Google fit smartphone application or Gt3X Actigraph: Which is better for detecting the stepping activity of individuals with stroke? A validity study

Polese, Janaíne Cunha; Faria, Giselle Silva e; Ribeiro-Samora, Giane Amorim; Lima, Lorena Pereira; Faria, Christina Danielli Coelho de Morais; Scianni, Aline Alvim; Teixeira-Salmela, Luci Fuscaldi

doi:10.1016/j.jbmt.2019.01.011

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…Number of steps was measured by mHealth devices, as smartphone applications (Google Fit, Health, STEPZ, and Pacer), and wearable sensors (Fitbit Inc ® ), and criterion-standard measure (steps counted live and videotaped—for comparison—by two trained researchers) during a Two-Minute Walk Test with habitual speed. 9,19 Both iOS and Android OS were chosen as operating systems.…”

Section: Methodsmentioning

confidence: 99%

“…Fitbit Inc ® and Google Fit are already known to be accurate for step measurement in individuals after stroke. 9,19,20 To the best of our knowledge, there are no previous studies which have investigated the accuracy of Pacer, Health, and STEPZ for step measurement after stroke. The Health device was previously validated only in non-disabled individuals.…”

Section: Methodsmentioning

confidence: 99%

“…The position of the monitors is illustrated in Supplemental Figure S1; the order of measurement was randomized. Prior to the test, the smartphones were positioned in the front pockets of the individuals’ paretic and/or non-paretic lower limb 9,22 and the gender, body mass (Kg), and height (cm) of the individuals were input to the software system. The first test was performed with the Android OS phone (S480 Positivo) and Fitbit Inc ® on the paretic side, and the iOS phone (iphone 6s ® —iOS) on the non-paretic side.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the use of a step-by-step activity monitoring program can be useful to increase activity outside of a clinical facility in individuals after chronic stroke. 8,9…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the use of a step-by-step activity monitoring program can be useful to increase activity outside of a clinical facility in individuals after chronic stroke. 8,9 The Statement from the American Heart Association 6 recommends investigation of the measurement properties of commercially available smartphone applications and wearable sensors, since this technology is largely already in use. However, there are few studies that have investigated the validity and reliability of mHealth devices in individuals after stroke.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Therefore, the use of a step-by-step activity monitoring program can be useful to increase activity outside of a clinical facility in individuals after chronic stroke. 8,9…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Cloud-Based Healthcare Architecture for Diabetes Patients Using Machine Learning

Xhaferra,

Ismaili,

Chaushi

2023

Economic Recovery, Consolidation, and Sustainable Growth

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Fatigue Assessment with Visualizations of Patient-Generated Data: An Evaluation with Informatics-Savvy Healthcare Professionals

Guardado,

Holappa,

Isomursu

2024

Communications in Computer and Information Science

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Severe and chronic fatigue is a prevalent symptom in multiple chronic conditions. Its complexity, its multifaceted nature and its varied manifestations across different conditions require a nuanced approach for accurate assessment by healthcare professionals. In our research, informatics-savvy public health nurses from a Digital Health Services and Health Promotion Master’s program evaluated various visualizations of patient-generated health data which could potentially be collected through a mobile app designed for people with Multiple Sclerosis. The data visualization prototypes could be a tool to support fatigue assessment and effective communication during consultations and their design was based on prior suggestions from healthcare professionals with experience in fatigue assessment. The patient-generated health data represented in the prototypes comprised a combination of fatigue-related factors and physical activity tracked by Google Fit. This study presents the recommendations of the participants regarding various aspects linked to the visualizations of patient-generated health data, including their utility in the clinical setting, the most suitable types of data summaries, usability aspects and the possibility of meaningful interrelations between distinct types of data. The results of our study emphasize the importance of well-designed data visualizations to support healthcare professionals in decision-making and to improve patient participation in the chronic care process. The iterative design process of the prototypes ensures that the final visualizations have proper usability and the potential to become clinically relevant, and instrumental in the effective assessment of fatigue in chronic management.

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Google fit smartphone application or Gt3X Actigraph: Which is better for detecting the stepping activity of individuals with stroke? A validity study

Cited by 25 publications

References 20 publications

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

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

Cloud-Based Healthcare Architecture for Diabetes Patients Using Machine Learning

Fatigue Assessment with Visualizations of Patient-Generated Data: An Evaluation with Informatics-Savvy Healthcare Professionals

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