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

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.

Effects of a Mobilization Program Applied to Bariatric Surgery Patients on Preventing Gastrointestinal Complications: a Quasi-Experimental Study

PURPOSE

To investigate the effects of a planned early targeted mobilization program applied to patients that underwent bariatric surgery with the laparoscopic sleeve gastrectomy method on gastrointestinal complications (nausea-vomiting, abdominal distention, delayed flatus-defecation, and intolerance of early oral intake).

MATERIALS AND METHODS

This prospective, controlled group, quasi-experimental design study was conducted between July 2019 and March 2020 in the general surgery clinic of a training and research hospital with 70 patients who underwent sleeve gastrectomy and met the inclusion criteria. The prepared mobilization program was applied to the patients on the 0th, 1st, and 2nd postoperative days, and the gastrointestinal functions of the patients were monitored.

RESULTS

The intervention group had a significantly shorter time to first flatus, defecation, and oral intake; higher frequency of defecation; lower pain, abdominal distention, and nausea; better tolerance of oral intake; and higher total oral intake compared to the control group (p < 0.05).

CONCLUSION

Planned, early, and targeted mobilization was determined to be a feasible, safe, and cost-effective nursing intervention to prevent gastrointestinal complications in patients undergoing sleeve gastrectomy.

Smartphone Pedometer Sensor Application for Evaluating Disease Activity and Predicting Comorbidities in Patients with Rheumatoid Arthritis: A Validation Study

Smartphone-based pedometer sensor telemedicine applications could be useful for measuring disease activity and predicting the risk of developing comorbidities, such as pulmonary or cardiovascular disease, in patients with rheumatoid arthritis (RA), but the sensors have not been validated in this patient population. The aim of this study was to validate step counting with an activity-tracking application running the inbuilt Android smartphone pedometer virtual sensor in patients with RA. Two Android-based smartphones were tested in a treadmill test-bed setup at six walking speeds and compared to manual step counting as the gold standard. Guided by a facilitator, the participants walked 100 steps at each test speed, from 2.5 km/h to 5 km/h, wearing both devices simultaneously in a stomach pouch. A computer automatically recorded both the manually observed and the sensor step count. The overall difference in device step counts versus the observed was 5.9% mean absolute percentage error. Highest mean error was at the 2.5 km/h speed tests, where the mean error of the two devices was 18.5%. Both speed and cadence were negatively correlated to the absolute percentage error, which indicates that the greater the speed and cadence, the lower the resulting step counting error rate. There was no correlation between clinical parameters and absolute percentage error. In conclusion, the activity-tracking application using the inbuilt Android smartphone pedometer virtual sensor is valid for step counting in patients with RA. However, walking at very low speed and cadence may represent a challenge.

[Digital geriatric self-assessment-A narrative review]

BACKGROUND

Digital health apps have a large potential for autonomous screening and monitoring of older people with respect to maintaining their independence. Due to demographic change and the shortage of specialized personnel in medicine, these premedical self-assessment apps could be of great value in the future.

OBJECTIVE

This narrative review enables the assessment of whether a digital geriatric self-assessment for older people ≥ 70 years is feasible using currently available apps.

MATERIAL AND METHODS

A search was carried out for apps that enable a self-assessment in the following domains: physical capacity, cognition, emotion, nutrition, sensory perception and context factors. Based on predefined criteria apps were selected and presented.

RESULTS

Self-assessment apps could be identified in four of the six domains: physical capacity, cognition, emotion and sensory perception. In total five apps are presented as examples. No apps were identified regarding nutrition and context factors. Numerous self-assessment apps were identified for the field of physical activity.

CONCLUSION

The presented results indicate that digital self-assessment can currently be realized for certain domains of the comprehensive geriatric assessment. New promising apps are currently under development. More research is needed to verify test quality criteria and usability of available apps. Furthermore, there is a need for a platform that integrates individual assessment apps to provide users with an overview of the results and recommendations.

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.

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.

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.

Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

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.