Validity of the accelerometer and smartphone application in estimating energy expenditure in individuals with chronic stroke

The effect of multiple nursing interventions on physical activity and health promotion in the elderly: A randomized controlled trial

Health promotion and physical activity practices for the elderly are important but still not sufficient. This study aimed to determine if the effects of education through home visit, mobile application (Google Fit), group walking, and phone reminder interventions on health promotion and physical activity in the elderly. The study has a randomized controlled design with pretest-posttest interventions and a control group. The study sample consisted of 66 elderly individuals, 33 of whom were assigned to the intervention group and 33 to the control group by randomization. Multiple nursing interventions, including education through home visit, mobile application (Google Fit) use, group walking, and phone reminder messages, were implemented by the researcher in line with Pender's Health Promotion Model. The first month score of the intervention group participants on the healthy lifestyle habits subscale was higher than that of the control group participants (all p-values < 0.05). The effect size was 0.577 (high effect size) and the confidence interval was 0.085-1.07. It can be recommended that interventions with reminders and repetitions regarding healthy lifestyle behaviors to health promotion in the elderly be planned.

Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review

BACKGROUND

People with physical disabilities and/or chronic diseases tend to have an inactive lifestyle. Monitoring physical activity levels is important to provide insight on how much and what types of activities people with physical disabilities and/or chronic diseases engage in. This information can be used as input for interventions to promote a physically active lifestyle. Therefore, valid and reliable physical activity measurement instruments are needed. This scoping review aims 1) to provide a critical mapping of the existing literature and 2) directions for future research on measurement properties of device-based instruments assessing physical activity behavior in ambulant adults with physical disabilities and/or chronic diseases.

METHODS

Four databases (MEDLINE, CINAHL, Web of Science, Embase) were systematically searched from 2015 to April 16th 2023 for articles investigating measurement properties of device-based instruments assessing physical activity in ambulatory adults with physical disabilities and/or chronic diseases. For the majority, screening and selection of eligible studies were done in duplicate. Extracted data were publication data, study data, study population, device, studied measurement properties and study outcome. Data were synthesized per device.

RESULTS

One hundred three of 21566 Studies were included. 55 Consumer-grade and 23 research-grade devices were studied on measurement properties, using 14 different physical activity outcomes, in 23 different physical disabilities and/or chronic diseases. ActiGraph (n = 28) and Fitbit (n = 39) devices were most frequently studied. Steps (n = 68) was the most common used physical activity outcome. 97 studies determined validity, 11 studies reliability and 6 studies responsiveness.

CONCLUSION

This scoping review shows a large variability in research on measurement properties of device-based instruments in ambulatory adults with physical disabilities and/or chronic diseases. The variability highlights a need for standardization of and consensus on research in this field. The review provides directions for future research.

Smartphone App in Stroke Management: A Narrative Updated Review

The spread of smartphones and mobile-Health (m-health) has progressively changed clinical practice, implementing access to medical knowledge and communication between doctors and patients. Dedicated software called Applications (or Apps), assists the practitioners in the various phases of clinical practice, from diagnosis to follow-up and therapy management. The impact of this technology is even more important in diseases such as stroke, which are characterized by a complex management that includes several moments: primary prevention, acute phase management, rehabilitation, and secondary prevention. This review aims to evaluate and summarize the available literature on Apps for the clinical management of stroke. We described their potential and weaknesses, discussing potential room for improvement. Medline databases were interrogated for studies concerning guideline-based decision support Apps for stroke management and other medical scenarios from 2007 (introduction of the first iPhone) until January 2022. We found 551 studies. Forty-three papers were included because they fitted the scope of the review. Based on their purpose, Apps were classified into three groups: primary prevention Apps, acute stroke management Apps, and post-acute stroke Apps. We described the aim of each App and, when available, the results of clinical studies. For acute stroke, several Apps have been designed with the primary purpose of helping communication and sharing of patients' clinical data among healthcare providers. However, interactive systems Apps aiming to assist clinicians are still lacking, and this field should be developed because it may improve stroke patients' management.

Establishing Accelerometer Cut-Points to Classify Walking Speed in People Post Stroke

While accelerometers could be used to monitor important domains of walking in daily living (e.g., walking speed), the interpretation of accelerometer data often relies on validation studies performed with healthy participants. The aim of this study was to develop cut-points for waist- and ankle-worn accelerometers to differentiate non-ambulation from walking and different walking speeds in people post stroke. Forty-two post-stroke persons wore waist and ankle accelerometers (ActiGraph GT3x+, AG) while performing three non-ambulation activities (i.e., sitting, setting the table and washing dishes) and while walking in self-selected and brisk speeds. Receiver operating characteristic (ROC) curve analysis was used to define AG cut-points for non-ambulation and different walking speeds (0.41−0.8 m/s, 0.81−1.2 m/s and >1.2 m/s) by considering sensor placement, axis, filter setting and epoch length. Optimal data input and sensor placements for measuring walking were a vector magnitude at 15 s epochs for waist- and ankle-worn AG accelerometers, respectively. Across all speed categories, cut-point classification accuracy was good-to-excellent for the ankle-worn AG accelerometer and fair-to-excellent for the waist-worn AG accelerometer, except for between 0.81 and 1.2 m/s. These cut-points can be used for investigating the link between walking and health outcomes in people post stroke.

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.

Does Google Fit provide valid energy expenditure measurements of functional tasks compared to those of Fibion accelerometer in healthy individuals? A cross-sectional study

BACKGROUND AND AIMS

Smartphone applications (e.g., Google Fit) may be a good alternative tool for accelerometers in estimating energy expenditure of physical activities because they are affordable, easy to use, and freely downloadable on smartphones. We aimed to determine the concurrent validity of the Fibion and Google Fit for measuring energy expenditure of functional tasks in healthy individuals.

METHODS

In this cross-sectional study, 28 healthy individuals (21.25 ± 1.84 years) performed certain tasks (lying, standing, 6-min walk test, treadmill walking, stair climbing and cycling) for ∼90 min, while wearing a Fibion accelerometer on their thigh and having the Google Fit application in a smartphone placed in their trouser pocket. Concurrent validity between the energy expenditure data of the Google Fit and Fibion was assessed using the Spearman rho correlation coefficient (data were not normally distributed), Bland-Altman plots and linear regression.

RESULTS

Neither energy expenditure for the whole duration nor for the tasks, except sitting + treadmill walking (r = 0.419, p = 0.027), showed significant correlations between the Google Fit and Fibion measurements. A proportional bias was evident for almost all comparisons.

CONCLUSIONS

The Google Fit did not provide valid energy expenditure measurements compared to the Fibion for most of the investigated tasks in healthy individuals.

Comparison of Energy Expenditure Assessed Using Wrist- and Hip-Worn ActiGraph GT3X in Free-Living Conditions in Young and Older Adults

The World Health Organization has presented their recommendations for energy expenditure to improve public health. Activity trackers do represent a modern solution for measuring physical activity, particularly in terms of steps/day and energy expended in physical activity (active energy expenditure). According to the manufacturer's instructions, these activity trackers can be placed on different body locations, mostly at the wrist and the hip, in an undifferentiated manner. The objective of this study was to compare the absolute error rate of active energy expenditure measured by a wrist-worn and hip-worn ActiGraph GT3X+ over a 24-h period in free-living conditions in young and older adults. Over the period of a 24-h period, 22 young adults and 22 older adults were asked to wear two ActiGraph GT3X+ at two different body locations recommended by the manufacturer, namely one around the wrist and one above the hip. Freedson algorithm was applied for data analysis. For both groups, the absolute error rate tended to decrease from 1,252 to 43% for older adults and from 408 to 46% for young participants with higher energy expenditure. Interestingly, for both young and older adults, the wrist-worn ActiGraph provided a significantly higher values of active energy expenditure (943 ± 264 cal/min) than the hip-worn (288 ± 181 cal/min). Taken together, these results suggest that caution is needed when using active energy expenditure as an activity tracker-based metric to quantify physical activity.

mHealth technologies used to capture walking and arm use behavior in adult stroke survivors: a scoping review beyond measurement properties

PURPOSE

We aimed to provide a critical review of measurement properties of mHealth technologies used for stroke survivors to measure the amount and intensity of functional skills, and to identify facilitators and barriers toward adoption in research and clinical practice.

MATERIALS AND METHODS

Using Arksey and O'Malley's framework, two independent reviewers determined eligibility and performed data extraction. We conducted an online consultation survey exercise with 37 experts.

RESULTS

Sixty-four out of 1380 studies were included. A majority reported on lower limb behavior (n = 32), primarily step count (n = 21). Seventeen studies reported on arm-hand behaviors. Twenty-two studies reported metrics of intensity, 10 reported on energy expenditure. Reliability and validity were the most frequently reported properties, both for commercial and non-commercial devices. Facilitators and barriers included: resource costs, technical aspects, perceived usability, and ecological legitimacy. Two additional categories emerged from the survey: safety and knowledge, attitude, and clinical skill.

CONCLUSIONS

This provides an initial foundation for a field experiencing rapid growth, new opportunities and the promise that mHealth technologies affords for envisioning a better future for stroke survivors. We synthesized findings into a set of recommendations for clinicians and clinician-scientists about how best to choose mHealth technologies for one's individual objective.Implications for RehabilitationRehabilitation professionals are encouraged to consider the measurement properties of those technologies that are used to monitor functional locomotor and object-interaction skills in the stroke survivors they serve.Multi-modal knowledge translation strategies (research synthesis, educational courses or videos, mentorship from experts, etc.) are available to rehabilitation professionals to improve knowledge, attitude, and skills pertaining to mHealth technologies.Consider the selection of commercially available devices that are proven to be valid, reliable, accurate, and responsive to the targeted clinical population.Consider usability and privacy, confidentiality and safety when choosing a specific device or smartphone application.

Quantification of energy expenditure during daily living activities after stroke by multi-sensor

Objective: To explore the validity of energy expenditure estimates using the SenseWear Armband during a sequence of four daily living activities in patients post-stroke. Method: Patients with stroke who were able to walk during 6 min without human assistance were asked to wear the SenseWear Armband on the non-paretic arm while performing transfers, a manual task, walking, and walking up and down stairs. The energy expenditure estimated using the SenseWear Armband was compared to the energy expenditure calculated from oxygen consumption, measured by a portable indirect calorimeter (Metamax 3B). The mean of energy expenditure was pooled for each task. Accuracy was explored by mean bias (MB) of Bland-Altman analysis and root mean square error (RMSE), agreement by 95% of limits of agreement (95%LoA) and coefficient of correlation (r). Results: Thirty-eight participants (65.7 ± 13.5 years) were included. The SenseWear Armband globally underestimated energy expenditure, MB = 9.77 kcal for the whole sequence. RMSE were large, accounting for 15% to 41% of the measured energy expenditure. Agreement was low with r < 0.70 and 95%LoA from 42% to 93% of the measured energy expenditure. Conclusions: This study reported a global underestimation and a low level of agreement of the energy expenditure estimated by SenseWear Armband in four daily living activities in patients after stroke. Abbreviations: EE: Energy Expenditure; NIHSS: National Institute of Health Stroke Score.