When a Step Is Not a Step! Specificity Analysis of Five Physical Activity Monitors

Smart Tattoo Sensors 2.0: A Ten-Year Progress Report through a Narrative Review

The increased interest in sensing tattoos reflects a shift in wearable technology, emphasizing their flexible, skin-adherent nature. These devices, driven by advancements in nanotechnology and materials science, offer highly sensitive and customizable sensors. The growing body of research in this area indicates a rising curiosity in their design and applications, with potential uses ranging from vital sign monitoring to biomarker detection. Sensing tattoos present a promising avenue in wearable healthcare technology, attracting attention from researchers, clinicians, and technology enthusiasts. The objective of this study is to analyze the development, application, and integration of the sensing tattoos in the health domain. A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 37 studies. Sensing tattoos hold transformative potential in health monitoring and physiological sensing, driven by their focus on affordability, user-friendly design, and versatile sensorization solutions. Despite their promise, ongoing refinement is essential, addressing limitations in adhesion, signal quality, biocompatibility, and regulatory complexities. Identified opportunities, including non-invasive health monitoring, multiplexed detection, and cost-effective fabrication methods, open avenues for personalized healthcare applications. However, bridging gaps in medical device standards, cybersecurity, and regulatory compliance is imperative for seamless integration. A key theme calls for a holistic, user-centric approach, emphasizing interdisciplinary collaboration. Balancing innovation with practicality, prioritizing ethics, and fostering collaboration are crucial for the evolution of these technologies. The dynamic state of the field is evident, with active exploration of new frontiers. This overview also provides a roadmap, urging scholars, industry players, and regulators to collectively contribute to the responsible integration of sensing tattoos into daily life.

Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a "gold standard" and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

Wearable Device Validity in Measuring Steps, Energy Expenditure, and Heart Rate Across Age, Gender, and Body Mass Index: Data Analysis From a Systematic Review

BACKGROUND

This paper examined whether the criterion validity of step count (SC), energy expenditure (EE), and heart rate (HR) varied across studies depending on the average age, body mass index (BMI), and predominant gender of participants.

METHODS

Data from 1536 studies examining the validity of various wearable devices were used. Separate multilevel regression models examined the associations among age, gender, and BMI with device criterion validity assessed using mean absolute percent error (MAPE) at the study level.

RESULTS

MAPE values were reported in 970 studies for SC, 328 for EE, and 238 for HR, respectively. There were several significant differences in MAPE between age, gender, and BMI categories for SC, EE, and HR. SC MAPE was significantly different for older adults compared with adults. Compared with studies among normal-weight populations, MAPE was greater among studies with overweight samples for SC, HR, and EE. Comparing studies with more women than men, MAPE was significantly greater for EE and HR.

CONCLUSIONS

There are important differences in the criterion validity of commercial wearable devices across studies of varying ages, BMIs, and genders. Few studies have examined differences in error between different age groups, particularly for EE and HR.

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm

Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.

Validity of Fitbit activity monitoring for adults with progressive muscle diseases

PURPOSE

Measuring physical activity informs activity recommendations in clinical practice and provides outcomes in clinical trials that are meaningful to patients. Activity assessment in muscle disease is challenging and there is insufficient evidence to support any single activity measure; however, multi-modal activity measurement might have potential.

MATERIALS AND METHODS

This two-part study included 20 and 95 adults with progressive muscle diseases with mobility ranging from independent to assisted, including wheelchair users. Their activity was measured using a multi-sensor Fitbit activity monitor, for which criterion validity and acceptability were tested in study 1 and validity, reliability, and responsiveness were tested in the longitudinal, home-based study 2.

RESULTS

Study 1: Fitbit was acceptable and had strong criterion validity (rho/kappa ≥0.90), although up to 15% measurement error. Study 2: Fitbit had satisfactory concurrent and construct validity, reliability, and responsiveness. However, Fitbit active minutes registered 75 min more activity per week than gold standard moderate and vigorous physical activity (MVPA) time.

CONCLUSIONS

Fitbit had satisfactory measurement properties for monitoring physical activity in adults with progressive muscle diseases. However, Fitbit should not be considered an exact step counter, heart rate monitor or calorimeter and Fitbit active minutes are not synonymous with MVPA time.Implications for rehabilitationPeople with progressive muscle diseases mobilise independently, with walking aids and with wheelchairs; physical activity measurement can be challenging in this population.Multisensor smart activity monitoring by Fitbit had satisfactory validity, reliability, responsiveness, and acceptability for the estimation of physical activity in adults with progressive muscle diseases.Fitbit active minutes are not synonymous with moderate and vigorous physical activity (MVPA) time measured using a research grade accelerometer.

Step Count in Patients With Lumbar Spinal Stenosis: Accuracy During Walking and Nonwalking Activities

STUDY DESIGN

This is a method development and validation study.

OBJECTIVES

The purpose of this study was to develop and test a method for step detection using accelerometer data in patients with lumbar spinal stenosis (LSS). There are 2 objectives: (1) to describe a method for step detection from accelerations measured at the wrist, hip, lower back, thigh and ankle; (2) to assess the accuracy of the method during walking with and without walking aids and during nonwalking activities.

SUMMARY OF BACKGROUND DATA

Loss of walking ability is one of the main symptoms of LSS, and there is no validated measure to assess walking activity in daily living in patients with LSS.

MATERIALS AND METHODS

Thirty patients with LSS performed a standardized movement protocol that included walking with and without walking aids and performing nonwalking activities while wearing accelerometers on five different wear-sites. After the walking tests, a method was designed for optimal step detection and compared with a gold standard of observed step count.

RESULTS

The method for step detection applied to accelerations from the lower back, hip, thigh, and ankle provided an accurate step counts during continuous walking without walking aids. Accuracy diminished at all wear-sites when walking with walking aids, except the ankle. The wrist provided the most inaccurate step count, and the accelerometers on the thigh and ankle were prone to falsely detecting steps during bicycling.

CONCLUSION

The ankle-worn accelerometer provided the most accurate step count, but wrongly registered steps during nonwalking activities. The developed step detection method shows potential as a measure of walking activity why further development and testing under free-living conditions should be performed.

A Web-Based mHealth Intervention With Telephone Support to Increase Physical Activity Among Pregnant Patients With Overweight or Obesity: Feasibility Randomized Controlled Trial

BACKGROUND

Pregnant patients with overweight or obesity are at high risk for perinatal complications. Excess gestational weight gain (GWG) further exacerbates this risk. Mobile health (mHealth) lifestyle interventions that leverage technology to facilitate self-monitoring and provide just-in-time feedback may motivate behavior change to reduce excess GWG, reduce intervention costs, and increase scalability by improving access.

OBJECTIVE

This study aimed to test the acceptability and feasibility of a pilot mHealth lifestyle intervention for pregnant patients with overweight or obesity to promote moderate intensity physical activity (PA), encourage guideline-concordant GWG, and inform the design of a larger pragmatic cluster randomized controlled trial.

METHODS

We conducted a mixed methods acceptability and feasibility randomized controlled trial among pregnant patients with a prepregnancy BMI of 25 to 40 kg/m2. Patients with singletons at 8 to 15 weeks of gestation who were aged ≥21 years and had Wi-Fi access were recruited via email from 2 clinics within Kaiser Permanente Northern California and randomized to receive usual prenatal care or an mHealth lifestyle intervention. Participants in the intervention arm received wireless scales, access to an intervention website, activity trackers to receive automated feedback on weight gain and activity goals, and monthly calls from a lifestyle coach. Surveys and focus groups with intervention participants assessed intervention satisfaction and ways to improve the intervention. PA outcomes were self-assessed using the Pregnancy Physical Activity Questionnaire, and GWG was assessed using electronic health record data for both arms.

RESULTS

Overall, 33 patients were randomly assigned to the intervention arm, and 35 patients were randomly assigned to the usual care arm. All participants in the intervention arm weighed themselves at least once a week, compared with 20% (7/35) of the participants in the usual care arm. Participants in the intervention arm wore the activity tracker 6.4 days per week and weighed themselves 5.3 times per week, and 88% (29/33) of them rated the program "good to excellent." Focus groups found that participants desired more nutrition-related support to help them manage GWG and would have preferred an app instead of a website. Participants in the intervention arm had a 23.46 metabolic equivalent of task hours greater change in total PA per week and a 247.2-minute greater change in moderate intensity PA per week in unadjusted models, but these effects were attenuated in adjusted models (change in total PA: 15.55 metabolic equivalent of task hours per week; change in moderate intensity PA: 199.6 minutes per week). We found no difference in total GWG (mean difference 1.14 kg) compared with usual care.

CONCLUSIONS

The pilot mHealth lifestyle intervention was feasible, highly acceptable, and promoted self-monitoring. Refined interventions are needed to effectively affect PA and GWG among pregnant patients with overweight or obesity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03936283; https://clinicaltrials.gov/ct2/show/NCT03936283.

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis

BACKGROUND

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

OBJECTIVE

The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

METHODS

We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

RESULTS

A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively).

CONCLUSIONS

Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

Telemonitoring of Real-World Health Data in Cardiology: A Systematic Review

Background: New sensor technologies in wearables and other consumer health devices open up promising opportunities to collect real-world data. As cardiovascular diseases remain the number one reason for disease and mortality worldwide, cardiology offers potent monitoring use cases with patients in their out-of-hospital daily routines. Therefore, the aim of this systematic review is to investigate the status quo of studies monitoring patients with cardiovascular risks and patients suffering from cardiovascular diseases in a telemedical setting using not only a smartphone-based app, but also consumer health devices such as wearables and other sensor-based devices. Methods: A literature search was conducted across five databases, and the results were examined according to the study protocols, technical approaches, and qualitative and quantitative parameters measured. Results: Out of 166 articles, 8 studies were included in this systematic review; these cover interventional and observational monitoring approaches in the area of cardiovascular diseases, heart failure, and atrial fibrillation using various app, wearable, and health device combinations. Conclusions: Depending on the researcher’s motivation, a fusion of apps, patient-reported outcome measures, and non-invasive sensors can be orchestrated in a meaningful way, adding major contributions to monitoring concepts for both individual patients and larger cohorts.

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.

Step and Distance Measurement From a Low-Cost Consumer-Based Hip and Wrist Activity Monitor: Protocol for a Validity and Reliability Assessment

Background

Self-tracking via wearable and mobile technologies is becoming an essential part of personal health management. At this point, however, little information is available to substantiate the validity and reliability of low-cost consumer-based hip and wrist activity monitors, with regard more specifically to the measurements of step counts and distance traveled while walking.

Objective

The aim of our study is to assess the validity and reliability of step and distance measurement from a low-cost consumer-based hip and wrist activity monitor specific in various walking conditions that are commonly encountered in daily life. Specifically, this study is designed to evaluate whether and to what extent validity and reliability could depend on the sensor placement on the human body and the walking task being performed.

Methods

Thirty healthy participants will be instructed to wear four PBN 2433 (Nakosite) activity monitors simultaneously, with one placed on each hip and each wrist. Participants will attend two experimental sessions separated by 1 week. During each experimental session, two separate studies will be performed. In study 1, participants will be instructed to complete a 2-minute walk test along a 30-meter indoor corridor under 3 walking speeds: very slow, slow, and usual speed. In study 2, participants will be required to complete the following 3 conditions performed at usual walking speed: walking on flat ground, upstairs, and downstairs. Activity monitor measured step count and distance values will be computed along with the actual step count (determined from video recordings) and distance (measured using a measuring tape) to determine validity and reliability for each activity monitor placement and each walking condition.

Results

Participant recruitment and data collection began in January 2020. As of June 2020, we enrolled 8 participants. Dissemination of study results in peer-reviewed journals is expected in spring 2021.

Conclusions

To the best of our knowledge, this is the first study that examines the validity and reliability of step and distance measurement during walking using the PBN 2433 (Nakosite) activity monitor. Results of this study will provide beneficial information on the effects of activity monitor placement, walking speed, and walking tasks on the validity and reliability of step and distance measurement. We believe such information is of utmost importance to general consumers, clinicians, and researchers.

International Registered Report Identifier (IRRID)

DERR1-10.2196/21262

Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network

Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a 'basic' and 'advanced' multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative 'gold-standard' criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.

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.

Walking Recognition in Mobile Devices

Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.

Comparison of Step Count Assessed Using Wrist- and Hip-Worn Actigraph GT3X in Free-Living Conditions in Young and Older Adults

Background: Walking represents a major component of physical activity (PA), and its restriction could degrade autonomy and quality of life. An important objective for preventive and/or rehabilitative strategies to improve balance and gait in normal and pathological aging conditions is to focus on physical activity. Activity monitors have recently been getting increasingly popular and represent a modern solution to measure—and communicate—PA notably in terms of steps/day. These activity monitors are well-suited for various populations as they can be worn on a variety of locations on the body, including the wrist and the hip (i.e., the two most common locations), in an undifferentiated way according to the manufacturer's instruction. The aim of this study was hence to verify potential differences in step count (SC) by comparing this parameter assessed using wrist- and hip-worn activity trackers over a 24-h period in free-living conditions in young and older adults.

Methods: Young adults (n = 22) and older adults (n = 22) voluntarily participated in this study. They were required to wear two commercially-available Actigraph GT3X+ activity monitors simultaneously at two locations recommended by the manufacturer, i.e., one positioned around the wrist and one above the hip, over a 24-h period in free-living conditions. The manufacturer's software was used to obtain estimates of the SC.

Results: For both groups, the wrist-worn activity tracker provided significantly higher SC than the hip-worn activity tracker did. For both placements on the body, older adults exhibited significantly lower SC than young adults. Interestingly, for both young and older participants, the difference between both measurements tended to decrease for longer distances.

Conclusion: The different estimations of the step count provided by the comparison between two identical Actigraph GT3x on the wrist or the hip during the 24-h observation period in free-living conditions in young and older adults strongly suggests that caution is needed when using total step per day values as an outcome to quantify walking behavior. Probably we can suggest the same caution across implementation of different activity Tracker.

Validity Evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in Free-Living Environments in an Older Adult Cohort

Background

Few studies have investigated the validity of mainstream wrist-based activity trackers in healthy older adults in real life, as opposed to laboratory settings.

Objective

This study explored the performance of two wrist-worn trackers (Fitbit Charge 2 and Garmin vivosmart HR+) in estimating steps, energy expenditure, moderate-to-vigorous physical activity (MVPA) levels, and sleep parameters (total sleep time [TST] and wake after sleep onset [WASO]) against gold-standard technologies in a cohort of healthy older adults in a free-living environment.

Methods

Overall, 20 participants (>65 years) took part in the study. The devices were worn by the participants for 24 hours, and the results were compared against validated technology (ActiGraph and New-Lifestyles NL-2000i). Mean error, mean percentage error (MPE), mean absolute percentage error (MAPE), intraclass correlation (ICC), and Bland-Altman plots were computed for all the parameters considered.

Results

For step counting, all trackers were highly correlated with one another (ICCs>0.89). Although the Fitbit tended to overcount steps (MPE=12.36%), the Garmin and ActiGraph undercounted (MPE 9.36% and 11.53%, respectively). The Garmin had poor ICC values when energy expenditure was compared against the criterion. The Fitbit had moderate-to-good ICCs in comparison to the other activity trackers, and showed the best results (MAPE=12.25%), although it underestimated calories burned. For MVPA levels estimation, the wristband trackers were highly correlated (ICC=0.96); however, they were moderately correlated against the criterion and they overestimated MVPA activity minutes. For the sleep parameters, the ICCs were poor for all cases, except when comparing the Fitbit with the criterion, which showed moderate agreement. The TST was slightly overestimated with the Fitbit, although it provided good results with an average MAPE equal to 10.13%. Conversely, WASO estimation was poorer and was overestimated by the Fitbit but underestimated by the Garmin. Again, the Fitbit was the most accurate, with an average MAPE of 49.7%.

Conclusions

The tested well-known devices could be adopted to estimate steps, energy expenditure, and sleep duration with an acceptable level of accuracy in the population of interest, although clinicians should be cautious in considering other parameters for clinical and research purposes.

The use of wearable technology to monitor physical activity in patients with COPD: a literature review

Background: Physical activity is an important predictor for survival in patients with COPD. Wearable technology, such as pedometer or accelerometer, may offer an opportunity to quantify physical activity and evaluate related health benefits in these patients.

Objectives: To assess the performance of wearable technology in monitoring and improving physical activity in COPD patients from published studies.

Methods: Literature search of Medline, Cochrane, Dare, Embase and PubMed databases was made to find relevant articles that used wearable technology to monitor physical activity in COPD patients.

Results: We identified 13 studies that used wearable technology, a pedometer or an accelerator, to monitor physical activity in COPD patients. Of these, six studies were randomized controlled trials (RCTs) which used the monitors as part of the intervention. Two studies reported the same outcomes and comparable units. They had measured the difference that the intervention makes on the number of steps taken daily by the patients. The results were highly heterogeneous with I²=92%. The random-effects model gave an effect outcome on the number of steps taken daily of 1,821.01 [−282.71; 3,924.74] in favor of the wearable technology. Four of the 13 studies have reported technical issues with the use of the wearable technology, including high signal-to-noise ratio, memory storage problems and inaccuracy of counts. While other studies did not mention any technical issues, it is not clear whether these did not experience them or chose not to report them.

Conclusions: Our literature search has shown that data on the use of wearable technology to monitor physical activity in COPD patients are limited by the small number of studies and their heterogeneous study design. Further research and better-designed RCTs are needed to provide reliable results before physical activity monitors can be implemented routinely for COPD patients.

Objectively measured 24-hour activity profiles before and after total hip arthroplasty

AIMS

The purpose of this exploratory study was to investigate if the 24-hour activity profile (i.e. waking activities and sleep) objectively measured using wrist-worn accelerometry of patients scheduled for total hip arthroplasty (THA) improves postoperatively.

PATIENTS AND METHODS

A total of 51 THA patients with a mean age of 64 years (24 to 87) were recruited from a single public hospital. All patients underwent THA using the same surgical approach with the same prosthesis type. The 24-hour activity profiles were captured using wrist-worn accelerometers preoperatively and at 2, 6, 12, and 26 weeks postoperatively. Patient-reported outcomes (Hip Disability and Osteoarthritis Outcome Score (HOOS)) were collected at all timepoints except two weeks postoperatively. Accelerometry data were used to quantify the intensity (sedentary, light, moderate, and vigorous activities) and frequency (bouts) of activity during the day and sleep efficiency. The analysis investigated changes with time and differences between Charnley class.

RESULTS

Patients slept or were sedentary for a mean of 19.5 hours/day preoperatively and the 24-hour activity pattern did not improve significantly postoperatively. Outside of sleep, the patients spent their time in sedentary activities for a mean of 620 minutes/day (sd 143) preoperatively and 641 minutes/day (sd 133) six months postoperatively. No significant improvements were observed for light, moderate, and vigorous intensity activities (p = 0.140, p = 0.531, and p = 0.407, respectively). Sleep efficiency was poor (< 85%) at all timepoints. There was no postoperative improvement in sleep efficiency when adjusted for medications (p > 0.05). Patient-reported outcome measures showed a significant improvement with time in all domains when compared with preoperative levels. There were no differences with Charnley class at six months postoperatively. However, Charnley class C patients were more sedentary at two weeks postoperatively when compared with Charnley class A patients (p < 0.05). There were no further differences between Charnley classifications.

CONCLUSION

This study describes the 24-hour activity profile of THA patients for the first time. Prior to THA, patients in this cohort were inactive and slept poorly. This cohort shows no improvement in 24-hour activity profiles at six months postoperative. Cite this article: Bone Joint J 2019;101-B:415-425.

Effects of increased daily physical activity on mental health and depression biomarkers in postmenopausal women

[Purpose] Little is known about the effectiveness of daily physical activity on depression biomarkers in older adults. This study aimed to investigate the effects of increased daily physical activity for 8 weeks on depression biomarkers in postmenopausal women. [Participants and Methods] Thirty-eight postmenopausal females were randomly assigned into a control or an active group and were asked to wear a uniaxial accelerometer for 8 weeks. Blood samples were obtained at baseline and at the end of the intervention. During the intervention, the active group was asked to increase their physical activity level above their usual lifestyle whereas those in the control group maintained their daily lifestyle. [Results] After the 8-week intervention, the step counts of the participants in the active group increased. The serum concentration of the brain-derived neurotrophic factor and serotonin increased significantly in the active group, but not in the control group, as compared with baseline values. The serum concentration of derivatives of reactive oxygen metabolites and biological antioxidant potential did not change after the intervention in either group. [Conclusion] These findings may suggest that promotion of daily physical activity in postmenopausal women has a positive impact on depression without any change in oxidative stress.

Robust Step Counting for Inertial Navigation with Mobile Phones

Mobile phones are increasingly used for purposes that have nothing to do with phone calls or simple data transfers, and one such use is indoor inertial navigation. Nevertheless, the development of a standalone application able to detect the displacement of the user starting only from the data provided by the most common inertial sensors in the mobile phones (accelerometer, gyroscope and magnetometer), is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the mobile phone can experience and which have nothing to do with the physical displacement of the owner. In our case, we describe a proposal, which, after using quaternions and a Kalman filter to project the sensors readings into an Earth Centered inertial reference system, combines a classic Peak-valley detector with an ensemble of SVMs (Support Vector Machines) and a standard deviation based classifier. Our proposal is able to identify and filter out those segments of signal that do not correspond to the behavior of “walking”, and thus achieve a robust detection of the physical displacement and counting of steps. We have performed an extensive experimental validation of our proposal using a dataset with 140 records obtained from 75 different people who were not connected to this research.

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Background

Although designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions.

Objective

The objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings.

Methods

We conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search. We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability. We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation. We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy.

Results

We included 67 studies. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings. Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations. Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition. Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation. However, further accuracy studies are warranted. Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined.

Conclusions

Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement.

Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution. An initial barrier is the development of accurate and easily-scalable platforms. In this study, we made a customized smartphone app and used mobile health devices to track PWV, blood pressure, heart rate, physical activity, sleep duration, and multiple lifestyle risk factors in ≈250 adults for 17 continual weeks. Eligible participants were identified by a company database and then were consented and enrolled using only a smartphone app, without any special training given. Study participants reported high overall satisfaction, and 73% of participants were able to measure blood pressure and PWV, <1 hour apart, for at least 14 of 17 weeks. The study population's blood pressure, PWV, heart rate, activity levels, sleep duration, and the interrelationships among these measurements were found to closely match either population averages or values obtained from studies performed in a controlled setting. As a proof-of-concept, we demonstrated the accuracy and ease, as well as many challenges, of using mHealth technology to accurately track PWV and new cardiovascular vital signs at home.

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.

Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study

Background

Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA).

Objective

The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population.

Methods

This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire.

Results

A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10.

Conclusions

Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases.

Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns

In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30–40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50).