Locomotion detection and cadence estimation using 3D wrist accelerometer: an in-field validation

Soltani, Abolfazl; Dejnabadi, H.; Fasel, Benedikt; Ionescu, Anisoara; Gubelmann, Cédric; Marques‐Vidal, Pedro; Vollenweiderb, Peter; Aminian, Kamiar

doi:10.1016/j.gaitpost.2017.06.359

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…Indeed, accelerometers allow the collection of objective and continuous PA data and have been tested and validated in older patients [11][12][13][14]. Further, the data collected can be analyzed with algorithms that classify locomotion and non-locomotion periods in everyday life [15]. Still, there is a paucity of studies assessing PA levels by accelerometry in hospitalized older patients [12][13][14][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study

et al. 2020

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Background In a hospital setting and among older patients, inactivity and bedrest are associated with a wide range of negative outcomes such as functional decline, increased risk of falls, longer hospitalization and institutionalization. Our aim was to assess the distribution, determinants and predictors of physical activity (PA) levels using wrist-worn accelerometers in older patients hospitalized with acute medical illness. Methods Observational study conducted from February to November 2018 at an acute internal medicine unit in the University hospital of Lausanne, Switzerland. We enrolled 177 patients aged ≥65 years, able to walk prior to admission. PA during acute hospital stay was continuously recorded via a 3D wrist accelerometer. Clinical data was collected from medical records or by interview. Autonomy level prior to inclusion was assessed using Barthel Index score. PA levels were defined as < 30 mg for inactivity, 30–99 mg for light and ≥ 100 for moderate PA. Physically active patients were defined as 1) being in the highest quartile of time spent in light and moderate PA or 2) spending ≥20 min/day in moderate PA. Results Median [interquartile range - IQR] age was 83 [74–87] years and 60% of participants were male. The median [IQR] time spent inactive and in light PA was 613 [518–663] and 63 [30–97] minutes/day, respectively. PA peaked between 8 and 10 am, at 12 am and at 6 pm. Less than 10% of patients were considered physically active according to definition 2. For both definitions, active patients had a lower prevalence of walking aids and a lower dependency level according to Barthel Index score. For definition 1, use of medical equipment was associated with a 70% reduction in the likelihood of being active: odds ratio (OR) 0.30 [0.10–0.92] p = 0.034; for definition 2, use of walking aids was associated with a 75% reduction in the likelihood of being active: OR = 0.24 [0.06–0.89], p = 0.032. Conclusion Older hospitalized patients are physically active only 10% of daily time and concentrate their PA around eating periods. Whether a Barthel Index below 95 prior to admission may be used to identify patients at risk of inactivity during hospital stay remains to be proven.

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Section: Introductionmentioning

confidence: 99%

Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study

et al. 2020

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“…Indeed, accelerometers allow the collection of objective and continuous PA data and have been tested and validated in older patients (11)(12)(13)(14). Further, the data collected can be analyzed with algorithms that classify locomotion and nonlocomotion periods in everyday life (15). Still, there is a paucity of studies assessing PA levels by accelerometry in hospitalized older patients (12)(13)(14)(16)(17)(18)(19)(20)(21)(22).…”

Section: Introductionmentioning

confidence: 99%

Accelerometry Assessed Physical Activity of Older Adults Hospitalized with Acute Medical Illness - an Observational Study

Tasheva

Kraege

Vollenweider

et al. 2020

Preprint

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Background: In a hospital setting and among older patients, inactivity and bedrest are associated with a wide range of negative outcomes such as functional decline, increased risk of falls, longer hospitalization and institutionalization. Our aim was to assess the distribution, determinants and predictors of physical activity (PA) levels using wrist-worn accelerometers in older patients hospitalized with acute medical illness.Methods: Observational study conducted from February to November 2018 at an acute internal medicine unit in the University hospital of Lausanne, Switzerland. We enrolled 177 patients aged ≥65 years, able to walk prior to admission. PA during acute hospital stay was continuously recorded via a 3D wrist accelerometer. Clinical data was collected from medical records or by interview. Autonomy level prior to inclusion was assessed using Barthel Index score. PA levels were defined as <30 mg for inactivity, 30-99 mg for light and ≥100 for moderate PA. Physically active patients were defined as 1) being in the highest quartile of time spent in light and moderate PA or 2) spending ≥20 minutes/day in moderate PA.Results: Median [interquartile range - IQR] age was 83 [74-87] years and 60% of participants were male. The median [IQR] time spent inactive and in light PA was 613 [518-663] and 63 [30-97] minutes/day, respectively. PA peaked between 8 and 10 am, at 12 am and at 6 pm. Less than 10% of patients were considered physically active according to definition 2. For both definitions, active patients had a lower prevalence of walking aids and a lower dependency level according to Barthel Index score. For definition 1, use of medical equipment was associated with a 70% reduction in the likelihood of being active: odds ratio (OR) 0.30 [0.10-0.92] p=0.034; for definition 2, use of walking aids was associated with a 75% reduction in the likelihood of being active: OR=0.24 [0.06-0.89], p=0.032.Conclusion: Older hospitalized patients are physically active only 10% of daily time and concentrate their PA around eating periods. Whether a Barthel Index below 95 prior to admission may be used to identify patients at risk of inactivity during hospital stay remains to be proven.

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“…However, given our settings and the high number of subjects assessed, it was not feasible to use hand‐counted steps across 1 day. Algorithm, which detect non‐locomotion periods, could be used in future studies …”

Section: Discussionmentioning

confidence: 99%

Concurrent validity of Nokia Go activity tracker in walking and free‐living conditions

Lebleu

Detrembleur

Guebels

et al. 2019

Evaluation Clinical Practice

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Rationale, aims, and objectives:Consumer-based activity trackers aim at quantifying physical activity in a wide range of contexts. Nevertheless, they need to be validated before they are confidently used. This study assessed the concurrent validity of the Nokia Go against reference devices, according to different sensor locations, in two measurement conditions: during a walking task and during a 24-hour free-living condition. Methods:We examined the agreement between devices and between locations in the number of steps and total sleep time by using intraclass correlation coefficient and Bland-Altman method.Results: In the walking task, the agreement is good to excellent for steps between the Nokia Go and the reference device. In the free-living condition, there is a systematic underestimation of steps in comparison with the ActiGraph. Excellent agreement was found between locations. The device worn at the hip indicated the lowest number of steps, and the device located at the dominant wrist indicated the greatest number of steps. Conclusions:There are high discrepancies in step count between devices because of the different types of activities in daily life. The Nokia Go may be confidently used for step counting during pure walking tasks, at different locations. However, the lack of concurrent validity with ActiGraph call for caution regarding their use in daily living conditions.

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Locomotion detection and cadence estimation using 3D wrist accelerometer: an in-field validation

Cited by 6 publications

References 5 publications

Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study

Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study

Accelerometry Assessed Physical Activity of Older Adults Hospitalized with Acute Medical Illness - an Observational Study

Concurrent validity of Nokia Go activity tracker in walking and free‐living conditions

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