Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis

BACKGROUND

Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study.

METHODS

Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed.

RESULTS

After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count.

SIGNIFICANCE

Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.

Cubic-shaped corylus colurna extract coated Cu2O nanoparticles-based smartphone biosensor for the detection of ascorbic acid in real food samples

Ascorbic acid (AA) is a highly water-soluble organic chemical compound and plays a significant role in human metabolism. For the purpose of food quality monitoring, this study focuses on the development of a smartphone-integrated colorimetric and non-enzymatic electrochemical Corylus Colurna (CC) extract-Cu₂O nanoparticles (Cu₂O NPs) biosensor to detect AA in real food samples. The characterization of the CC-Cu₂O NPs was determined using SEM, SEM/EDX, HRTEM, XRD, FTIR, XPS, TGA, and DSC. The CC-Cu₂O NPs are cubic in shape with an approximate size of 10 nm. According to electrochemical results, the oxidation of AA at the modified electrode exhibited a LOD of 27.92 nmolL^-1 in a wide concentration range of 0.55-22 mmolL^-1. The fabricated digital CC-Cu₂O NPs sensor successfully detected AA in food samples. This strategy provides a nanoplatform to determine the detection of AA in food samples.

Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

BACKGROUND

A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests.

METHODS

In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis.

RESULTS

Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35).

CONCLUSIONS

Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.

Smartphone-based Surface Plasmon Resonance Sensors: a Review

The surface plasmon resonance (SPR) is a phenomenon based on the combination of quantum mechanics and electromagnetism, which leads to the creation of charge oscillations on a metal-dielectric interface. The SPR phenomenon creates a signal which measures refractive index change at the metal-dielectric interface. SPR-based sensors are being developed for real-time and label-free detection of water pollutants, toxins, disease biomarkers, etc., which are highly sensitive and selective. Smartphones provide hardware and software capability which can be incorporated into SPR sensors, enabling the possibility of economical and accurate on-site portable sensing. The camera, screen, and LED flashlight of the smartphone can be employed as components of the sensor. The current article explores the recent advances in smartphone-based SPR sensors by studying their principle, components, application, and signal processing. Furthermore, the general theoretical and practical aspects of SPR sensors are discussed.

Opportunistic Environmental Sensing with Smartphones: a Critical Review of Current Literature and Applications

PURPOSE OF REVIEW

This review sought to summarize recent literature and applications of passive, or opportunistic, mobile sensing in the fields of exposure science in built environment settings; highlight innovative opportunistic sensing systems; and analyze their functionality, significant features, and limitations.

RECENT FINDINGS

Fifty-two papers related to opportunistic environmental sensing from 2009 or later were related to this review, of which 27 were included. An array of applications have emerged in environmental monitoring, employing anywhere from one to six of the phone's on-board sensors. The viability of an application is determined by several key factors: the number and quality of sensors on-board the smartphone; power and processing demand; algorithm complexity; data security; mobile network coverage; reliance on external data sources; minimum number of users required; and degree of user burden when using the application. Some factors are universal, while others are more context-specific. Future research should assess sensing applications based on these factors.

Ergonomic analysis of construction worker's body postures using wearable mobile sensors

Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.

A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera

The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively.