Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

Psychometric Characteristics of Smartphone-Based Gait Analyses in Chronic Health Conditions: A Systematic Review

Background: Chronic health conditions frequently result in gait disturbances, impacting quality of life and mobility. Smartphone-based gait analysis has emerged as a promising alternative to traditional methods, offering accessibility, cost effectiveness, and portability. This systematic review evaluates smartphone-based inertial measurement units' validity, reliability, and sensitivity for assessing gait parameters in individuals with chronic conditions. Methods: A comprehensive literature search in Web of Science, PubMed, Google Scholar, and SportDiscus identified 54 eligible studies. Results: Validity was evaluated in 70% of the included studies, with results showing moderate-to-strong associations between smartphone apps and gold-standard systems (e.g., Vicon), particularly for parameters such as gait speed and stride length (e.g., r = 0.42-0.97). However, variability was evident across studies depending on the health condition, measurement protocols, and device placement. Reliability, examined in only 27% of the included studies, displayed a similar trend, with intraclass correlation coefficients (ICCs) ranging from moderate (ICC = 0.53) to excellent (ICC = 0.95) for spatiotemporal parameters. Sensitivity and specificity metrics were explored in 41% and 35% of the included studies, respectively, with several applications achieving over 90% accuracy in detecting gait abnormalities. Feasibility was rated positively in 94% of the included studies, emphasising the practical advantages of smartphones in diverse settings. Conclusions: The findings of this systematic review endorse the clinical potential of smartphones for remote and real-world gait analysis, while highlighting the need for standardised methodologies. Future research should adopt a more comprehensive approach to psychometric evaluation, ensuring that reliability aspects are adequately explored. Additionally, long-term studies are needed to assess the effectiveness of smartphone-based technologies in supporting the personalised treatment and proactive management of chronic conditions.

An Acetylcholine M1 Receptor-Positive Allosteric Modulator (TAK-071) in Parkinson Disease With Cognitive Impairment: A Phase 2 Randomized Clinical Trial

Importance

Fall risk and cognitive impairment are prevalent and burdensome in Parkinson disease (PD), requiring efficacious, well-tolerated treatment.

Objective

To evaluate the safety and efficacy of TAK-071, a muscarinic acetylcholine M1 positive allosteric modulator, in participants with PD, increased fall risk, and cognitive impairment.

Design, Setting, and Participants

This phase 2 randomized double-blind placebo-controlled crossover clinical trial was conducted from October 21, 2020, to February 27, 2023, at 19 sites in the US. Participants included patients aged 40 to 85 years with a diagnosis of PD, with at least 1 fall in the prior 12 months, with a Montreal Cognitive Assessment score of 11 to 26, and receiving stable antiparkinsonian medications and no acetylcholinesterase inhibitors.

Intervention

One-to-one randomization to once-daily oral TAK-071 or placebo for 6 weeks, followed by washout and 6 weeks of crossover treatment.

Main Outcomes and Measures

The primary end point was change from baseline in gait variability (stride time variability [STV]) during a 2-minute walk test with or without cognitive load. The secondary efficacy end point was change from baseline in a cognitive composite score consisting of tests of attention, executive function, and memory.

Results

Among the 54 participants included in the analysis, 45 (83%) were male, mean (SD) age was 69.7 (6.9) years, and median Montreal Cognitive Assessment score was 24 (range, 17-26). After 6 weeks of treatment, the primary outcome was negative: the change from baseline in STV did not differ between participants receiving TAK-071 or placebo, with cognitive load (geometric mean ratio, 1.15; 95% CI, 0.94-1.41; P = .16) or without cognitive load (geometric mean ratio, 1.02; 95% CI, 0.88-1.18; P = .78). TAK-071 improved the secondary efficacy outcome (cognitive composite score) vs placebo. The least squares mean difference of the change from baseline was 0.22 (95% CI, 0.05-0.38; P = .01). Treatment-emergent adverse events occurred in 18 of 49 participants (37%) while receiving placebo and in 19 of 53 (36%) while receiving TAK-071. Four participants (8%) receiving TAK-071 had adverse events resulting in withdrawal of study drug; 4 had gastrointestinal tract adverse events.

Conclusions and Relevance

In this study, in participants with PD, risk for falls, and cognitive impairment, TAK-071 was well-tolerated. The treatment did not improve the primary outcome of gait variability, but did improve cognition compared with placebo. Larger and longer studies in more diverse populations are needed to better understand the safety and efficacy of TAK-071 in broader populations.

Trial Registration

ClinicalTrials.gov Identifier: NCT04334317.

Validity Analysis of Monocular Human Pose Estimation Models Interfaced with a Mobile Application for Assessing Upper Limb Range of Motion

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person's pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were <10° in 8 of 10 analyzed movements. HPE models demonstrated better performance in shoulder flexion and abduction movements while exhibiting unsatisfactory performance in elbow flexion. Challenges such as image perspective distortion, environmental lighting conditions, images in monocular view, and complications in the pose may influence the models' performance. Nevertheless, HPE models show promise in identifying KJPs and facilitating ROM measurements, potentially enhancing convenience and efficiency in assessments. However, their current accuracy for this application is unsatisfactory, highlighting the need for caution when considering automated upper limb ROM measurement with them. The implementation of these models in clinical practice does not diminish the crucial role of examiners in carefully inspecting images and making adjustments to ensure measurement reliability.

Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study

BACKGROUND

Smartphones and wearables are revolutionizing the assessment of cognitive and motor function in neurological disorders, allowing for objective, frequent, and remote data collection. However, these assessments typically provide a plethora of sensor-derived measures (SDMs), and selecting the most suitable measure for a given context of use is a challenging, often overlooked problem.

OBJECTIVE

This analysis aims to develop and apply an SDM selection framework, including automated data quality checks and the evaluation of statistical properties, to identify robust SDMs that describe the cognitive and motor function of people with multiple sclerosis (MS).

METHODS

The proposed framework was applied to data from a cross-sectional study involving 85 people with MS and 68 healthy participants who underwent in-clinic supervised and remote unsupervised smartphone-based assessments. The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. Selected SDMs were then analyzed to establish their reliability, using an intraclass correlation coefficient and minimal detectable change at 95% CI. The convergence of selected SDMs with in-clinic MS functional measures and patient-reported outcomes was also evaluated.

RESULTS

A total of 16 (34%) of the 47 SDMs passed the selection framework. All selected SDMs demonstrated moderate-to-good reliability in remote settings (intraclass correlation coefficient 0.5-0.85; minimal detectable change at 95% CI 19%-35%). Selected SDMs extracted from the smartphone-based cognitive test demonstrated good-to-excellent correlation (Spearman correlation coefficient, |ρ|>0.75) with the in-clinic Symbol Digit Modalities Test and fair correlation with Expanded Disability Status Scale (EDSS) scores (0.25≤|ρ|<0.5). SDMs extracted from the manual dexterity tests showed either fair correlation (0.25≤|ρ|<0.5) or were not correlated (|ρ|<0.25) with the in-clinic 9-hole peg test and EDSS scores. Most selected SDMs from mobility tests showed fair correlation with the in-clinic timed 25-foot walk test and fair to moderate-to-good correlation (0.5<|ρ|≤0.75) with EDSS scores. SDM correlations with relevant patient-reported outcomes varied by functional domain, ranging from not correlated (cognitive test SDMs) to good-to-excellent correlation (|ρ|>0.75) for mobility test SDMs. Overall, correlations were similar when smartphone-based tests were performed in a clinic or remotely.

CONCLUSIONS

Reported results highlight that smartphone-based assessments are suitable tools to remotely obtain high-quality SDMs of cognitive and motor function in people with MS. The presented SDM selection framework promises to increase the interpretability and standardization of smartphone-based SDMs in people with MS, paving the way for their future use in interventional trials.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study

Background

Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non-smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions.

Objective

This study aimed to validate and test the reliability of a smartphone IMU-based gait speed measurement placed in the user's front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking).

Methods

A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat.

Results

The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of -0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds.

Conclusions

The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Health Digital Twins in Life Science and Health Care Innovation

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Validation of gait analysis using smartphones: Reliability and validity

Objective

This study aims to validate the reliability and validity of gait analysis using smartphones in a controlled environment.

Methods

Thirty healthy adults attached smartphones to the waist and thigh, while an inertial measurement unit was fixed at the shank as a reference device; each participant was asked to walk six gait cycles at self-selected low, normal, and high speeds. Thirty-five cerebral small vessel disease patients were recruited to attach the smartphone to the thigh, performing single-task (ST), cognitive dual-task (DT1), and physical dual-task walking (DT2) to obtain gait parameters.

Results

The results from the healthy group indicate that, regardless of whether attached to the thigh or waist, the smartphones calculated gait parameters with good reliability (ICC2,1 > 0.75) across three different walking speeds. There were no significant differences in the gait parameters between the smartphone attached to the thigh and the IMU across all three walking speeds (P > 0.05). However, significant differences were observed between the smartphone at the waist and the IMU during the stance phase, swing phase, stance time, and stride length at high speeds (P < 0.05). At the same time, measurements of other gait parameters were similar (P > 0.05). Patients demonstrated significant differences in the cadence, stride time, stance phase, swing phase, stance time, stride length, and walking speed between ST and DT1 (P < 0.05). Significant differences were observed in the stance phase, swing phase, stride length, and walking speed between ST and DT2 (P < 0.05).

Conclusions

This study demonstrates the feasibility of using built-in smartphone sensors for gait analysis in a controlled environment.

eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software)

BACKGROUND

Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research.

METHODS

Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here.

FINDINGS

A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection.

INTERPRETATION

The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research.

FUNDING

The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

Potential Role of Smartphone Technology in Advancing Work on Neurological Soft Signs with a Focus on Schizophrenia

LEARNING OBJECTIVE AFTER PARTICIPATING IN THIS CME ACTIVITY, THE PSYCHIATRIST SHOULD BE BETTER ABLE TO

• Outline and Identify potential benefits of using neurological soft signs (NSS) as biomarkers of schizophrenia.

ABSTRACT

Since the late 1960s, NSS have been a focus of study across psychiatric illnesses, including depression, bipolar disorder, and schizophrenia in particular. Utilizing these subtle neurological impairments as biomarkers of illness has numerous benefits; NSS offer a direct connection between clinical presentation and neurological functioning, and assessments are cost-effective. However, incongruent measurement scales, confounding variables, and rating system subjectivity have hindered the advancement and scalability of NSS research and clinical implementation. This article provides a brief overview of the literature on NSS as related to schizophrenia, and proposes utilizing smartphone sensing technology to create standardized NSS assessments with objective scoring. Incorporating digital phenotyping into NSS assessment offers the potential to make measurement more scalable, accessible, and directly comparable across locations, cultures, and demographics. We conducted a narrative search in PubMed and APA PsycInfo using the following keywords: neurological soft signs, schizophrenia spectrum disorders, and psychotic illnesses. No date limitations were used. There is no other direct work on NSS and new smartphone methods like digital phenotyping; though, there is related work in neurology. Harnessing advances in smartphone technology could provide greater insight into and further our understanding of specific aspects of the NSS field. For instance, it could help us distinguish trait vs. state markers and better understand how distinct groups of signs may reflect different aspects of psychiatric illness and neurological impairment. In addition, such technology can help advance research on the capabilities of NSS as an effective diagnostic tool.

Digital Biomarker Applications Across the Spectrum of Opioid Use Disorder

Opioid use disorder (OUD) is one of the most pressing public health problems of the past decade, with over eighty thousand overdose related deaths in 2021 alone. Digital technologies to measure and respond to disease states encompass both on- and off-body sensors. Such devices can be used to detect and monitor end-user physiologic or behavioral measurements (i.e. digital biomarkers) that correlate with events of interest, health, or pathology. Recent work has demonstrated the potential of digital biomarkers to be used as a tools in the prevention, risk mitigation, and treatment of opioid use disorder (OUD). Multiple physiologic adaptations occur over the course of opioid use, and represent potential targets for digital biomarker based monitoring strategies. This review explores the current evidence (and potential) for digital biomarkers monitoring across the spectrum of opioid use. Technologies to detect opioid administration, withdrawal, hyperalgesia and overdose will be reviewed. Driven by empirically derived algorithms, these technologies have important implications for supporting the safe prescribing of opioids, reducing harm in active opioid users, and supporting those in recovery from OUD.

Technology-Assisted Cognitive Motor Dual-Task Rehabilitation in Chronic Age-Related Conditions: Systematic Review

BACKGROUND

Cognitive-motor dual-task (CMDT) is defined as the parallel processing of motor (eg, gait) and cognitive (eg, executive functions) activities and is an essential ability in daily life. Older adults living with frailty, chronic conditions (eg, neurodegenerative diseases), or multimorbidity pay high costs during CMDT. This can have serious consequences on the health and safety of older adults with chronic age-related conditions. However, CMDT rehabilitation can provide useful and effective therapies for these patients, particularly if delivered through technological devices.

OBJECTIVE

This review aims to describe the current technological applications, CMDT rehabilitative procedures, target populations, condition assessment, and efficacy and effectiveness of technology-assisted CMDT rehabilitation in chronic age-related conditions.

METHODS

We performed this systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, on 3 databases (Web of Science, Embase, and PubMed). Original articles that were published in English; involved older adults (>65 years) with ≥1 chronic condition and/or frailty; and tested, with a clinical trial, a technology-assisted CMDT rehabilitation against a control condition were included. Risk of bias (Cochrane tool) and the RITES (Rating of Included Trials on the Efficacy-Effectiveness Spectrum) tool were used to evaluate the included studies.

RESULTS

A total of 1097 papers were screened, and 8 (0.73%) studies met the predefined inclusion criteria for this review. The target conditions for technology-assisted CMDT rehabilitation included Parkinson disease and dementia. However, little information regarding multimorbidity, chronicity, or frailty status is available. The primary outcomes included falls, balance, gait parameters, dual-task performance, and executive functions and attention. CMDT technology mainly consists of a motion-tracking system combined with virtual reality. CMDT rehabilitation involves different types of tasks (eg, obstacle negotiation and CMDT exercises). Compared with control conditions, CMDT training was found to be pleasant, safe, and effective particularly for dual-task performances, falls, gait, and cognition, and the effects were maintained at midterm follow-up.

CONCLUSIONS

Despite further research being mandatory, technology-assisted CMDT rehabilitation is a promising method to enhance motor-cognitive functions in older adults with chronic conditions.

Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

This study assessed the concurrent validity and test-retest-reliability of the Apple Health app on iPhone for measuring gait parameters in different age groups. Twenty-seven children, 28 adults and 28 seniors equipped with an iPhone completed a 6-min walk test (6MWT). Gait speed (GS), step length (SL), and double support time (DST) were extracted from the gait recordings of the Health app. Gait parameters were simultaneously collected with an inertial sensors system (APDM Mobility Lab) to assess concurrent validity. Test-retest reliability was assessed via a second iPhone-instrumented 6MWT 1 week later. Agreement of the Health App with the APDM Mobility Lab was good for GS in all age groups and for SL in adults/seniors, but poor to moderate for DST in all age groups and for SL in children. Consistency between repeated measurements was good to excellent for all gait parameters in adults/seniors, and moderate to good for GS and DST but poor for SL in children. The Health app on iPhone is reliable and valid for measuring GS and SL in adults and seniors. Careful interpretation is required when using the Health app in children and when measuring DST in general, as both have shown limited validity and/or reliability.

The age-related contribution of cognitive function to dual-task gait in middle-aged adults in Spain: observations from a population-based study

BACKGROUND

Poor dual-task gait performance is associated with a risk of falls and cognitive decline in adults aged 65 years or older. When and why dual-task gait performance begins to deteriorate is unknown. This study aimed to characterise the relationships between age, dual-task gait, and cognitive function in middle age (ie, aged 40-64 years).

METHODS

We conducted a secondary analysis of data from community-dwelling adults aged 40-64 years that took part in the Barcelona Brain Health Initiative (BBHI) study, an ongoing longitudinal cohort study in Barcelona, Spain. Participants were eligible for inclusion if they were able to walk independently without assistance and had completed assessments of both gait and cognition at the time of analysis and ineligble if they could not understand the study protocol, had any clinically diagnosed neurological or psychiatric diseases, were cognitively impaired, or had lower-extremity pain, osteoarthritis, or rheumatoid arthritis that could cause abnormal gait. Stride time and stride time variability were measured under single-task (ie, walking only) and dual-task (ie, walking while performing serial subtractions) conditions. Dual-task cost (DTC; the percentage increase in the gait outcomes from single-task to dual-task conditions) to each gait outcome was calculated and used as the primary measure in analyses. Global cognitive function and composite scores of five cognitive domains were derived from neuropsychological testing. We used locally estimated scatterplot smoothing to characterise the relationship between age and dual-task gait, and structural equation modelling to establish whether cognitive function mediated the association between observed biological age and dual tasks.

FINDINGS

996 people were recruited to the BBHI study between May 5, 2018, and July 7, 2020, of which 640 participants completed gait and cognitive assessments during this time (mean 24 days [SD 34] between first and second visit) and were included in our analysis (342 men and 298 women). Non-linear associations were observed between age and dual-task performance. Starting at 54 years, the DTC to stride time (β=0·27 [95% CI 0·11 to 0·36]; p<0·0001) and stride time variability (0·24 [0·08 to 0·32]; p=0·0006) increased with advancing age. In individuals aged 54 years or older, decreased global cognitive function correlated with increased DTC to stride time (β=-0·27 [-0·38 to -0·11]; p=0·0006) and increased DTC to stride time variability (β=-0·19 [-0·28 to -0·08]; p=0·0002).

INTERPRETATION

Dual-task gait performance begins to deteriorate in the sixth decade of life and, after this point, interindividual variance in cognition explains a substantial portion of dual-task performance.

FUNDING

La Caixa Foundation, Institut Guttmann, and Fundació Abertis.

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of "Dry" Immersion

Parkinson's disease (PD) is increasingly being studied using science-intensive methods due to economic, medical, rehabilitation and social reasons. Wearable sensors and Internet of Things-enabled technologies look promising for monitoring motor activity and gait in PD patients. In this study, we sought to evaluate gait characteristics by analyzing the accelerometer signal received from a smartphone attached to the head during an extended TUG test, before and after single and repeated sessions of terrestrial microgravity modeled with the condition of "dry" immersion (DI) in five subjects with PD. The accelerometer signal from IMU during walking phases of the TUG test allowed for the recognition and characterization of up to 35 steps. In some patients with PD, unusually long steps have been identified, which could potentially have diagnostic value. It was found that after one DI session, stepping did not change, though in one subject it significantly improved (cadence, heel strike and step length). After a course of DI sessions, some characteristics of the TUG test improved significantly. In conclusion, the use of accelerometer signals received from a smartphone IMU looks promising for the creation of an IoT-enabled system to monitor gait in subjects with PD.

Clinical Review of Smartphone Applications in Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is the second leading neurodegenerative disease worldwide. Important advances in monitoring and treatment have been made in recent years. This article reviews literature on utility of smartphone applications in monitoring PD symptoms that may ultimately facilitate improved patient care, and on movement modulation as a potential therapeutic.

REVIEW SUMMARY

Novel mobile phone applications can provide one-time and/or continuous data to monitor PD motor symptoms in person or remotely, that may support precise therapeutic adjustments and management decisions. Apps have also been developed for medication management and treatment.

CONCLUSIONS

Smartphone applications provide a wide array of platforms allowing for meaningful short-term and long-term data collection and are also being tested for intervention. However, the variability of the applications and the need to translate complicated sensor data may hinder immediate clinical applicability. Future studies should involve stake-holders early in the design process to promote usability and streamline the interface between patients, clinicians, and PD apps.

Validity and Reliability of Smartphone App for Evaluating Postural Adjustments during Step Initiation

The evaluation of anticipatory postural adjustments (APAs) requires high-cost and complex handling systems, only available at research laboratories. New alternative methods are being developed in this field, on the other hand, to solve this issue and allow applicability in clinic, sport and hospital environments. The objective of this study was to validate an app for mobile devices to measure the APAs during gait initiation by comparing the signals obtained from cell phones using the Momentum app with measurements made by a kinematic system. The center-of-mass accelerations of a total of 20 healthy subjects were measured by the above app, which read the inertial sensors of the smartphones, and by kinematics, with a reflective marker positioned on their lumbar spine. The subjects took a step forward after hearing a command from an experimenter. The variables of the anticipatory phase, prior to the heel-off and the step phase, were measured. In the anticipatory phase, the linear correlation of all variables measured by the two measurement techniques was significant and indicated a high correlation between the devices (APAonset: r = 0.95, p < 0.0001; APAamp: r = 0.71, p = 0.003, and PEAKtime: r = 0.95, p < 0.0001). The linear correlation between the two measurement techniques for the step phase variables measured by ques was also significant (STEPinterval: r = 0.56, p = 0.008; STEPpeak1: r = 0.79, p < 0.0001; and STEPpeak2: r = 0.64, p < 0.0001). The Bland−Altman graphs indicated agreement between instruments with similar behavior as well as subjects within confidence limits and low dispersion. Thus, using the Momentum cell phone application is valid for the assessment of APAs during gait initiation compared to the gold standard instrument (kinematics), proving to be a useful, less complex, and less costly alternative for the assessment of healthy individuals.

Impact of walking states, self-reported daily walking amount and age on the gait of older adults measured with a smart-phone app: a pilot study

BACKGROUND

Smartphones provide a cost-effective avenue for gait assessment among older adults in the community. The purpose of this study is to explore the impact of walking state, self-reported daily walking amount, and age on gait quality, using a smartphone application.

METHODS

One hundred older adult individuals from North China, aged 73.0 ± 7.7 years, voluntarily participated in this study. They performed three walking tests: normal walking, fast walking, and visually impaired walking. Three-dimensional acceleration data for gait were obtained using the smartphone app Pocket Gait. This study used multivariate analysis of variance (MANOVA) to explore the effects of the walking state, self-reported daily walking amount, and age on the step frequency, root mean square (RMS) acceleration, step time variability, regularity, and symmetry.

RESULTS

The walking state, self-reported daily walking amount, and age had statistically significant effects on gait quality. Compared with normal walking, the step frequency, RMS acceleration, variability, and regularity were greater in the fast-walking state, and simulated visually impaired walking did not significantly affect gait quality. Relatively older individuals had a significant decline in gait quality compared to (relatively) younger older adult individuals. Compared with older adults who walked less than 1 km a day, older adults who walked more had better gait quality.

CONCLUSIONS

The walking state, self-reported daily walking amount, and age have a significant effect on the gait quality of older adults. Walking with pigmented sunglasses can be used as a training intervention to improve gait performance. Older adult people who walk less than 1 km/day have worse gait quality compared with their counterparts.

Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics

BACKGROUND

Smartphones are increasingly recognized as the future technology for clinical gait assessment.

RESEARCH QUESTION

To determine the concurrent validity of gait parameters obtained using the smartphone technology and application in a group of patients with musculoskeletal pathologies.

METHODS

Patients with knee, lower back, hip, or ankle pain were included in the study (n = 72). Spatiotemporal outcomes were derived from the walkway and the smartphone simultaneously. Pearson's correlations and limits of agreement (LoA) determined the association between the two methods.

RESULTS

Cadence and gait cycle time showed excellent correlation and agreement between the smartphone and the walkway (cadence: r = 0.997, LoA=1.4%, gait cycle time: r = 0.996, LoA = 1.6%). Gait speed, double-limb support and left and right step length demonstrated strong correlations and moderate agreement between methods (gait speed: r = 0.914, LoA=15.4%, left step length: r = 0.842, LoA = 17.0%, right step length: r = 0.800, LoA=16.4%). The left and right measures of single-limb support and stance percent showed a consistent 4% bias across instruments, yielding moderate correlation and very good agreement between the smartphone and the walkway (r = 0.532, LoA = 9% and r = 0.460, LoA=9.8% for left and right single-limb support; r = 0.463, LoA = 5.1% and r = 0.533, LoA = 4.4% for left and right stance).

SIGNIFICANCE

The examined application appears to be a valid tool for gait analysis, providing clinically significant metrics for the assessment of patients with musculoskeletal pathologies. However, additional studies should examine the technology amongst patients with severe gait abnormalities.

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.

Gait and Balance Assessments using Smartphone Applications in Parkinson's Disease: A Systematic Review

Gait dysfunctions and balance impairments are key fall risk factors and associated with reduced quality of life in individuals with Parkinson's Disease (PD). Smartphone-based assessments show potential to increase remote monitoring of the disease. This review aimed to summarize the validity, reliability, and discriminative abilities of smartphone applications to assess gait, balance, and falls in PD. Two independent reviewers screened articles systematically identified through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Studies that used smartphone-based gait, balance, or fall applications in PD were retrieved. The validity, reliability, and discriminative abilities of the smartphone applications were summarized and qualitatively discussed. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies. Thirty-one articles were included in this review. The studies present mostly with low risk of bias. In total, 52% of the studies reported validity, 22% reported reliability, and 55% reported discriminative abilities of smartphone applications to evaluate gait, balance, and falls in PD. Those studies reported strong validity, good to excellent reliability, and good discriminative properties of smartphone applications. Only 19% of the studies formally evaluated the usability of their smartphone applications. The current evidence supports the use of smartphone to assess gait and balance, and detect freezing of gait in PD. More studies are needed to explore the use of smartphone to predict falls in this population. Further studies are also warranted to evaluate the usability of smartphone applications to improve remote monitoring in this population.Registration: PROSPERO CRD 42020198510.