharmacological interventions have proved useful in dealing with these symptoms. However, while most PwD exhibit BPSD, their manifestation (in frequency, intensity and type) varies widely among patients, thus the need to personalize the intervention and its assessment. Traditionally, instruments to measure behavioral symptoms of dementia, such as NPI-NH and CMAI, are used to evaluate these interventions. We propose the use of activity trackers as a complement to monitor behavioral symptoms in dementia research. To illustrate this approach we describe a nine week Cognitive Stimulation Therapy conducted with the assistance of a social robot, in which the ten participants wore an activity tracker. We describe how data gathered from these wearables complements the assessment of traditional behavior assessment instruments with the advantage that this assessment can be conducted continuously and thus be used to tailor the intervention to each PwD.

Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review

BACKGROUND

Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health.

OBJECTIVE

The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic.

METHODS

We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults.

RESULTS

The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography.

CONCLUSIONS

This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring.

Internet of things in medicine: A systematic mapping study

CONTEXT

The current studies on IoT in healthcare have reviewed the uses of this technology in a combination of healthcare domains, including nursing, rehabilitation sciences, ambient assisted living (AAL), medicine, etc. However, no review study has scrutinized IoT advances exclusively in medicine irrespective of other healthcare domains.

OBJECTIVES

The purpose of the current study was to identify and map the current IoT developments in medicine through providing graphical/tabular classifications on the current experimental and practical IoT information in medicine, the involved medical sub-fields, the locations of IoT use in medicine, and the bibliometric information about IoT research articles.

METHODS

In this systematic mapping study, the studies published between 2000 and 2018 in major online scientific databases, including IEEE Xplore, Web of Science, Scopus, and PubMed were screened. A total of 3679 papers were found from which 89 papers were finally selected based on specific inclusion/exclusion criteria.

RESULTS

While the majority of medical IoT studies were experimental and prototyping in nature, they generally reported that home was the most popular place for medical IoT applications. It was also found that neurology, cardiology, and psychiatry/psychology were the medical sub-fields receiving the most IoT attention. Bibliometric analysis showed that IEEE Internet of Things Journal has published the most influential IoT articles. India, China and the United States were found to be the most involved countries in medical IoT research.

CONCLUSIONS

Although IoT has not yet been employed in some medical sub-fields, recent substantial surge in the number of medical IoT studies will most likely lead to the engagement of more medical sub-fields in the years to come. IoT literature also shows that the ambiguity of assigning a variety of terms to IoT, namely system, platform, device, tool, etc., and the interchangeable uses of these terms require a taxonomy study to investigate the precise definition of these terms. Other areas of research have also been mentioned at the end of this article.

Environmental open-source data sets and sleep-wake rhythms of populations: an overview

OBJECTIVE/BACKGROUND

In recent decades, the epidemiology of sleep disorders has mainly consisted of interviewing subjects through validated questionnaires; more recently, this has been done by assessing total sleep time (TST) per 24 h via sleep logs or connected devices. Thus, a vast amount of data has helped demonstrate the decline of TST in most countries. Nonetheless, we believe from a societal and environmental point of view that sleep researchers have largely overlooked a wide-open field of data that may help us to better understand and describe global sleep wake rhythms (SWR), eg, data regarding the sleep environment.

METHODS

Based on recent literature, we identified several environmental and societal fields that may have an effect on SWR. With the help of an expert panel, we selected the five most pertinent fields with multiple open-source data sets that may have an impact on human SWR. Then, we performed web-based research and proposed open-field data sets for each field, all of which are open to researchers and possibly scientifically associated with SWR.

RESULTS

The open fields relevant to the environment that we selected were noise, light pollution, and radio frequencies. The two societal fields were transportation and internet use. The evolution of most of these fields in recent decades may explain (even partially) the decline in TST. Importantly, the open data sets in each field are widely available to sleep researchers.

CONCLUSIONS

SWR must be assessed not only by patient accounts, but also in terms of the evolution of environmental cues.

[What prospects for the sleep apnea syndrome and connected health?]

Connected health is a growing field and can be viewed from different perspectives, particularly in sleep apnea syndrome. The purpose of this review is to show how all these aspects of connected health are already used in the management of sleep apnea syndrome (SAS) and its comorbidities. First, it can give patients a better understanding and a better assessment of their health. It also facilitates their healthcare by allowing them a greater role in their care pathway. For healthcare providers, connected health tools make it possible to set up new procedures for diagnosing and monitoring ambulatory patients, and for the making of joint decisions by health professionals and patients. Finally, for researchers, e-health generates massive amounts of data, thus facilitating the acquisition of knowledge in real life situations and the development of new methodologies for clinical studies that are faster, less expensive and just as reliable. All these considerations are already applicable in the field of sleep apnea, both for proposed treatments and for comorbidities management and for the patient's involvement in his/her care pathway.

Sleep tracking: A systematic review of the research using commercially available technology

PURPOSE OF REVIEW

To systematically review the available research studies that characterize the benefits, uncertainty, or weaknesses of commercially-available sleep tracking technology.

RECENT FINDINGS

Sleep is a vital component of health and well-being. Research shows that tracking sleep using commercially available sleep tracking technology (e.g., wearable or smartphone-based) is increasingly popular in the general population.

METHODS

Systematic literature searches were conducted using PubMed/Medline, Embase (Ovid) the Cochrane Library, PsycINFO (Ovid), CINAHL, and Web of Science Plus (which included results from Biosis Citation Index, INSPEC, and Food, Science & Technology Abstracts) (n=842).

STUDY INCLUSION AND EXCLUSION CRITERIA

Three independent reviewers reviewed eligible articles that administered a commercially-available sleep tracker to participants and reported on sleep parameters as captured by the tracker, including either sleep duration or quality. Eligible articles had to include sleep data from users for >=4 nights.

Social Jetlag and Chronotypes in the Chinese Population: Analysis of Data Recorded by Wearable Devices

Background

Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China.

Objective

We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices.

Methods

We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc.

Results

The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively).

Conclusions

Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL.

Exploring Associations Between the Self-Reported Values, Well-Being, and Health Behaviors of Finnish Citizens: Cross-Sectional Analysis of More Than 100,000 Web-Survey Responses

BACKGROUND

Understanding the relationship between personal values, well-being, and health-related behavior could facilitate the development of engaging, effective digital interventions for promoting well-being and the healthy lifestyles of citizens. Although the associations between well-being and values have been quite extensively studied, the knowledge about the relationship between health behaviors and values is less comprehensive.

OBJECTIVE

The aim of this study was to assess retrospectively the associations between self-reported values and commitment to values combined with self-reported well-being and health behaviors from a large cross-sectional dataset.

METHODS

We analyzed 101,130 anonymous responses (mean age 44.78 years [SD 13.82]; 78.88%, 79,770/101,130 women) to a Finnish Web survey, which were collected as part of a national health promotion campaign. The data regarding personal values were unstructured, and the self-reported value items were classified into value types based on the Schwartz value theory and by applying principal component analysis. Logistic and multiple linear regression were used to explore the associations of value types and commitment to values with well-being factors (happiness, communal social activity, work, and family-related distress) and health behaviors (exercise, eating, smoking, alcohol consumption, and sleep).

RESULTS

Commitment to personal values was positively related to happiness (part r²=0.28), communal social activity (part r²=0.09), and regular exercise (part r²=0.06; P<.001 for all). Health, Power (social status and dominance), and Mental balance (self-acceptance) values had the most extensive associations with health behaviors. Regular exercise, healthy eating, and nonsmoking increased the odds of valuing Health by 71.7%, 26.8%, and 40.0%, respectively (P<.001 for all). Smoking, unhealthy eating, irregular exercise, and increased alcohol consumption increased the odds of reporting Power values by 27.80%, 27.78%, 24.66%, and 17.35%, respectively (P<.001 for all). Smoking, unhealthy eating, and irregular exercise increased the odds of reporting Mental balance values by 20.79%, 16.67%, and 15.37%, respectively (P<.001 for all). In addition, lower happiness levels increased the odds of reporting Mental balance and Power values by 24.12% and 20.69%, respectively (P<.001 for all).

CONCLUSIONS

The findings suggest that commitment to values is positively associated with happiness and highlight various, also previously unexplored, associations between values and health behaviors.

Off-Training Levels of Physical Activity and Sedentary Behavior in Young Athletes: Preliminary Results during a Typical Week

The level of physical activity (PA) and sedentary behavior (SED) off-training of young athletes may reveal the quality of recovery from training and highlight health related issues. Thus, the aim was to identify and describe young athletes' PA and SED off-training, according to daily life activities. Eight athletes (15.7 ± 2 years, 1.72 ± 0.6 m height, 62.9 ± 10.2 kg) of a sport talent program wore on their waist a tri-axial accelerometer (ActiGraph^® wGT9X-link, Shalimar, FL, USA) at 30 Hz for 15 consecutive days, and reported their schedule. A two-step cluster analysis classified three groups according to sedentary PA and MVPA. The Sedentary (56.9%), presented the highest sedentary PA (mean [CI], 37.37 [36.45⁻38.29] min/hour); The Hazardous (19.4%) had the lowest values of sedentary and MVPA (10.07 [9.41⁻10.36] min/hour and 8.67 [7.64⁻9.70] min/hour, respectively). Balanced (23.7%) had the highest MVPA (28.61 [27.16⁻30.07] min/hour). Sedentary had the lowest count of home time associated (20%) and higher school (26%) time when compared to the Hazardous (13%). The Balanced showed the highest count of school (61%) and home time (47%). Different profiles for young athletes revealed alarming behavior in the associations with sedentary PA, sitting and SED breaks, which may influence performance and health.