Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

Tele-Monitoring Applications in Respiratory Allergy

Respiratory allergic diseases affect over 500 million people globally and pose a substantial burden in terms of morbidity, mortality, and healthcare costs. Restrictive factors such as geographical disparities, infectious pandemics, limitations in resources, and shortages of allergy specialists in underserved areas impede effective management. Telemedicine encompasses real-time visits, store-and-forward option triage, and computer-based technologies for establishing efficient doctor-patient communication. Recent advances in digital technology, including designated applications, informative materials, digital examination devices, wearables, digital inhalers, and integrated platforms, facilitate personalized and evidence-based care delivery. The integration of telemonitoring in respiratory allergy care has shown beneficial effects on disease control, adherence, and quality of life. While the COVID-19 pandemic accelerated the adoption of telemedicine, certain concerns regarding technical requirements, platform quality, safety, reimbursement, and regulatory considerations remain unresolved. The integration of artificial intelligence (AI) in telemonitoring applications holds promise for data analysis, pattern recognition, and personalized treatment plans. Striking the balance between AI-enabled insights and human expertise is crucial for optimizing the benefits of telemonitoring. While telemonitoring exhibits potential for enhancing patient care and healthcare delivery, critical considerations have to be addressed in order to ensure the successful integration of telemonitoring into the healthcare landscape.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

A scoping review of mHealth monitoring of pediatric bronchial asthma before and during COVID-19 pandemic

Mobile (m) Health technology is well-suited for Remote Patient Monitoring (RPM) in a patient's habitual environment. In recent years there have been fast-paced developments in mHealth-enabled pediatric RPM, especially during the COVID-19 pandemic, necessitating evidence synthesis. To this end, we conducted a scoping review of clinical trials that had utilized mHealth-enabled RPM of pediatric asthma. MEDLINE, Embase and Web of Science were searched from September 1, 2016 through August 31, 2021. Our scoping review identified 25 publications that utilized synchronous and asynchronous mHealth-enabled RPM in pediatric asthma, either involving mobile applications or via individual devices. The last three years has seen the development of evidence-based, multidisciplinary, and participatory mHealth interventions. The quality of the studies has been improving, such that 40% of included study reports were randomized controlled trials. In conclusion, there exists high-quality evidence on mHealth-enabled RPM in pediatric asthma, warranting future systematic reviews and/or meta-analyses of the benefits of such RPM.

Utility of Fitbit devices among children and adolescents with chronic health conditions: a scoping review

Background

While Fitbit® devices were initially intended for leisurely, consumer use, there has been recent interest among scientific and medical communities in the prospective use of Fitbit devices for clinical and research purposes. Those who have chronic health conditions are often required to spend considerable amounts of money and time undergoing physiological tests and activity monitoring to support, stabilize, and manage their health. This disease burden is only amplified in pediatric populations. Devices that are used to collect these data can be invasive, uncomfortable, and disconcerting. Using the Fitbit tracker to acquire such biometric data could ease this burden. Our scoping review seeks to summarize the research that has been conducted on the utilization of Fitbit devices in studies of children and adolescents with chronic health conditions and the feasibility, accuracy, and potential benefits of doing so.

Methods

Searches were conducted on PubMed for articles relating pediatric health to Fitbit device usage (using a Boolean search strategy). The eligibility criteria included trials being clinical and/or randomized controlled and articles being in English. Once articles were obtained, they underwent screening and exclusion processes and were charted for their titles, authors, objectives, results, and respective chronic illnesses. In the subsequent full-text review, further charting was conducted, collecting study designs, Fitbit parameters, feasibility, accuracy, and related health and clinical outcomes.

Results

Fitbit trackers were unanimously demonstrated to be feasible devices in this population for physical activity monitoring and were determined to be potentially beneficial in measuring and improving overall wellbeing and physical health in children with chronic illness. Nevertheless, sufficient evidence was not found in support of Fitbit accuracy. Additional biases were identified against the population of children with chronic health conditions that may further enable inaccurate data.

Conclusions

While Fitbit devices may be beneficial for those interested in improving physical health, discretion is advised for those seeking to collect accurate and/or medically necessitated data. Given the existing literature evaluated, medical-grade technologies are preferred in instances of the latter, as Fitbit devices have not been found to provide reliably accurate data.

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.

Five-year pediatric use of a digital wearable fitness device: lessons from a pilot case study

Objectives

Wearable fitness devices are increasingly being used by the general population, with many new applications being proposed for healthy adults as well as for adults with chronic diseases. Fewer, if any, studies of these devices have been conducted in healthy adolescents and teenagers, especially over a long period of time. The goal of this work was to document the successes and challenges involved in 5 years of a wearable fitness device use in a pediatric case study.

Materials and methods

Comparison of 5 years of step counts and minutes asleep from a teenaged girl and her father.

Results

At 60 months, this may be the longest reported pediatric study involving a wearable fitness device, and the first simultaneously involving a parent and a child. We find step counts to be significantly higher for both the adult and teen on school/work days, along with less sleep. The teen walked significantly less towards the end of the 5-year study. Surprisingly, many of the adult’s and teen’s sleeping and step counts were correlated, possibly due to coordinated behaviors.

Discussion

We end with several recommendations for pediatricians and device manufacturers, including the need for constant adjustments of stride length and calorie counts as teens are growing.

Conclusion

With periodic adjustments for growth, this pilot study shows these devices can be used for more accurate and consistent measurements in adolescents and teenagers over longer periods of time, to potentially promote healthy behaviors.

Comorbid Sleep Disturbance in Adolescents with Functional Abdominal Pain

OBJECTIVE/BACKGROUND

Sleep disturbances have been commonly reported as comorbid in youth with pain conditions, but prior research specific to functional abdominal pain (FAP) is limited. This study describes individual factors associated with increased risk for sleep disturbance and characterizes the relationship between sleep disturbance and pain-related variables.

PARTICIPANTS

Participants included 278 adolescents (age 11 to 17 years, M age = 15 years; 89% Caucasian; 65% female) with FAP.

METHODS

Participants reported on sleep disturbances, abdominal pain severity, functional disability, somatic symptoms, and healthcare utilization.

RESULTS

Female adolescents reported greater sleep disturbance than male adolescents (t(276) = 5.52, p < .001, Cohen's d = 0.70) and increased age was associated with greater sleep disturbance (r =.20, p =.001). In hierarchical regressions controlling for age, sex, and abdominal pain, greater sleep disturbance was significantly associated with greater functional disability (β =.32), non-gastrointestinal somatic symptoms (β =.35), and emergency department visits (β =.29).

CONCLUSIONS

Results suggest that sleep disturbance is common and should be assessed in youth presenting with FAP and may be a potential target for intervention.

Combining Wearable Devices and Mobile Surveys to Study Child and Youth Development in Malawi: Implementation Study of a Multimodal Approach

BACKGROUND

Multimodal approaches have been shown to be a promising way to collect data on child development at high frequency, combining different data inputs (from phone surveys to signals from noninvasive biomarkers) to understand children's health and development outcomes more integrally from multiple perspectives.

OBJECTIVE

The aim of this work was to describe an implementation study using a multimodal approach combining noninvasive biomarkers, social contact patterns, mobile surveying, and face-to-face interviews in order to validate technologies that help us better understand child development in poor countries at a high frequency.

METHODS

We carried out a mixed study based on a transversal descriptive analysis and a longitudinal prospective analysis in Malawi. In each village, children were sampled to participate in weekly sessions in which data signals were collected through wearable devices (electrocardiography [ECG] hand pads and electroencephalography [EEG] headbands). Additionally, wearable proximity sensors to elicit the social network were deployed among children and their caregivers. Mobile surveys using interactive voice response calls were also used as an additional layer of data collection. An end-line face-to-face survey was conducted at the end of the study.

RESULTS

During the implementation, 82 EEG/ECG data entry points were collected across four villages. The sampled children for EEG/ECG were 0 to 5 years old. EEG/ECG data were collected once a week. In every session, children wore the EEG headband for 5 minutes and the ECG hand pad for 3 minutes. In total, 3531 calls were sent over 5 weeks, with 2291 participants picking up the calls and 984 of those answering the consent question. In total, 585 people completed the surveys over the course of 5 weeks.

CONCLUSIONS

This study achieved its objective of demonstrating the feasibility of generating data through the unprecedented use of a multimodal approach for tracking child development in Malawi, which is one of the poorest countries in the world. Above and beyond its multiple dimensions, the dynamics of child development are complex. It is the case not only that no data stream in isolation can accurately characterize it, but also that even if combined, infrequent data might miss critical inflection points and interactions between different conditions and behaviors. In turn, combining different modes at a sufficiently high frequency allows researchers to make progress by considering contact patterns, reported symptoms and behaviors, and critical biomarkers all at once. This application showcases that even in developing countries facing multiple constraints, complementary technologies can leverage and accelerate the digitalization of health, bringing benefits to populations that lack new tools for understanding child well-being and development.

Empowering children and young people who have asthma

Asthma is the most common chronic condition of childhood. In this review, we discuss an overview of strategies to empower children and young people with asthma. The key aspects of empowerment are to enable shared decision making and self-management, and help children minimise the impact of asthma on their life. The evidence behind these strategies is either sparse or heterogenous, and it is difficult to identify which interventions are most likely to improve clinical outcomes. Wider determinants of health, in high-resource and low-resource settings, can be disempowering for children with asthma. New approaches to technology could help empower young people with asthma and other chronic health conditions.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs

BACKGROUND

Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD.

OBJECTIVE

In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration.

METHODS

We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers.

RESULTS

We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit.

CONCLUSIONS

We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

Physical Activity in Children and Adolescents With Chronic Respiratory Diseases: A Systematic Review and Meta-Analysis

BACKGROUND

The literature is unclear as to whether children and adolescents with chronic respiratory diseases (CRDs) differ from their healthy peers in physical activity (PA).

OBJECTIVE

To determine the PA levels measured through accelerometers in children and adolescents with CRDs.

METHODS

The authors conducted a systematic review using five databases. The authors included studies that assessed the PA measured by accelerometers in children and adolescents with CRDs. Two independent reviewers analyzed the studies, extracted the data, and assessed the quality of evidence.

RESULTS

From 11,497 reports returned by the initial search, 29 articles reporting on 4381 patients were included. In the sensitivity analysis, the authors found that children and adolescents with CRDs had a moderate-to-vigorous PA (MVPA) of -0.08 hours per day (95% confidence interval [CI], -0.12 to -0.03 h/d; P = .001), which was lower than the healthy controls; the values for sedentary time (mean difference -0.47 h/d; 95% CI, -1.29 to 0.36 h/d; P = .27) and steps/d (mean difference 361 steps/d; 95% CI -385 to 1707 steps/d; P = .45) were similar for both.

CONCLUSION

Children and adolescents with CRDs have a slight reduction in MVPA in comparison with healthy controls, but sedentary time and steps/d were similar for both.

Digital health interventions in children with asthma

Although healthcare providers are actively involved in offering education, information and interventions for asthmatic patients, medication and therapeutic adherence remain low in the paediatric population, with estimates suggesting that adherence rates hover below 50%. A range of available digital health interventions has been explored in paediatric asthma with promising but variable results, limiting their widespread adoption in clinical practice. They include emerging technologies that yield the advantage of tracking asthma symptoms and medications, setting drug reminders, improving inhaler technique and delivering asthma education, such as serious games (video games designed for medical‐ or health‐related purposes), electronic monitoring devices, speech recognition calls, text messaging, mobile apps and interactive websites. Some of the proposed digital interventions have used multiple components, including educational and behavioural strategies and interactions with medical professionals. Overall, the implementation of such interventions may offer the opportunity to improve adherence and asthma control. In a state of emergency as the COVID‐19 pandemic, telemedicine can also play a central role in supporting physicians in managing children with asthma. This review evaluates the published literature examining digital health interventions for paediatric asthma and explores the most relevant issues affecting their implementation in practice and the associated evidence gaps, research limitations and future research perspectives.

Evidence Base Update on Assessing Sleep in Youth

BACKGROUND

Sleep is vital to youth well-being and when it becomes disturbed - whether due to environmental or individual factors - mental and physical health suffer. Sleep problems can also be a symptom of underlying mental health disorders. Assessing different components of sleep, including quality and hygiene, can be useful both for identifying mental health problems and for measuring changes in well-being over time. However, there are dozens of sleep-related measures for youth and it can be difficult to determine which to select for a specific research or clinical purpose. The goal of this review was to identify sleep-related measures for clinical and/or research use in youth mental health settings, and to update the evidence base on this topic.

METHOD

We generated a list of candidate measures based on other reviews and searched in PubMed and PsycINFO using the terms "sleep" AND (measure OR assessment OR questionnaire) AND (psychometric OR reliability OR validity). Search results were limited to studies about children and adolescents (aged 2-17) published in English. Additional criteria for inclusion were that there had to be at least three publications reporting on the measure psychometrics in community or mental health populations. Sleep measures meeting these criteria were evaluated using the criteria set by De Los Reyes and Langer (2018).

RESULTS

Twenty-six measures, across four domains of sleep - insomnia, sleep hygiene, sleepiness, sleep quality - met inclusion criteria. Each measure had at least adequate clinical utility. No measure(s) emerged as superior across psychometric domains.

CONCLUSION

Clinicians and researchers must evaluate sleep measures for each use case, as the intended purpose will dictate which measure is best. Future research is necessary to evaluate measure performance in transdiagnostic mental health populations, including youth with serious mental illness.

Subgroups of Pediatric Patients With Functional Abdominal Pain: Replication, Parental Characteristics, and Health Service Use

OBJECTIVES

Prior work in a cohort of youth with functional abdominal pain (FAP) identified patient subgroups (High Pain Dysfunctional, High Pain Adaptive, Low Pain Adaptive) that predicted differences in the course of FAP from childhood into young adulthood. We aimed to replicate these subgroups in a new sample of adolescents with FAP using the original classification algorithm and to extend subgroup characteristics to include parental characteristics and health service use.

METHODS

Adolescents (n=278; ages 11 to 17 y, 66% females) presenting to a gastroenterology clinic for abdominal pain, and their parents (92% mothers) completed self-report measures; adolescents also completed a 7-day pain diary.

RESULTS

The replicated patient subgroups exhibited distress and impairment similar to subgroups in the original sample. Moreover, in novel findings, the High Pain Dysfunctional subgroup differed from other subgroups by the predominance of mother-daughter dyads jointly characterized by high levels of anxiety, depressive symptoms, pain behavior, and pain catastrophizing. The High Pain Dysfunctional subgroup used more health care services than Low Pain Adaptive but did not differ from High Pain Adaptive.

DISCUSSION

Findings replicate and extend the original FAP classification and suggest that the subgroups have unique patient and parent features that may reflect distinct illness mechanisms requiring different treatments.

Wearable Technology and How This Can Be Implemented into Clinical Practice

PURPOSE OF REVIEW

Our day-to-day life is saturated with health data that was previously out of reach. Over the last decade, new devices and fitness technology companies are attempting to tap into this data, uncovering a treasure trove of useful information that, when applied correctly, has the potential to revolutionize the way we approach healthcare and chronic conditions like asthma, especially in the wake of the COVID-19 pandemic.

RECENT FINDINGS

By harnessing exciting developments in personalization, digitization, wellness, and patient engagement, care providers can improve health outcomes for our patients in a way we have never been able to do in the past. While new technologies to capture individual health metrics are everywhere, how can we use this information to make a real difference in our patients' lives? Navigating the complicated landscape of personal wearable devices, asthma inhaler sensors, and exercise apps can be daunting to even the most tech savvy physician. This manuscript will give you the tools necessary to make lasting changes in your patients' lives by exposing them to a world of usable, affordable, and relatable health technology that resonates with their personal fitness and wellness goals. These tools will be even more important post-COVID-19, as the landscape of clinical outpatient care changes from mainly in-person visits to a greater reliance on telemedicine and remote monitoring.

Psychometric properties of the Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance and Sleep-Related Impairment item banks in adolescents

Sleep problems have a high prevalence and negative daytime consequences in adolescents. Current sleep measures for this age group have limitations. The Patient‐Reported Outcomes Measurement Information System (PROMIS^®) developed sleep item banks for adults. In a previous validation study, these item banks were adapted to a shortened version for adolescents. The current study aimed to further explore the psychometric properties of the 11‐item Sleep‐Related Impairment and 23‐item Sleep Disturbance item banks in Dutch adolescents. We investigated structural validity by testing item response theory assumptions and model fit; measurement invariance by performing differential item functioning analyses; performance as a computerized adaptive test; reliability by marginal reliability estimates and test–retest reliability (intraclass correlation coefficients and limits of agreement); and construct validity by hypothesis testing. Additionally, we provide mean values for the item banks. The study sample consisted of 1,046 adolescents (mean age 14.3 ± 1.6), including 1,013 high‐school students and 33 sleep‐clinic patients. The Sleep Disturbance‐23 showed lack of unidimensionality, but had sufficient test–retest reliability, and could distinguish between adolescents with and without sleep or health issues. The Sleep‐Related Impairment‐11 showed sufficient unidimensionality and model fit and was thus tested as a computerized adaptive test, demonstrating an equal amount of reliable measures to the full item bank. Furthermore, the Sleep‐Related Impairment‐11 could distinguish between adolescents with and without sleep or health issues and test–retest reliability was moderate. The use of both item banks in the full form and the use of the Sleep‐related Impairment‐11 as a computer adaptive test is recommended.

Sensors Capabilities, Performance, and Use of Consumer Sleep Technology

Sleep is crucial for the proper functioning of bodily systems and for cognitive and emotional processing. Evidence indicates that sleep is vital for health, well-being, mood, and performance. Consumer sleep technologies (CSTs), such as multisensory wearable devices, have brought attention to sleep and there is growing interest in using CSTs in research and clinical applications. This article reviews how CSTs can process information about sleep, physiology, and environment. The growing number of sensors in wearable devices and the meaning of the data collected are reviewed. CSTs have the potential to provide opportunities to measure sleep and sleep-related physiology on a large scale.

Prediction model development of women's daily asthma control using fitness tracker sleep disruption

BACKGROUND

Night-time wakening with asthma symptoms is an important indicator of disease control and severity, with no gold-standard objective measurement.

OBJECTIVE

The study objective was to use fitness tracker sleep data to develop predictive models of daily disease control-related asthma-specific wakening and FEV₁ in working-aged women with poorly controlled asthma.

METHODS

A repeated measures panel design included data from 43 women with poorly controlled asthma. Two components of asthma control were the primary outcomes, measured daily as (1) self-reported asthma-specific wakening and (2) self-administered spirometry to measure FEV₁. Data were analyzed using generalized linear mixed models.

RESULTS

Our models demonstrated predictive value (AUC=0.77) for asthma-specific night-time wakening and good predictive value (AUC=0.83) for daily FEV_1. CONCLUSIONS: Fitness tracker sleep efficiency and wake counts demonstrate clinical utility as predictive of asthma-specific night-time wakening and daily FEV_1. Fitness tracker sleep data demonstrated predictive capability for daily asthma outcomes.

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.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

BACKGROUND

It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown.

OBJECTIVE

This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy.

METHODS

A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors.

RESULTS

Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.

CONCLUSIONS

Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

Background

New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects.

Objective

The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research.

Methods

We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands.

Results

We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands.

Conclusions

The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

How Will the Internet of Things Enable Augmented Personalized Health?

The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other similar devices over the Internet. The IoT is profoundly redefining the way we create, consume, and share information. Ordinary citizens increasingly use these technologies to track their sleep, food intake, activity, vital signs, and other physiological statuses. This activity is complemented by IoT systems that continuously collect and process environment-related data that has a bearing on human health. This synergy has created an opportunity for a new generation of healthcare solutions.