Validity of the Samsung Phone S Health application for assessing steps and energy expenditure during walking and running: Does phone placement matter?

Lifelog-based daily step counts, walking speed, and metabolically healthy status

Objective

Optimal metabolically healthy status is important to prevent various chronic diseases. This study investigated the association between lifelog-derived physical activity and metabolically healthy status.

Methods

This cross-sectional study included 51 Korean adults aged 30-40 years with no history of chronic diseases. Physical activity data were obtained by the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Lifelog-derived physical activity was defined by step counts and walking speed for 1 week, as recorded by the Samsung Health application on both the Samsung Galaxy Fit2 and mobile phones. Participants without metabolic syndrome components were categorized as the metabolically healthy group (n = 31) and the remaining participants as the metabolically unhealthy group (n = 20). Prevalence ratios and 95% confidence intervals were estimated using Poisson regression models. The predictive ability of each physical activity measure was evaluated according to the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) values.

Results

Among the physical activity measures, lifelog-derived walking speed was significantly inversely associated with prevalent metabolically unhealthy status. The lifelog component model including walking speed, age, and sex had the highest AUC value for metabolically unhealthy status. Adding lifelog-derived step counts to the IPAQ-SF-derived metabolic equivalent (MET) model (including age, sex, and IPAQ-SF-METs) yielded 37% and 13% increases in the NRI and IDI values, respectively. Incorporating walking speed into the IPAQ-SF-derived MET model improved metabolically unhealthy status prediction by 42% and 21% in the NRI and IDI analyses, respectively.

Conclusions

Slow walking speed derived from the lifelog was associated with a higher prevalence of metabolically unhealthy status. Lifelog-derived physical activity information may aid in identifying individuals with metabolic abnormalities.

Predictors of physical activity behavior change among patients with heart failure enrolled in home-based cardiac rehabilitation intervention

BACKGROUND

Physical activity behavior change is considered one of the most challenging lifestyle modifications in patients with heart failure. Even after participation in a cardiac rehabilitation program, most patients do not engage in the recommended level of physical activity.

OBJECTIVE

To determine which baseline demographic, physical activity levels, psychological distress, and clinical variables predicted physical activity behavior change to increasing light-to-vigorous physical activity by 10,000 steps/day following participation in home-based cardiac rehabilitation intervention.

METHODS

A prospective design involving secondary analysis was used to analyze data obtained from 127 patients (mean, 61; range, 45-69 years) enrolled in and completed an 8-week home-based mobile health app intervention. The intervention was designed to encourage health behavior change with regard to decreasing sedentary behavior and increasing physical activities performed at light or greater intensities.

RESULTS

None of the participants accumulated 10,000 steps or more per day pre-intervention (mean, 1549; range, 318-4915 steps/day). Only 55 participants (43%) achieved an average daily step count of 10,000 or more at week 8 of the intervention (10,674 ± 263). The results of the logistic regression showed that higher pre-intervention physical activity levels and anxiety symptoms and lower depressive symptoms were associated with a higher likelihood of achieving physical activity behavior change (p < .003).

CONCLUSION

These data highlight that determining pre-intervention physical activity levels and depressive symptoms can be the key to designing an effective home-based cardiac rehabilitation intervention in patients with heart failure.

Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network

BACKGROUND

Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy.

OBJECTIVES

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 network was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables and smartphones in the estimation of EE.

METHODS

The recommendations were developed through (1) a systematic literature review; (2) an unstructured review of the wider literature discussing the potential factors that may introduce bias during validation studies; and (3) evidence-informed expert opinions from members of the INTERLIVE network.

RESULTS

The systematic literature review process identified 1645 potential articles, of which 62 were deemed eligible for the final dataset. Based on these studies and the wider literature search, a validation framework is proposed encompassing six key domains for validation: the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis.

CONCLUSIONS

The INTERLIVE network recommends that the proposed protocol, and checklists provided, are used to standardise the testing and reporting of the validation of any consumer wearable or smartphone device to estimate EE. This in turn will maximise the potential utility of these technologies for clinicians, researchers, consumers, and manufacturers/developers, while ensuring transparency, comparability, and replicability in validation.

TRIAL REGISTRATION

PROSPERO ID: CRD42021223508.

Validity of mHealth devices for counting steps in individuals with Parkinson's disease

BACKGROUND

Step quantification is a good way to characterize the mobility and functional status of individuals with some functional disorder. Therefore, a validation study may lead to the feasibility of devices to stimulate an increase in the number of steps and physical activity level of individuals with Parkinson's Disease (PD).

AIM

To investigate the validity of mHealth devices to estimate the number of steps in individuals with PD and compare the estimate with a standard criterion measure.

METHOD

An observational study in a university laboratory with 34 individuals with idiopathic PD. The number of steps was measured using mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit INC®), and compared against a criterionstandard measure during the Two-Minute Walk Test using habitual speed.

RESULTS

Our sample was 82% men with a Hoehn and Yahr mean of 2.3 ± 1.3 and mean walking speed of 1.2 ± 0.2 m/s. Positive and statistically significant associations were found between Google Fit (r = 0.92; p < 0.01), STEPZ (r = 0.91; p < 0.01), Pacer (r = 0.77; p < 0.01), Health (r = 0.54; p < 0.01), and Fitbit Inc® (r = 0.82; p < 0.01) with the criterion-standard measure.

CONCLUSIONS

GoogleFit, STEPZ, Fitbit Inc.®, Pacer, and Health are valid instruments to measure the number of steps over a given period of time with moderate to high correlation with the criterion-standard in individuals with PD. This result shows that technology such as smartphone applications and activity monitor can be used to assess the number of steps in individuals with PD, and allows the possibility of using this technology for assessment and intervention purposes.

Proposed objective scoring algorithm for walking performance, based on relevant gait metrics: the Simplified Mobility Score (SMoS™)-observational study

Background

Walking is a fundamental part of living, and its importance is not limited by age or medical status. Reduced walking speed (WS), or gait velocity, is a sign of advancing age, various disease states, cognitive impairment, mental illness and early mortality. Activity levels, as defined in the literature as “daily step count” (DSC), is also a relevant measure of health status. A deterioration in our walking metrics, such as reduced WS and DSC, is associated with poor health outcomes. These objective measures are of such importance, that walking speed has been dubbed “the 6th vital sign”. We report a new objective measure that scores walking using the relevant metrics of walking speed and daily step count, into an easy-to-understand score from 0 (nil mobility) to 100 (excellent mobility), termed the Simplified Mobility Score (SMoS™). We have provided equal weighting to walking speed and daily step count, using a simple algorithm to score each metric out of 50.

Methods

Gait data was collected from 182 patients presenting to a tertiary hospital spinal unit with complaints of pain and reduced mobility. Walking speed was measured from a timed walk along an unobstructed pathway. Daily step count information was obtained from patients who had enabled step count tracking on their devices. The SMoS of the sample group were compared to expected population values calculated from the literature using 2-tailed Z tests.

Results

There were significantly reduced SMoS in patients who presented to the spinal unit than those expected at each age group for both genders, except for the 50–59 age bracket where no statistically significant reduction was observed. Even lower scores were present in those that went on to have surgical management. There was a significant correlation of SMoS scores with subjective disability scores such as the Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) in this cohort.

Conclusions

The SMoS is a simple and effective scoring tool which is demonstrably altered in spinal patients across age and gender brackets and correlates well with subjective disability scores. The SMoS has the potential to be used as a screening tool in primary and specialised care settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02546-8.

The Use of Samsung Health and ECG M-Trace Base II Applications for the Assessment of Exercise Tolerance in the Secondary Prevention in Patients after Ischemic Stroke

Background and objectives: The aim of the study was to use the mobile application Samsung Health for the assessment of parameters of exercise tolerance and the ECG (electrocardiogram) M-Trace Base II for the assessment of cardiological parameters. Materials and Methods: The measurements were conducted during rest and after performing SMWT (Six Minute Walk Test) and SCT (Stair Climb Test) in 26 patients after ischemic stroke (IS) and 26 healthy individuals. Results: In the SMWT, the post-stroke group (SG) walked a shorter distance (p < 0.001), achieving lower mean gait velocity (p < 0.001) and lower maximum gait velocity (p = 0.002). In the SCT, SG achieved a lower mean gait velocity (p < 0.001) and lower maximum gait velocity (p < 0.001) when compared to the control group (CG). In SG, myocardial ischemia in ECG was noted in four patients after SMWT and in three patients following SCT. Both in SG and in CG the increase in SBP (systolic blood pressure) value measured after SMWT and SCT compared to at rest (p < 0.001) was observed. In SG, in the compared ratios rest to SMWT and SCT as well as SMWT to SCT, there was an increase in HR (heart rate) (p < 0.001). Conclusions: ECG M-Trace Base II and Samsung Health are mobile applications that can assess cardiological parameters and exercise tolerance parameters in patients after IS, so they can be used to plan the intensity of exercise in rehabilitation programs.

Factors associated with validity of consumer-oriented wearable physical activity trackers: a meta-analysis

The purposes of this study were to examine (1) the strength of the criterion validity evidence of various consumer-oriented wearable physical activity trackers, (2) the influence of brands of consumer-oriented wearable physical activity on validity evidence and (3) factors that may contribute to differences in the strength of the criterion validity evidence. A total of 589 articles were identified through four databases. Pairs of researchers reviewed the articles to determine eligibility. A total of 29 studies with 96 validity coefficients were included in the meta-analysis. Five different moderators, including the brands of physical activity trackers, placement of devices, type of activities (ambulatory vs. lifestyle activities), population, and release year, were analysed to examine which factors impact the validity evidence. The summarised validity coefficient between activity trackers and energy expenditure ranged from r = .41 to r = .91. Moderator analyses revealed that the brand, placement of the device, and population significantly impact the magnitude of the validity evidence, while the type of activity and release year of the devices do not. Device brand, population, andplacement are each factor that significantly affects the validity coefficientsbetween consumer-oriented wearable physical activity trackers. Efforts should be made to improve the accuracy of these devices to maintain the credibility of the research and the trust of consumers.

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.

An investigation into the validity and reliability of mHealth devices for counting steps in chronic stroke survivors

Objective:

To investigate the validity and test–retest reliability of mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra) to estimate the number of steps in individuals after chronic stroke and to compare whether the measurement of the number of steps is affected by their location on the body (paretic and non-paretic side).

Design:

Observational study with repeated measures.

Setting:

University laboratory.

Subjects:

Fifty-five community-dwelling individuals with chronic stroke.

Intervention:

Not applicable.

Main measures:

The number of steps was measured using mHealth devices (Google Fit, Health, STEPZ, Pacer, and Fitbit Ultra), and compared against criterion-standard measure during the Two-Minute Walk Test using habitual speed.

Results:

Our sample was 54.5% men, mean age of 62.5 years (SD 14.9) with a chronicity after stroke of 66.8 months (SD 55.9). There was a statistically significant association between the actual number of steps and those estimated by the Google Fit, STEPZ Iphone and Android applications, Pacer iphone and Android, and Fitbit Ultra (0.30 ⩽  r ⩾ 0.80). The Pacer iphone application demonstrated the highest reliability coefficient (ICC(2,1) = 0.80; P < 0.001). There were no statistically significant differences in device measurements that depended on body location.

Conclusions:

mHealth devices (Pacer–iphone, Fitbit Ultra, Google Fit, and Pacer–Android) are valid and reliable for step counting in chronic stroke survivors. Body location (paretic or non-paretic side) does not affect validity or reliability of the step count metric.

An observational study investigating the use of patient-owned technology to quantify physical activity in survivors of critical illness

BACKGROUND

Physical activity after intensive care unit (ICU) discharge is challenging to measure but could inform research and practice. A patient's smartphone may provide a novel method to quantify physical activity.

OBJECTIVES

We aimed to evaluate the feasibility and accuracy of using smartphone step counts among survivors of critical illness.

METHODS

We performed a prospective observational cohort study in 50 patients who had an ICU length of stay>48 h, owned a smartphone, were ambulatory before admission, and were likely to attend follow-up at 3 and 6 months after discharge. At follow-up, daily step counts were extracted from participants' smartphones and two FitBit pedometers, and exercise capacity (6-min walk test) and quality of life (European Quality of Life-5 Dimensions) were measured.

RESULTS

Thirty-nine (78%) patients returned at 3 months and 33 (66%) at 6 months, the median [interquartile range] smartphone step counts being 3372 [1688-5899] and 2716 [1717-5994], respectively. There was a strong linear relationship, with smartphone approximating 0.71 (0.58, 0.84) of FitBit step counts, P < 0.0001, R-squared = 0.87. There were weak relationships between step counts and the 6-min walk test distance.

CONCLUSION

Although smartphone ownership and data acquisition limit the viability of using extracted smartphone steps at this time, mean daily step counts recorded using a smartphone may act as a surrogate for a dedicated pedometer; however, the relationship between step counts and other measures of physical recovery remains unclear.

Validity of the "Samsung Health" application to measure steps: A study with two different samsung smartphones

The purpose of this study was to examine the validity of a highly popular pedometer application (Samsung Health). Sixteen adults (28.8 ± 8.9 years of age) wore two Samsung smartphone models, Samsung Galaxy Core Prime (SGCP) and Samsung Galaxy S4 (SGS4), at three body locations (waist, arm, and hand) while walking and running over a 50-m test. All trials were recorded using a video as a gold standard measure of step counts. Results indicated that the validity of Samsung Health varied depending on the smartphone model, its body location, and the type of gait (walking and running). Samsung Health showed acceptable validity when the SGCP was located on the hand (Bias = -8.3%; RMSE = 5.6), and especially on the arm (Bias = -7.2%; RMSE = 4.9) while running, and when the SGS4 was located on the arm (Bias = -7.5%; RMSE = 5.4), and especially on the waist (Bias = 5.4%; RMSE = 3.7) while walking. Samsung Health only showed good validity when the SGS4 was located on the arm (Bias = 2.9%; RMSE = 3.6), and especially on the hand (Bias = 0.5%; RMSE = 2.5) while running. This application showed unacceptable validity in the remaining options.