Validity of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Yanez Touzet, Alvaro; Houhou, Tatiana; Rahic, Zerina; Laufer, Ilya; Margetis, Konstantinos; Martin, Allan R; Dea, Nicolas; Ghogawala, Zoher; Kapushesky, Misha; Kotter, Mark R N; Davies, Benjamin M.

doi:10.1101/2023.09.13.23294900

Cited by 5 publications

(6 citation statements)

References 31 publications

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“…Content and construct validity were previously assessed in Yanez Touzet et al (5). In this study, the Stability Score was used as a reflective measure of upper limb stability (5).…”

Section: Movemed and Tasksmentioning

confidence: 94%

“…The construct (upper limb stability) is assessed by measuring the involuntariness, rhythmicity, and oscillation of the upper limbs as continuous variables and analysing them as a multidimensional Stability Score. Content and construct validity were previously assessed in Yanez Touzet et al (5). In this study, the Stability Score was used as a reflective measure of upper limb stability (5).…”

Section: Movemed and Tasksmentioning

confidence: 94%

“…The construct (finger dexterity) is assessed by measuring speed, accuracy, and efficiency of typing as continuous variables and analysing them as a panel of unidimensional measures. Content and construct validity were previously assessed in Yanez Touzet et al (5). In this study, typing speed was used as a reflective measure of finger dexterity.…”

Section: Movemed and Tasksmentioning

confidence: 97%

“…MoveMed, a battery of PerfOM's in a mobile application, recently demonstrated validity in DCM (5). The application leverages the accuracy of mobile sensors to assess hand, arm, and leg function in real-time, in the user's natural environment, and under standardised conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in this study, we use formal methods and criteria from the United States Food and Drug Administration (FDA) and Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) to assess the reliability of MoveMed's PerFOM's. This is the second article in a series of clinimetric studies on the measurement properties of MoveMed's PerfOM's (5).…”

Section: Introductionmentioning

confidence: 99%

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Reliability of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Touzet,

Houhou,

Rahic

et al. 2024

Preprint

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Objectives: To assess the reliability of the MoveMed battery of performance outcome measures. Design: Prospective observational study. Setting: Decentralised secondary care in England, United Kingdom. Participants: Seven adults aged 59.5 (SD 12.4) who live with degenerative cervical myelopathy (DCM) and possess an approved smartphone. Primary and secondary outcome measures: The primary outcome was to determine the test-retest reliability of the MoveMed performance outcomes using the intra-class correlation of agreement (ICCagreement). The secondary outcome was to determine the measurement error of the MoveMed performance outcomes using both the standard error of agreement of the mean (SEMagreement) and the smallest detectable change of agreement (SDCagreement). Criteria from the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) manual were used to determine adequate reliability (i.e., ICCagreement≥ 0.7) and risk of bias. Disease stability was controlled for using two minimum clinically important difference (MCID) thresholds obtained from the literature on the patient-derived modified Japanese Orthopaedic Association (P-mJOA): namely, MCID ≤1 point and MCID ≤2 points. Results: All tests demonstrated moderate-to-excellent test-retest coefficients and low measurement errors. In the MCID ≤1 group, ICCagreementvalues ranged from: 0.84–0.94 in the Fast Tap Test, 0.89–0.95 in the Hold Test, 0.95 in the Typing Test, and 0.98 in the Stand and Walk Test. SEMagreementvalues ranged from ±1 tap, ±1–3% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively. SDCagreementvalues were ±3 taps, ±4–7% stability score points, ±0.2 keys per second, and ±27 steps per minute. In the MCID ≤2 group, ICCagreementvalues ranged from: 0.61–0.91, 0.75–0.77, 0.98, and 0.62, respectively; SEMagreementvalues from: ±1 tap, ±2–4% stability score points, ±0.06 keys per second, and ±10 steps per minute; and SDCagreementvalues from: ±3–7 taps, ±7–10% stability score points, ±0.2 keys per second, and ±27 steps per minute. Furthermore, the Fast Tap, Hold, and Typing Tests obtained sufficient ratings (ICCagreement≥ 0.7) in both MCID≤1 and MCID≤2 groups. No risk of bias factors from the COSMIN Risk of Bias checklist were recorded. Overall, 'very good' quality evidence of sufficient reliability was found for the MoveMed battery in DCM. Conclusions: Criteria from COSMIN provide 'very good' quality evidence of the reliability of the MoveMed tests in an adult population living with DCM.

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“…Content and construct validity were previously assessed in Yanez Touzet et al (5). In this study, the Stability Score was used as a reflective measure of upper limb stability (5).…”

Section: Movemed and Tasksmentioning

confidence: 94%

Section: Movemed and Tasksmentioning

confidence: 94%

Section: Movemed and Tasksmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reliability of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Touzet,

Houhou,

Rahic

et al. 2024

Preprint

Self Cite

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Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study

Touzet,

Rujeedawa,

Munro

et al. 2024

JMIR Form Res

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Background Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging. Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined. Objective This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features. Methods A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics. Results After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: χ22=159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM. Conclusions Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition. However, the challenge lies in creating quality data sets necessary to derive benefit from such approaches.

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Reliability of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Observational Study

Yanez Touzet,

Houhou,

Rahic

et al. 2024

JMIR Form Res

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Background Developing new clinical measures for degenerative cervical myelopathy (DCM) is an AO Spine RECODE-DCM Research, an international and multi-stakeholder partnership, priority. Difficulties in detecting DCM and its changes cause diagnostic and treatment delays in clinical settings and heightened costs in clinical trials due to elevated recruitment targets. Digital outcome measures can tackle these challenges due to their ability to measure disease remotely, repeatedly, and more economically. Objective The aim of this study is to assess the reliability of the MoveMed battery of performance outcome measures. Methods A prospective observational study in decentralized secondary care was performed in England, United Kingdom. The primary outcome was to determine the test-retest reliability of the MoveMed performance outcomes using the intraclass correlation (ICC) of agreement . The secondary outcome was to determine the measurement error of the MoveMed performance outcomes using both the SE of the mean (SEM) of agreement and the smallest detectable change (SDC) of agreement . Criteria from the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) manual were used to determine adequate reliability (ie, ICC of agreement ≥0.7) and risk of bias. Disease stability was controlled using 2 minimum clinically important difference (MCID) thresholds obtained from the literature on the patient-derived modified Japanese Orthopaedic Association (p-mJOA) score, namely, MCID ≤1 point and MCID ≤2 points. Results In total, 7 adults aged 59.5 (SD 12.4) years who live with DCM and possess an approved smartphone participated in the study. All tests demonstrated moderate to excellent test-retest coefficients and low measurement errors. In the MCID ≤1 group, ICC of agreement values were 0.84-0.94 in the fast tap test, 0.89-0.95 in the hold test, 0.95 in the typing test, and 0.98 in the stand and walk test. SEM of agreement values were ±1 tap, ±1%-3% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively. SDC of agreement values were ±3 taps, ±4%-7% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. In the MCID ≤2 group, ICC of agreement values were 0.61-0.91, 0.75-0.77, 0.98, and 0.62, respectively; SEM of agreement values were ±1 tap, ±2%-4% stability score points, ±0.06 keys per second, and ±10 steps per minute, respectively; and SDC of agreement values were ±3-7 taps, ±7%-10% stability score points, ±0.2 keys per second, and ±27 steps per minute, respectively. Furthermore, the fast tap, hold, and typing tests obtained sufficient ratings (ICC of agreement ≥0.7) in both MCID ≤1 and MCID ≤2 groups. No risk of bias factors from the COSMIN Risk of Bias checklist were recorded. Conclusions The criteria from COSMIN provide “very good” quality evidence of the reliability of the MoveMed tests in an adult population living with DCM.

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Validity of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Cited by 5 publications

References 31 publications

Reliability of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Reliability of a smartphone app to objectively monitor performance outcomes in degenerative cervical myelopathy: an observational study

Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study

Reliability of a Smartphone App to Objectively Monitor Performance Outcomes in Degenerative Cervical Myelopathy: Observational Study

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