Tracking Steps on Apple Watch at Different Walking Speeds

Defining Activity Thresholds Triggering a "Stand Hour" for Apple Watch Users: Cross-Sectional Study

BACKGROUND

Sedentary behavior (SB) is one of the largest contributing factors increasing the risk of developing noncommunicable diseases, including cardiovascular disease and type 2 diabetes. Guidelines from the World Health Organization for physical activity suggest the substitution of SB with light physical activity. The Apple Watch contains a health metric known as the stand hour (SH). The SH is intended to record standing with movement for at least 1 minute per hour; however, the activity measured during the determination of the SH is unclear.

OBJECTIVE

In this cross-sectional study, we analyzed the algorithm used to determine time spent standing per hour. To do this, we investigated activity measurements also recorded on Apple Watches that influence the recording of an SH. We also aimed to estimate the values of any significant SH predictors in the recording of a SH.

METHODS

The cross-sectional study used anonymized data obtained in August 2022 from 20 healthy individuals gathered via convenience sampling. Apple Watch data were extracted from the Apple Health app through the use of a third-party app. Appropriate statistical models were fitted to analyze SH predictors.

RESULTS

Our findings show that active energy (AE) and step count (SC) measurements influence the recording of an SH. Comparing when an SH is recorded with when an SH is not recorded, we found a significant difference in the mean and median AE and SC. Above a threshold of 97.5 steps or 100 kJ of energy, it became much more likely that an SH would be recorded when each predictor was analyzed as a separate entity.

CONCLUSIONS

The findings of this study reveal the pivotal role of AE and SC measurements in the algorithm underlying the SH recording; however, our findings also suggest that a recording of an SH is influenced by more than one factor. Irrespective of the internal validity of the SH metric, it is representative of light physical activity and might, therefore, have use in encouraging individuals through various means, for example, notifications, to reduce their levels of SB.

Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

BACKGROUND

Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques.

OBJECTIVE

The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity.

METHODS

A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models.

RESULTS

Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01).

CONCLUSIONS

The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.

Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review

BACKGROUND

Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders.

METHODS AND RESULTS

In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided.

CONCLUSION

Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.

Comparison of Early Postoperative Step and Stair Counts With the Direct Anterior Approach Versus the Posterior Approach for Total Hip Arthroplasty

BACKGROUND

The purpose of this study was to evaluate the impact of direct anterior approach (DAA) or posterior approach (PA) on step and stair counts after total hip arthroplasty using a remotely monitored mobile application with a smartwatch while controlling for baseline characteristics.

METHODS

This is a secondary data analysis from a prospective cohort study of patients utilizing a smartphone-based care management platform. The primary outcomes were step and stair counts and changes from baseline through one year. Step and stair counts were available for 1,501 and 847 patients, respectively. Longitudinal regression models were created to control for baseline characteristics.

RESULTS

Patients in the DAA group had significantly lower body mass index (P = .049) and comorbidities (P = .028), but there were no significant differences in age (P = .225) or sex (P = .315). The DAA patients had a higher average and improvement from baseline in step count at 2 and 3 weeks postoperatively after controlling for patient characteristics (P = .028 and P = .044, respectively). The average stair counts were higher for DAA patients at one month postoperatively (P = .035), but this difference was not significant after controlling for patient demographics. Average stair ascending speeds and changes from baseline were not different between DAA and PA patients. Descending stair speed was higher at 2 weeks postoperatively for DAA patients, but was no longer higher after controlling for baseline demographics.

CONCLUSIONS

After controlling for baseline characteristics, DAA patients demonstrate earlier improvement in step count than PA patients after total hip arthroplasty. However, patient selection and surgeon training may continue to influence outcomes through a surgical approach.

The impact of the COVID-19 pandemic on physical activity habits at a residential university

OBJECTIVE

To assess changes in physical activity (PA) after a COVID-19 shutdown on a primarily residential university campus.

METHODS

Eighty students, faculty, and staff (FS) of a university (age: 32.2 ± 13.6 yr) who wore a consumer wearable technology (CWT) device completed an anonymous survey by inputting data for 30 days prior to- and 30 days following an academic break in 2020, in which the university transitioned to remote learning.

RESULTS

Steps decreased after spring break in all subjects (p < .001), but steps were impacted to a greater extent in students. 30-day, weekday, and weekend step averages all decreased in students (p < .001). FS were able to maintain their weekend step averages.

CONCLUSIONS

PA decreased in a university community after the COVID-19 shutdown. Students, no longer active transport for campus life, saw a greater impact on their PA. These changes could have an impact on health status.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study

Aims

Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.

Methods and results

We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.

Conclusion

Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

Development of SkinTracker, an integrated dermatology mobile app and web portal enabling remote clinical research studies

Introduction

In-person dermatology clinical research studies often face recruitment and participation challenges due to travel-, time-, and cost-associated barriers. Studies incorporating virtual/asynchronous formats can potentially enhance research subject participation and satisfaction, but few mobile health tools are available to enable remote study conduct. We developed SkinTracker, a patient-facing mobile app and researcher-facing web platform, that enables longitudinal collection of skin photos, patient reported outcomes, and biometric health and environmental data.

Methods

Eight design thinking sessions including dermatologists, clinical research staff, software engineers, and graphic designers were held to create the components of SkinTracker. Following iterative prototyping, SkinTracker was piloted across six adult and four pediatric subjects with atopic dermatitis (AD) of varying severity levels to test and provide feedback on SkinTracker for six months.

Results

The SkinTracker app enables collection of informed consent for study participation, baseline medical history, standardized skin photographs, patient-reported outcomes (e.g., Patient Oriented Eczema Measure (POEM), Pruritus Numerical Rating Scale (NRS), Dermatology Life Quality Index (DLQI)), medication use, adverse events, voice diary to document qualitative experiences, chat function for communication with research team, environmental and biometric data such as exercise and sleep metrics through integration with an Apple Watch. The researcher web portal allows for management and visualization of subject enrollment, skin photographs for examination and severity scoring, survey completion, and other patient modules. The pilot study requested that subjects complete surveys and photographs on a weekly to monthly basis via the SkinTracker app. Afterwards, participants rated their experience in a 7-item user experience survey covering app function, design, and desire for participation in future studies using SkinTracker. Almost all subjects agreed or strongly agreed that SkinTracker enabled more convenient participation in skin research studies compared to an in-person format.

Discussion

To our knowledge, SkinTracker is one of the first integrated app- and web-based platforms allowing collection and management of data commonly obtained in clinical research studies. SkinTracker enables detailed, frequent capture of data that may better reflect the fluctuating course of conditions such as AD, and can be modularly customized for different skin conditions to improve dermatologic research participation and patient access.

Factors affecting resting heart rate in free-living healthy humans

Resting heart rate (RHR) is a potential cardiac disease prevention target because it is strongly associated with cardiac morbidity and mortality, yet community-based monitoring of RHR remains in its infancy. Recently, smartwatches have become available enabling measurement with non-intrusive devices of relationships between RHR and other factors outside the laboratory. We carried out cross-sectional observational retrospective analysis of anonymised smartwatch data obtained by participants in their everyday lives between 2016 and 2021 in a single centre community-based study, using convenience sampling. Between participants, overall RHR means strongly or moderately inversely correlated with means of stand hour (SH), calculated VO₂ max, walking and running distance (WRD), steps and flights climbed (FC). Within participants, in quarterly averages, RHR inversely correlated moderately with frequency of standing (stand hours, SH). RHR also inversely correlated moderately with heart rate variability (HRV), consistent with the known impact of increasing parasympathetic dominance on RHR. These within participant correlations suggest that RHR might be modifiable by changes in SH and HRV within individuals. Indeed, analysing paired daily data, relationships between these three categories were dose dependent. 15 SH versus 5 SH associated with a reduction of 10 beats per minute in mean RHR and increase in mean HRV of 14 ms, respectively. We conclude that within individuals, RHR inversely correlates with frequency of standing and HRV, with paired daily measurements indicating effects are mediated that day. RHR also inversely correlates with fitness and activity measures between participants. Our findings provide initial community-based observational evidence supporting further prospective interventional investigation of frequency of standing or HRV modifiers, alongside more familiar interventions, for cardiac disease prevention.

Leveraging Mobile-Based Sensors for Clinical Research to Obtain Activity and Health Measures for Disease Monitoring, Prevention, and Treatment

Clinical researchers are using mobile-based sensors to obtain detailed and objective measures of the activity and health of research participants, but many investigators lack expertise in integrating wearables and sensor technologies effectively into their studies. Here, we describe the steps taken to design a study using sensors for disease monitoring in older adults and explore the benefits and drawbacks of our approach. In this study, the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare), we created an iOS app to collect data from the Apple Watch's gyroscope, accelerometer, and other sensors; results of cognitive and fitness tests; and participant-entered survey data. We created the study app using ResearchKit, an open-source framework developed by Apple for medical research that includes neuropsychological tests (e.g., of executive function and memory), gait speed, balance, and other health assessments. Data is transmitted via an Application Programming Interface (API) from the app to REDCap for researchers to monitor and analyze in real-time. Employing the lessons learned from GAPcare could help researchers create study-tailored research apps and access timely information about their research participants from wearables and smartphone devices for disease prevention, monitoring, and treatment.

Asthma and technology in emerging African American adults (The ATHENA Project): Protocol for a trial using the Multiphase Optimization Strategy framework (Preprint)

Background

Asthma causes substantial morbidity and mortality in the United States, particularly among African American emerging adults (AAEAs; aged 18-30 years), but very few asthma programs have targeted this population. Interventions that provide education and address underlying motivation for managing asthma may be the most effective. However, intensive face-to-face interventions are often difficult to implement in this population.

Objective

The purpose of this study is to develop an effective mobile asthma management intervention to improve control among AAEAs.

Methods

We will assess the ability of multiple technologic components to assist and improve traditional asthma education. The first component is the Motivational Enhancement System for asthma management. It is a mobile 4-session intervention using supported self-regulation and motivational interviewing. Personalized content is based on each participant’s activity level, daily experiences, and goals. The second component is supportive accountability. It is administered by asthma nurses using targeted mobile support (Skype/voice calls) to provide education, promote self-efficacy, and overcome barriers through a motivational interviewing–based framework. The third component is SMS text messaging. It provides reminders for asthma education, medication adherence, and physical activity. The fourth component is physical activity tracking. It uses wearable technology to help meet user-defined physical activity goals. Using a multiphase optimization strategy (MOST) framework, we will test intervention components and combinations of components to identify the most effective mobile intervention. The MOST framework is an innovative, and cost- and time-effective framework that uses engineering principles to produce effective behavioral interventions. We will conduct a component selection experiment using a factorial research design to build an intervention that has been optimized for maximum efficacy, using a clinically significant improvement in asthma. Participants (N=180) will be randomized to 1 of 6 intervention arms. Participants will be recruited from multiple sites of the American Lung Association-Airway Clinical Research Centers network and ambulatory care clinics at the Detroit Medical Center. Data collections will occur at baseline, and 3, 6, and 12 months.

Results

At study completion, we will have an empirically supported optimized mobile asthma management intervention to improve asthma control for AAEAs. We hypothesize that postintervention (3, 6, and 12 months), participants with uncontrolled asthma will show a clinically significant improvement in asthma control. We also hypothesize that improvements in asthma management behaviors (including physical activity), quality of life, symptoms, adherence, and exacerbation (secondary outcomes) will be observed.

Conclusions

AAEAs are disproportionately impacted by asthma, but have been underrepresented in research. Mobile asthma management interventions may help improve asthma control and allow people to live healthier lives. During this project, we will use an innovative strategy to develop an optimized mobile asthma management intervention using the most effective combination of nurse-delivered asthma education, a smartphone app, and text messaging.

International Registered Report Identifier (IRRID)

PRR1-10.2196/37946

Associations Between Device-Measured Physical Activity and Glycemic Control and Variability Indices Under Free-Living Conditions

Background: Disturbances of glycemic control and large glycemic variability have been associated with increased risk of type 2 diabetes in the general population as well as complications in people with diabetes. Long-term health benefits of physical activity are well documented but less is known about the timing of potential short-term effects on glycemic control and variability in free-living conditions. Materials and Methods: We analyzed data from 85 participants without diabetes from the Food & You digital cohort. During a 2-week follow-up, device-based daily step count was studied in relationship to glycemic control and variability indices using generalized estimating equations. Glycemic indices, evaluated using flash glucose monitoring devices (FreeStyle Libre), included minimum, maximum, mean, standard deviation, and coefficient of variation of daily glucose values, the glucose management indicator, and the approximate area under the sensor glucose curve. Results: We observed that every 1000 steps/day increase in daily step count was associated with a 0.3588 mg/dL (95% confidence interval [CI]: -0.6931 to -0.0245), a 0.0917 mg/dL (95% CI: -0.1793 to -0.0042), and a 0.0022% (95% CI: -0.0043 to -0.0001) decrease in the maximum glucose values, mean glucose, and in the glucose management indicator of the following day, respectively. We did not find any association between daily step count and glycemic indices from the same day. Conclusions: Increasing physical activity level was linked to blunted glycemic excursions during the next day. Because health-related benefits of physical activity can be long to observe, such short-term physiological benefits could serve as personalized feedback to motivate individuals to engage in healthy behaviors.

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Background

Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual.

Objective

Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population.

Methods

We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life.

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study

BACKGROUND

Wearable technology has rapidly entered consumer markets and has health-care potential; however, wearable device data for diverse populations are scarce. We therefore aimed to describe and compare key wearable signals (ie, heart rate, step count, and home blood pressure measurements) across age, sex, race, ethnicity, and clinical phenotypes.

METHODS

In the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) prospective observational study, we enrolled participants from Michigan Medicine, Ann Abor, MI, USA, and followed them up for at least 90 days. Patients were included if they were aged 18 years or older, were fluent in English, owned an iPhone 6 or newer model with a supported iOS version, and had regular access to the internet throughout the study period. All participants were provided with an Apple Watch Series 3 or 4, an Omron Evolv Wireless Blood Pressure Monitor, and the MyDataHelps study smartphone application. Participants were asked to wear their watch for 12 h per day or longer and to do daily or weekly tasks, including home blood pressure measurements and breathing tasks. Heart rate, blood pressure, step counts, and distance walked were collected. The study was divided into two phases: an intensive 45-day collection phase (phase 1); and a 3-year longitudinal monitoring phase (phase 2). Here we report the first 90 days of data for all participants, which includes all of phase 1 and the first 45 days of phase 2. Participants' electronic health records were used to establish clinical diagnoses for analysis.

FINDINGS

We enrolled 6765 eligible participants between Aug 14, 2018, and Dec 19, 2019, of whom 6454 participants from Michigan Medicine completed the phase 1 study protocol and were included in this analysis (3482 [54%] women and 2972 [46%] men; 3657 [57%] participants were White, with 1094 [17%] Asian and 1090 [17%] Black participants). On days when participants wore their smart watches, median daily watch wear time was 15·5 h (IQR 14-17). Participants contributed a total of 1 107 320 blood pressure and 202 198 347 heart rate measurements over 90 days, with 172 (SD 50) blood pressure and 31 329 (SD 24 620) heart rate measurements per participant. Mean systolic blood pressure was 122 mm Hg (SD 10) and mean diastolic blood pressure was 77 mm Hg (SD 8), with 167 312 (15%) measurements having a systolic blood pressure higher than 140 mm Hg or diastolic blood pressure higher than 90 mm Hg. Mean resting heart rate was 64 beats per min (SD 8). Blood pressure and resting heart rate varied by sex, age, race, and ethnicity, with higher blood pressures in males and lower heart rate in participants aged 65 years or older (p<0·0001). Participants took 7511 steps per day (SD 2805) and walked 6009 metres per day (SD 2608), varying across demographic and clinical subgroups.

INTERPRETATION

These data could inform clinical trial design, interpretation of wearable data in clinical practice, and health-care interventions.

FUNDING

Apple, University of Michigan.

Can Older Adult Emergency Department Patients Successfully Use the Apple Watch to Monitor Health?

OBJECTIVE

To determine usability of the Apple Watch in older adult emergency department (ED) patients after a fall.

METHODS

We recruited older adults who fell and visited two urban EDs. They participated in an Apple Watch orientation and interviews on their experiences using the watch to complete varied tasks for 30 days. Interviews were recorded, transcribed, coded, and analyzed using framework analyses.

RESULTS

Eight participants (mean age 77.6 years) enrolled from November 2019 to March 2020. Participants reported being able to apply and charge the watch but struggled with navigating screens, monitoring charging status, and responding with de novo text messages. Many cited difficulties with advanced tasks, such as the study's app-based movement and memory activities. Experience with smartphones and caregiver assistance enhanced users' ability to complete tasks.

CONCLUSIONS

Older adults successfully performed basic Apple Watch functions. Family and community members may be necessary to assist with complex tasks.

Change in outdoor walking behavior during the coronavirus disease pandemic in Japan: A longitudinal study

BACKGROUND

Due to the high infectivity and seriousness of coronavirus disease, people's daily activities were restricted in countries worldwide; governments implemented lockdown measures and advised individuals to perform self-restraint in terms of leaving the house. However, there have been few scientific reports on the effects of such behavioral restrictions on walking parameters.

RESEARCH QUESTION

Did behavioral restrictions during the state of emergency in Japan effect walking parameters in daily life outdoor walking?

METHODS

In this retrospective cohort study, four walking parameters, namely, the average number of steps taken, walking speed, step length, and cadence, were measured using a smartphone application among 3901 participants (mean age ± standard deviation: 60.3 ± 28.9 years) from March 2 to June 15 in both 2019 and 2020. Repeated-measures two-way analysis of variance was used to compare the walking parameters between the two years.

RESULTS

The number of steps significantly decreased (p < .001) in 2020 (∼3400 steps) compared to that in 2019 (∼4400 steps), indicating that the state of emergency greatly affected the amount of physical activity performed per individual. Conversely, walking speed increased (p < .001 during the period when the state of emergency was issued) in 2020 (∼1.25 m/s) compared to that in 2019 (∼1.23 m/s), attributable to an increased step length.

SIGNIFICANCE

Although changes in walking speed and step length were small compared to those in the number of steps, those changes were consistently seen during the state of emergency, suggesting that people tried to walk faster in their outdoor walking. Such change in walking behavior may have protected further deterioration of health due to restricted activity.

Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study

Background

When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP.

Objective

We aimed to study the association of habitual physical activity with home BP.

Methods

Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure.

Results

We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI.

Conclusions

In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Background

Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures.

Objective

The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.

Methods

We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability.

Results

We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.

Conclusions

Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Association of Habitual Physical Activity With Cardiovascular Disease Risk

RATIONALE

A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD.

OBJECTIVE

To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults.

METHODS AND RESULTS

We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; P=0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women (P=1.3×10^-26). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk (P=3.2×10^-4). The association was attenuated but remained significant after further adjustment for body mass index (P=0.01).

CONCLUSIONS

In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.

Walking cadence required to elicit criterion moderate-intensity physical activity is moderated by fitness status

The aims of this study were to estimate the walking cadence required to elicit a VO₂reserve (VO₂R) of 40% and determine if fitness status moderates the relationship between walking cadence and %VO₂R. Twenty participants (10 male, mean(s) age 32(10) years; VO₂max 45(10) mL·kg^-1·min^-1) completed resting and maximal oxygen consumption tests prior to 7 x 5-min bouts of treadmill walking at increasing speed while wearing an Apple Watch and measuring oxygen consumption continuously. The 7 x 5-min exercise bouts were performed at speeds between 3 and 6 km·h^-1 with 5-min seated rest following each bout. Walking cadence measured at each treadmill speed was recorded using the Apple Watch "Activity" app. Using Bayesian regression, we predict that participants need a walking cadence of 138 to 140 steps·min^-1 to achieve a VO₂R of 40%. However, these values are moderated by fitness status such that those with lower fitness can achieve 40% VO₂R at a slower walking cadence. The results suggest that those with moderate fitness need to walk at ~40% higher than the currently recommended walking cadence (100 steps·min^-1) to elicit moderate-intensity physical activity. However, walking cadence required to achieve moderate-intensity physical activity is moderated by fitness status.

Evaluation of Clinical Outcomes and Simultaneous Digital Tracking of Daily Physical Activity, Heart Rate, and Inhalation Behavior in Patients With Pulmonary Arterial Hypertension Treated With Inhaled Iloprost: Protocol for the Observational VENTASTEP Study

BACKGROUND

Pulmonary arterial hypertension (PAH)-a progressive, ultimately fatal disease-patients often experience dyspnea, which can limit their daily physical activities. Iloprost is an inhaled therapy for PAH that has shown efficacy in clinical trials. However, clinical trials in PAH have provided only limited data on daily physical activity. Digital monitoring of daily physical activity in PAH is therefore attracting growing interest. To fully understand a patient's response to treatment, monitoring of treatment adherence is also required. The Breelib nebulizer for administration of iloprost saves inhalation data, thus allowing digital monitoring of adherence.

OBJECTIVE

This study aims to perform parallel digital tracking of daily physical activity parameters, heart rate, and iloprost inhalation data in patients with PAH, before and after starting inhaled iloprost treatment. The primary objective is to investigate correlations between changes in digital measures of daily physical activity and traditional clinical measures. Secondary objectives are to assess iloprost inhalation behavior, the association between daily physical activity measures and time since last inhalation, changes in sleep quality and heart rate, the association of heart rate with daily physical activity measures and iloprost inhalation, and adverse events.

METHODS

VENTASTEP is a digital, prospective, observational, multicenter, single-arm cohort study of adults with PAH in Germany, starting inhaled iloprost treatment via the Breelib nebulizer, in addition to existing PAH therapy. The study comprises a baseline period without iloprost treatment (≤2 weeks) and an observation period with iloprost treatment (3 months±2 weeks). The Apple Watch Series 2 and iPhone 6s are used with a dedicated study app to continuously measure digital daily physical activity parameters and heart rate during the baseline and observation periods; the watch is also used with a 6-min walk distance (6MWD) app to measure digital 6MWD at baseline and the end-of-observation visit. Inhalation frequency, completeness, and duration are monitored digitally via the nebulizer and the BreeConnect app. Sleep quality is assessed using the Pittsburgh Sleep Quality Index at baseline and the end-of-observation visit. Changes in traditional outcome measures (6MWD, Borg dyspnea scale, EuroQol 5-dimensions questionnaire, functional class, and brain natriuretic peptide [BNP] or N-terminal proBNP) between baseline and the end-of-observation visit will be correlated with changes in digital daily physical activity parameters and digital 6MWD as the primary analysis.

RESULTS

The first participant was enrolled in February 2018 (estimated study completion by July 2019; planned sample size: 80 patients).

CONCLUSIONS

The VENTASTEP study will inform future research on the utility of digital parameters as outcome assessment tools for disease monitoring in PAH. The study will also provide insight into clinical outcomes, daily physical activity, and quality of life in patients adding inhaled iloprost, to existing PAH therapy.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03293407; https://clinicaltrials.gov/ct2/show/NCT03293407 (Archived by WebCite at http://www.webcitation.org/6ywPGcn4I).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/12144.

Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review

Wearable devices have recently received considerable interest due to their great promise for a plethora of applications. Increased research efforts are oriented towards a non-invasive monitoring of human health as well as activity parameters. A wide range of wearable sensors are being developed for real-time non-invasive monitoring. This paper provides a comprehensive review of sensors used in wrist-wearable devices, methods used for the visualization of parameters measured as well as methods used for intelligent analysis of data obtained from wrist-wearable devices. In line with this, the main features of commercial wrist-wearable devices are presented. As a result of this review, a taxonomy of sensors, functionalities, and methods used in non-invasive wrist-wearable devices was assembled.