Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial.

BACKGROUND: Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes. OBJECTIVE: This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel. STUDY DESIGN: Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia. RESULTS: In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications. CONCLUSION: Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.

The 2023 wearable photoplethysmography roadmap.

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study).

INTRODUCTION: Gestational diabetes (GDM) causes various adverse short-term and long-term consequences for the mother and child, and its incidence is increasing globally. So far, the most promising digital health interventions for GDM management have involved healthcare professionals to provide guidance and feedback. The principal aim of this study is to evaluate the effects of comprehensive and real-time self-tracking with eMOM GDM mobile application (app) on glucose levels in women with GDM, and more broadly, on different other maternal and neonatal outcomes. METHODS AND ANALYSIS: This randomised controlled trial is carried out in Helsinki metropolitan area. We randomise 200 pregnant women with GDM into the intervention and the control group at gestational week (GW) 24-28 (baseline, BL). The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet for 1 week every month until delivery and an electronic 3-day food record every month until delivery. The follow-up visit after intervention takes place 3 months post partum for both groups. Data are collected by laboratory blood tests, clinical measurements, capillary glucose measures, wearable sensors, air displacement plethysmography and digital questionnaires. The primary outcome is fasting plasma glucose change from BL to GW 35-37. Secondary outcomes include, for example, self-tracked capillary fasting and postprandial glucose measures, change in gestational weight gain, change in nutrition quality, change in physical activity, medication use due to GDM, birth weight and fat percentage of the child. ETHICS AND DISSEMINATION: The study has been approved by Ethics Committee of the Helsinki and Uusimaa Hospital District. The results will be presented in peer-reviewed journals and at conferences. TRIAL REGISTRATION NUMBER: NCT04714762.

Comparison of Heart Rate Monitoring Accuracy between Chest Strap and Vest during Physical Training and Implications on Training Decisions.

Heart rate (HR) and heart rate variability (HRV) based physiological metrics such as Excess Post-exercise Oxygen Consumption (EPOC), Energy Expenditure (EE), and Training Impulse (TRIMP) are widely utilized in coaching to monitor and optimize an athlete's training load. Chest straps, and recently also dry electrodes integrated to special sports vests, are used to monitor HR during sports. Mechanical design, placement of electrodes, and ergonomics of the sensor affect the measured signal quality and artefacts. To evaluate the impact of the sensor mechanical design on the accuracy of the HR/HRV and further on to estimation of EPOC, EE, and TRIMP, we recorded HR and HRV from a chest strap and a vest with the same ECG sensor during supervised exercise protocol. A 3-lead clinical Holter ECG was used as a reference. Twenty-five healthy subjects (six females) participated. Mean absolute percentage error (MAPE) for HR was 0.76% with chest strap and 3.32% with vest. MAPE was 1.70% vs. 6.73% for EE, 0.38% vs. 8.99% for TRIMP and 3.90% vs. 54.15% for EPOC with chest strap and vest, respectively. Results suggest superior accuracy of chest strap over vest for HR and physiological metrics monitoring during sports.

Frequency of Self-Weighing and Weight Change: Cohort Study With 10,000 Smart Scale Users.

BACKGROUND: Frequent self-weighing is associated with successful weight loss and weight maintenance during and after weight loss interventions. Less is known about self-weighing behaviors and associated weight change in free-living settings. OBJECTIVE: This study aimed to investigate the association between the frequency of self-weighing and changes in body weight in a large international cohort of smart scale users. METHODS: This was an observational cohort study with 10,000 randomly selected smart scale users who had used the scale for at least 1 year. Longitudinal weight measurement data were analyzed. The association between the frequency of self-weighing and weight change over the follow-up was investigated among normal weight, overweight, and obese users using Pearson's correlation coefficient and linear models. The association between the frequency of self-weighing and temporal weight change was analyzed using linear mixed effects models. RESULTS: The eligible sample consisted of 9768 participants (6515/9768, 66.7% men; mean age 41.5 years; mean BMI 26.8 kg/m2). Of the participants, 4003 (4003/9768, 41.0%), 3748 (3748/9768, 38.4%), and 2017 (2017/9768, 20.6%) were normal weight, overweight, and obese, respectively. During the mean follow-up time of 1085 days, the mean weight change was -0.59 kg, and the mean percentage of days with a self-weigh was 39.98%, which equals 2.8 self-weighs per week. The percentage of self-weighing days correlated inversely with weight change, r=-0.111 (P<.001). Among normal weight, overweight, and obese individuals, the correlations were r=-0.100 (P<.001), r=-0.125 (P<.001), and r=-0.148 (P<.001), respectively. Of all participants, 72.5% (7085/9768) had at least one period of ≥30 days without weight measurements. During the break, weight increased, and weight gains were more pronounced among overweight and obese individuals: 0.58 kg in the normal weight group, 0.93 kg in the overweight group, and 1.37 kg in the obese group (P<.001). CONCLUSIONS: Frequent self-weighing was associated with favorable weight loss outcomes also in an uncontrolled, free-living setting, regardless of specific weight loss interventions. The beneficial associations of regular self-weighing were more pronounced for overweight or obese individuals.

Health timeline: an insight-based study of a timeline visualization of clinical data.

BACKGROUND: The increasing complexity and volume of clinical data poses a challenge in the decision-making process. Data visualizations can assist in this process by speeding up the time required to analyze and understand clinical data. Even though empirical experiments show that visualizations facilitate clinical data understanding, a consistent method to assess their effectiveness is still missing. METHODS: The insight-based methodology determines the quality of insights a user acquires from the visualization. Insights receive a value from one to five points based on a domain-specific criteria. Five professional psychiatrists took part in the study using real de-identified clinical data spanning 4 years of medical history. RESULTS: A total of 50 assessments were transcribed and analyzed. Comparing a total of 558 insights using Health Timeline and 576 without, the mean value using the Timeline (1.7) was higher than without (1.26; p<0.01), similarly the cumulative value with the Timeline (11.87) was higher than without (10.96: p<0.01). The average time required to formulate the first insight with the Timeline was higher (13.16 s) than without (7 s; p<0.01). Seven insights achieved the highest possible value using Health Timeline while none were obtained without it. CONCLUSIONS: The Health Timeline effectively improved understanding of clinical data and helped participants recognize complex patterns from the data. By applying the insight-based methodology, the effectiveness of the Health Timeline was quantified, documented and demonstrated. As an outcome of this exercise, we propose the use of such methodologies to measure the effectiveness of visualizations that assist the clinical decision-making process.

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.

The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients.

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Although not life-threatening itself, AF significantly increases the risk of stroke and myocardial infarction. Current tools available for screening and monitoring of AF are inadequate and an unobtrusive alternative, suitable for long-term use, is needed. This paper evaluates an atrial fibrillation detection algorithm based on wrist photoplethysmographic (PPG) signals. 29 patients recovering from surgery in the post-anesthesia care unit were monitored. 15 patients had sinus rhythm (SR, 67.5± 10.7 years old, 7 female) and 14 patients had AF (74.8± 8.3 years old, 8 female) during the recordings. Inter-beat intervals (IBI) were estimated from PPG signals. As IBI estimation is highly sensitive to motion or other types of noise, acceleration signals and PPG waveforms were used to automatically detect and discard unreliable IBI. AF was detected from windows of 20 consecutive IBI with 98.45±6.89% sensitivity and 99.13±1.79% specificity for 76.34±19.54% of the time. For the remaining time, no decision was taken due to the lack of reliable IBI. The results show that wrist PPG is suitable for long term monitoring and AF screening. In addition, this technique provides a more comfortable alternative to ECG devices.

Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation.

OBJECTIVE: Atrial fibrillation (AF) causes marked risk for patients, while silent fibrillation may remain unnoticed if not suspected and screened. Development of comfortable yet accurate beat-to-beat heart rate (HR) monitoring with good AF detection sensitivity would facilitate screening and improve treatment. The purpose of this study was to evaluate whether a wrist-worn photoplethysmography (PPG) device can be used to monitor beat-to-beat HR accurately during post-operative treatment in patients suffering from AF and whether wrist-PPG can be used to distinguish AF from sinus rhythm (SR). APPROACH: Twenty-nine patients (14 with AF, 15 with SR, mean age 71.5 years) with multiple comorbidities were monitored during routine post-operative treatment. The monitoring included standard ECG, finger PPG monitoring and a wrist-worn PPG monitor with green and infrared light sources. The HR from PPG sensors was compared against ECG-derived HR. MAIN RESULTS: The wrist PPG technology had very good HR and beat detection accuracy when using green light. For the SR group, the mean absolute error (MAE) for HR was 1.50 bpm, and for the inter-beat intervals (IBI), the MAE was 7.64 ms. For the AF group, the MAE for HR was 4.28 bpm and for IBI, the MAE was 14.67 ms. Accuracy for the infrared (IR) channel was worse. Finger PPG provided similar accuracy for HR and better accuracy for the IBI. AF detection sensitivity using green light was 99.0% and the specificity was 93.0%. Performance can be improved by discarding unreliable IBI periods. SIGNIFICANCE: Results suggest that wrist PPG measurement allows accurate HR and beat-to-beat HR monitoring also in AF patients, and could be used for differentiating between SR and AF with very good sensitivity.

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.

An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial AccelerometerWrist-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.

Estimating Heart Rate, Energy Expenditure, and Physical Performance With a Wrist Photoplethysmographic Device During Running.

BACKGROUND: Wearable sensors enable long-term monitoring of health and wellbeing indicators. An objective evaluation of sensors' accuracy is important, especially for their use in health care. OBJECTIVE: The aim of this study was to use a wrist-worn optical heart rate (OHR) device to estimate heart rate (HR), energy expenditure (EE), and maximal oxygen intake capacity (VO2Max) during running and to evaluate the accuracy of the estimated parameters (HR, EE, and VO2Max) against golden reference methods. METHODS: A total of 24 healthy volunteers, of whom 11 were female, with a mean age of 36.2 years (SD 8.2 years) participated in a submaximal self-paced outdoor running test and maximal voluntary exercise test in a sports laboratory. OHR was monitored with a PulseOn wrist-worn photoplethysmographic device and the running speed with a phone GPS sensor. A physiological model based on HR, running speed, and personal characteristics (age, gender, weight, and height) was used to estimate EE during the maximal voluntary exercise test and VO2Max during the submaximal outdoor running test. ECG-based HR and respiratory gas analysis based estimates were used as golden references. RESULTS: OHR was able to measure HR during running with a 1.9% mean absolute percentage error (MAPE). VO2Max estimated during the submaximal outdoor running test was closely similar to the sports laboratory estimate (MAPE 5.2%). The energy expenditure estimate (n=23) was quite accurate when HR was above the aerobic threshold (MAPE 6.7%), but MAPE increased to 16.5% during a lighter intensity of exercise. CONCLUSIONS: The results suggest that wrist-worn OHR may accurately estimate HR during running up to maximal HR. When combined with physiological modeling, wrist-worn OHR may be used for an estimation of EE, especially during higher intensity running, and VO2Max, even during submaximal self-paced outdoor recreational running.

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) 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.

Procedures for Evaluating the Adequacy of Anesthesia.

Assessing adequacy of anesthesia requires evaluation of its components: hypnosis, analgesia, and neuromuscular transmission. In order to do this, many methods have been developed that process signals representing different modalities. Assessment of hypnosis requires cortical measures of the central nervous system (CNS); methods that assess analgesia concentrate on subcortical and spinal levels of the CNS; and neuromuscular transmission is a peripheral phenomenon. This article presents an overview of the current state of methods available for measuring each of these components. We conclude that, whereas important gains have been made in the area of assessment of hypnosis, mainly owing to the advancement of methods using EEG and auditory evoked potentials, and whereas neuromuscular transmission can be objectively monitored using motor nerve stimulation, assessment of analgesia still contains many challenges.

Towards 24/7 continuous heart rate monitoring.

Heart rate (HR) and HR variability (HRV) carry rich information about physical activity, mental and physical load, physiological status, and health of an individual. When combined with activity monitoring and personalized physiological modelling, HR/HRV monitoring may be used for monitoring of complex behaviors and impact of behaviors and external factors on the current physiological status of an individual. Optical HR monitoring (OHR) from wrist provides a comfortable and unobtrusive method for HR/HRV monitoring and is better adhered by users than traditional ECG electrodes or chest straps. However, OHR power consumption is significantly higher than that for ECG based methods due to the measurement principle based on optical illumination of the tissue. We developed an algorithmic approach to reduce power consumption of the OHR in 24/7 HR trending. We use continuous activity monitoring and a fast converging frequency domain algorithm to derive a reliable HR estimate in 7.1s (during outdoor sports, in average) to 10.0s (during daily life). The method allows >80% reduction in power consumption in 24/7 OHR monitoring when average HR monitoring is targeted, without significant reduction in tracking accuracy.

Seasonal weight variation patterns in seven countries located in northern and southern hemispheres.

The digital revolution of information and technology in late 20th century has led to emergence of devices that help people monitor their weight in a long-term manner. Investigation of population-level variations of body mass using smart connected weight scales enabled the health coaches acquire deeper insights about the models of people's behavior as a function of time. Typically, body mass varies when the seasons change. That is, during the warmer seasons people's body mass tend to decrease while in colder seasons it usually moves up. In this paper we study the seasonal variations of body mass in seven countries by utilization of linear regression. Deviation of monthly weight values from the starting point of astronomical years (beginning of spring) were modeled by fitting orthogonal polynomials in each country. The distinction of weight variations in southern and northern hemispheres were then investigated. The studied population involves 6429 anonymous weight scale users from:(1) Australia, (2) Brazil, (3) France, (4) Germany, (5) Great Britain, (6) Japan, and (7) United States of America. The results suggest that there are statistically significant differences between the models of weight variation in southern and northern hemispheres. In both northern and southern hemispheres the lowest weight values were observed in the summer. However, the highest weight values were noticed in the winter and in the spring for northern and southern hemispheres, respectively.

W2E--Wellness Warehouse Engine for Semantic Interoperability of Consumer Health Data.

Novel health monitoring devices and applications allow consumers easy and ubiquitous ways to monitor their health status. However, technologies from different providers lack both technical and semantic interoperability and hence the resulting health data are often deeply tied to a specific service, which is limiting its reusability and utilization in different services. We have designed a Wellness Warehouse Engine (W2E) that bridges this gap and enables seamless exchange of data between different services. W2E provides interfaces to various data sources and makes data available via unified representational state transfer application programming interface to other services. Importantly, it includes Unifier--an engine that allows transforming input data into generic units reusable by other services, and Analyzer--an engine that allows advanced analysis of input data, such as combining different data sources into new output parameters. In this paper, we describe the architecture of W2E and demonstrate its applicability by using it for unifying data from four consumer activity trackers, using a test base of 20 subjects each carrying out three different tracking sessions. Finally, we discuss challenges of building a scalable Unifier engine for the ever-enlarging number of new devices.

Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents.

Sleep problems and disrupted circadian rhythms are common among older adults and may be associated with several health issues and physical functioning status. Wearable continuous monitoring of physical activity enables unobtrusive monitoring of circadian activity and sleep patterns. The objective of this retrospective study was to analyze whether physical functioning status (Activities of Daily Living assessment of Resident Assessment Instrument) is associated with diurnal activity rhythm and sleep patterns measured with wearable activity sensor in nursing home residents during their normal daily life. Continuous activity data were collected by the wearable sensor from 16 nursing home residents (average age of 90.7 years, seven demented subjects, one female) in their daily life over several months (12-18 months). The subjects' physical activity and sleep were quantified by several parameters from the activity data. In the cross-sectional analysis, physical functioning status was associated with the strength (RHO = 0.78, ) and the stability (RHO = 0.72, ) of the activity rhythm when the level of dementia was not controlled. In the longitudinal analysis (12-18 months), at an individual level the activity rhythm indices and activity level had the strongest correlations with changes in physical functioning but the associations were to some extent individual. In these long-term case recordings, decrease in the physical functioning was most strongly associated with decreasing levels of activity, stability, and strength of the activity rhythm, and with increasing fragmentation of rhythm and daytime passivity. Daily wearable monitoring of physical activity may hence reveal information about functioning state and health of older adults. However, since the changes in activity patterns implying changes in physical functioning status may not be consistent between the individuals, a multivariate approach is recommended for monitoring of these changes by continuous physical activity measurement.

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care.

Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.