Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Explanatory variables of objectively measured 24-h movement behaviors in people with prediabetes and type 2 diabetes: A systematic review

AIM

Physical activity (PA), sedentary behavior (SB) and sleep (i.e. 24-h movement behaviors) are associated with health indicators in people with prediabetes and type 2 diabetes (T2D). To optimize 24-h movement behaviors, it is crucial to identify explanatory variables related to these behaviors. This review aimed to summarize the explanatory variables of 24-h movement behaviors in people with prediabetes or T2D.

METHODS

A systematic search of four databases (PubMed, Web of Science, Scopus & Embase) was performed. Only objective measurements of 24-h movement behaviors were included in the search strategy. The explanatory variables were classified according to the levels of the socio-ecological model (i.e. intrapersonal, interpersonal and environmental). The risk of bias was assessed using the Joanna Briggs Institute appraisal checklist.

RESULTS

None of the 78 included studies investigated 24-h movement behaviors. The majority of the studies investigated PA in isolation. Most studied explanatory variables were situated at the intrapersonal level. Being male was associated with more moderate to vigorous PA but less light PA in people with T2D, and more total PA in people with prediabetes. An older age was associated with a decrease in all levels of PA in people with T2D. HbA1c was positively associated with sleep and SB in both groups. No associations were found at the interpersonal or environmental level.

CONCLUSION

The results of this review underscore the lack of a socio-ecological approach toward explanatory variables of 24-h movement behaviors and the lack of focus on an integrated 24-h movement behavior approach in both populations.

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.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors

BACKGROUND

Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care.

OBJECTIVE

This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives.

METHODS

We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook.

RESULTS

We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors.

CONCLUSIONS

A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults

The use of wearable technology, which is often acquired to support well-being and a healthy lifestyle, has become popular in Western countries. At the same time, healthcare is gradually taking the first steps to introduce wearable technology into patient care, even though on a large scale the evidence of its' effectiveness is still lacking. The objective of this study was to identify the factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the Finnish adult population (20-99) and among older adults (65-99). The study utilized a cross-sectional population survey of Finnish adults aged 20 and older (n = 6,034) to analyse non-causal relationships between wearable technology use and the users' characteristics. Logistic regression models of wearable technology use were constructed using statistically significant sociodemographic, well-being, health, benefit, and lifestyle variables. Both in the general adult population and among older adults, wearable technology use was associated with getting aerobic physical activity weekly according to national guidelines and with marital status. In the general adult population, wearable technology use was also associated with not sleeping enough and agreeing with the statement that social welfare and healthcare e-services help in taking an active role in looking after one's own health and well-being. Younger age was associated with wearable technology use in the general adult population but for older adults age was not a statistically significant factor. Among older adults, non-use of wearable technology went hand in hand with needing guidance in e-service use, using a proxy, or not using e-services at all. The results support exploration of the effects of wearable technology use on maintaining an active lifestyle among adults of all ages.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

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.

Anxiolytic Effect and Improved Sleep Quality in Individuals Taking Lippia citriodora Extract

The current fast-moving, hectic lifestyle has increased the number of individuals worldwide with difficulties in managing stress, which in turn is also affecting their sleep quality. Therefore, the objective of the current study was to assess a natural plant-based dietary supplement comprised of lemon verbena (Lippia citriodora) extract, purified in phenylpropanoids, in alleviating stress and improving quality of sleep. A double-blind, placebo-controlled study was conducted for 8 weeks, followed by a 4-week washout period. Both validated questionnaires and functional tests were performed during the study, whereas questionnaires were used after the washout. As a result, the group taking the lemon verbena extract significantly reduced their perception of stress after 8 weeks, which was corroborated by a significant decrease in cortisol levels. After the washout period, the subjects reported to present even lower stress levels, due to the lasting effect of the ingredient. As for sleep quality, the subjects taking the supplement reported feeling better rested, with a stronger effect observed in women. Sleep tracking using a wearable device revealed that the supplement users improved their times in the deeper stages of sleep, specifically their percentage of time in deep sleep and REM. In conclusion, lemon verbena extract purified in phenylpropanoids is revealed as a natural solution to help individuals to improve their stress and sleep quality.

Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review

Background

Complications due to type 2 diabetes (T2D) can be mitigated through proper self-management that can positively change health behaviors. Technological tools are available to help people living with, or at risk of developing, T2D to manage their condition, and such tools provide a large repository of patient-generated health data (PGHD). Analytics can provide insights into the health behaviors of people living with T2D.

Objective

The aim of this review is to investigate what can be learned about the health behaviors of those living with, or at risk of developing, T2D through analytics from PGHD.

Methods

A scoping review using the Arksey and O’Malley framework was conducted in which a comprehensive search of the literature was conducted by 2 reviewers. In all, 3 electronic databases (PubMed, IEEE Xplore, and ACM Digital Library) were searched using keywords associated with diabetes, behaviors, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted, after which studies were selected. Critical examination took place through a descriptive-analytical narrative method, and data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective.

Results

We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles.

Conclusions

The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources.

Perceived Benefits, Barriers, and Facilitators of a Digital Patient-Reported Outcomes Tool for Routine Diabetes Care: Protocol for a National, Multicenter, Mixed Methods Implementation Study

BACKGROUND

There is growing evidence that digital patient-reported outcome (PRO) questionnaires and PRO-based decision support tools may help improve the active engagement of people with diabetes in self-care, thereby improving the quality of care. However, many barriers still exist for the real-world effectiveness and implementation of such PRO tools in routine care. Furthermore, limited research has evaluated the acceptability, feasibility, and benefits of such tools across different health care settings.

OBJECTIVE

This study aims to evaluate the acceptability, feasibility, and perceived benefits of the Danish digital PRO diabetes tool in different health care settings in Denmark and to determine the factors affecting its implementation. Furthermore, the study evaluates the psychometric characteristics of the Danish PRO Diabetes Questionnaire and the validity of the scoring algorithms for dialogue support. The objective of this study is to guide the ongoing optimization of the PRO diabetes tool, its implementation, and the design of future randomized controlled effectiveness studies.

METHODS

We designed a multicenter, mixed methods, single-arm acceptability-feasibility implementation study protocol to contribute to the real-world pilot test of a new digital PRO diabetes tool in routine diabetes care. The use of the tool involves two main steps. First, the people with diabetes will complete a digital PRO Diabetes Questionnaire in the days before a routine diabetes visit. Second, the health care professional (HCP) will use a digital PRO tool to review the PRO results together with the people with diabetes during the visit. The PRO diabetes tool is designed to encourage and support people to take an active role for the people with diabetes in their own care and to expedite the delivery of person-centered, collaborative, and coordinated care.

RESULTS

A multicenter pilot study protocol and psychometrically designed digital data collection tools for evaluation were developed and deployed as part of a national evaluation of a new digital PRO diabetes intervention. A total of 598 people with diabetes and 34 HCPs completed the study protocol by April 1, 2021.

CONCLUSIONS

A large-scale, mixed methods, multicenter study for evaluating the use of the nationally developed PRO Diabetes Questionnaire in routine care across all health care sectors in Denmark by using the RE-AIM (Reach, Efficacy, Adoption, Implementation and Maintenance) model as a framework has been designed and is ongoing. This study is expected to provide new important and detailed information about the real-world acceptability, perceived relevance, and benefits of the PRO diabetes tool among a large heterogeneous population of people with diabetes in Denmark and HCPs in different care settings. The results will be used to further improve the PRO tool, design implementation facilitation support strategies, and design future controlled effectiveness studies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/28391.

Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2

There is no established method for processing data from commercially available physical activity trackers. This study aims to develop a standardized approach to defining valid wear time for use in future interventions and analyses. Sixteen African American women (mean age = 62.1 years and mean body mass index = 35.5 kg/m2) wore the Fitbit Charge 2 for 20 days. Method 1 defined a valid day as ≥10-hr wear time with heart rate data. Method 2 removed minutes without heart rate data, minutes with heart rate ≤ mean - 2 SDs below mean and ≤2 steps, and nighttime. Linear regression modeled steps per day per week change. Using Method 1 (n = 292 person-days), participants had 20.5 (SD = 4.3) hr wear time per day compared with 16.3 (SD = 2.2) hr using Method 2 (n = 282) (p < .0001). With Method 1, participants took 7,436 (SD = 3,543) steps per day compared with 7,298 (SD = 3,501) steps per day with Method 2 (p = .64). The proposed algorithm represents a novel approach to standardizing data generated by physical activity trackers. Future studies are needed to improve the accuracy of physical activity data sets.

A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers

Consumer wearable activity trackers, such as Fitbit are widely used in ubiquitous and longitudinal sleep monitoring in free-living environments. However, these devices are known to be inaccurate for measuring sleep stages. In this study, we develop and validate a novel approach that leverages the processed data readily available from consumer activity trackers (i.e., steps, heart rate, and sleep metrics) to predict sleep stages. The proposed approach adopts a selective correction strategy and consists of two levels of classifiers. The level-I classifier judges whether a Fitbit labeled sleep epoch is misclassified, and the level-II classifier re-classifies misclassified epochs into one of the four sleep stages (i.e., light sleep, deep sleep, REM sleep, and wakefulness). Best epoch-wise performance was achieved when support vector machine and gradient boosting decision tree (XGBoost) with up sampling were used, respectively at the level-I and level-II classification. The model achieved an overall per-epoch accuracy of 0.731 ± 0.119, Cohen's Kappa of 0.433 ± 0.212, and multi-class Matthew's correlation coefficient (MMCC) of 0.451 ± 0.214. Regarding the total duration of individual sleep stage, the mean normalized absolute bias (MAB) of this model was 0.469, which is a 23.9% reduction against the proprietary Fitbit algorithm. The model that combines support vector machine and XGBoost with down sampling achieved sub-optimal per-epoch accuracy of 0.704 ± 0.097, Cohen's Kappa of 0.427 ± 0.178, and MMCC of 0.439 ± 0.180. The sub-optimal model obtained a MAB of 0.179, a significantly reduction of 71.0% compared to the proprietary Fitbit algorithm. We highlight the challenges in machine learning based sleep stage prediction with consumer wearables, and suggest directions for future research.

Using an activity tracker to increase motivation for physical activity in patients with type 2 diabetes in primary care: a randomized pilot trial

BACKGROUND

Adopting healthy lifestyle habits reduces the risk of type 2 diabetes (T2D) and its complications. The use of an activity tracker to monitor physical activity (PA) could favor behavior changes in patients with chronic diseases such as diabetes. The aims of this study were: (I) to evaluate the impact of an activity tracker on PA and cardiometabolic risk variables in patients with T2D; (II) to assess the feasibility of its implantation in a primary care setting.

METHODS

This 3-month study was a pilot randomized controlled trial of 30 patients with T2D followed at a university-affiliated Family Medicine Group. Patients were randomly assigned to either: (I) control group, including a PA promotion intervention supported by a kinesiologist or (II) intervention group, including a PA promotion intervention supported by a kinesiologist with the addition of an activity tracker (Fitbit). Cardiometabolic risk variables, PA and motivation were assessed at baseline and after three months. Satisfaction and acceptability of wearing the activity tracker were measured in the intervention group.

RESULTS

PA assessed by questionnaires increased in both groups, change being greater in the intervention group (P<0.05). Autonomous motivation in both groups was higher than controlled motivation (P<0.001). Eighty-six percent of the participants in the intervention group were satisfied with their activity tracker use and the compliance remained high. High-density lipoprotein cholesterol increased in the intervention group and decreased in the control group (P=0.014). Resting systolic and diastolic blood pressure decreased over time in both groups (P<0.05) whereas glycated hemoglobin tended to decrease in both groups (P=0.080). Significant correlations were observed between average steps per day and changes in waist circumference (pre: -0.721, P=0.044; post: -0.736, P=0.038), body mass index (pre: -0.764, P=0.010; post: -0.771, P=0.009) and fat percentage (pre: -0.654, P=0.040; post: -0.686, P=0.028) in the intervention group.

CONCLUSIONS

Our pilot study shows that the use of an activity tracker improves cardiometabolic risk variables in patients with T2D and could potentially be a motivation tool to increase PA in primary care setting.

Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study

Background

Mental health disorders can disrupt a person’s sleep, resulting in lower quality of life. Early identification and referral to mental health services are critical for active duty service members returning from forward-deployed missions. Although technologies like wearable computing devices have the potential to help address this problem, research on the role of technologies like Fitbit in mental health services is in its infancy.

Objective

If Fitbit proves to be an appropriate clinical tool in a military setting, it could provide potential cost savings, improve clinician access to patient data, and create real-time treatment options for the greater active duty service member population. The purpose of this study was to determine if the Fitbit device can be used to identify indicators of mental health disorders by measuring the relationship between Fitbit sleep data, self-reported mood, and environmental contextual factors that may disrupt sleep.

Methods

This observational cohort study was conducted at the Madigan Army Medical Center. The study included 17 healthy adults who wore a Fitbit Flex for 2 weeks and completed a daily self-reported mood and sleep log. Daily Fitbit data were obtained for each participant. Contextual factors were collected with interim and postintervention surveys. This study had 3 specific aims: (1) Determine the correlation between daily Fitbit sleep data and daily self-reported sleep, (2) Determine the correlation between number of waking events and self-reported mood, and (3) Explore the qualitative relationships between Fitbit waking events and self-reported contextual factors for sleep.

Results

There was no significant difference in the scores for the pre-intevention Pittsburg Sleep Quality Index (PSQI; mean 5.88 points, SD 3.71 points) and postintervention PSQI (mean 5.33 points, SD 2.83 points). The Wilcoxon signed-ranks test showed that the difference between the pre-intervention PSQI and postintervention PSQI survey data was not statistically significant (Z=0.751, P=.05). The Spearman correlation between Fitbit sleep time and self-reported sleep time was moderate (r=0.643, P=.005). The Spearman correlation between number of waking events and self-reported mood was weak (r=0.354, P=.163). Top contextual factors disrupting sleep were “pain,” “noises,” and “worries.” A subanalysis of participants reporting “worries” found evidence of potential stress resilience and outliers in waking events.

Conclusions

Findings contribute valuable evidence on the strength of the Fitbit Flex device as a proxy that is consistent with self-reported sleep data. Mood data alone do not predict number of waking events. Mood and Fitbit data combined with further screening tools may be able to identify markers of underlying mental health disease.

The use of wearable devices in chronic disease management to enhance adherence and improve telehealth outcomes: A systematic review and meta-analysis

INTRODUCTION

Wearable device (WD) interventions are rapidly growing in chronic disease management; nevertheless, the effectiveness of these technologies to monitor telehealth outcomes has not been adequately discussed. This study aims to evaluate the effects of WDs in adherence and other health outcomes for people with chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and cardiac disease (CD).

METHODS

CINAHL, PsycINFO, CENTRAL, and EMBASE were searched for randomized controlled trials (RCTs) and non-RCTs from 1937 to February 2020. Studies comparing interventions with the use of WD were assessed for quality in RCTs and a meta-analysis was performed.

RESULTS

Eleven studies were included in this review. All of the interventions involved WD use with educational support such as goal setting, virtual social support, e-health program, real-time feedback, written information, maintain diary, and text messaging. The meta-analysis showed no difference in adherence (p = .38). The DM group showed effects of more than a 2% reduction in weight when WDs were implemented for three months (risk ratio = 2.20; 95% confidence interval (CI) 1.38 to 3.50; p = .0009), as well as blood glucose (mean difference (MD) = -32.39; 95% CI = -48.07 to -16.72; p < .0001), haemoglobin A_1c (MD = -0.69; 95% CI = -1.28 to -0.10; p = .02), and physical exercise time in the CD group (MD = 9.53; 95% CI = 0.59 to 18.47; p = .04).

DISCUSSION

WD with educational support may be particularly useful for people with DM and CD to enhance support beyond usual care. The results of this review showed insufficient evidence to support the use of WD for COPD to enhance telehealth outcomes for disease management.

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.

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.