{"doi":"10.2196/48270","title":"Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study","abstract":"<jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device–based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P&lt;.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of &gt;0.75 by 16 days of wear time.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.</jats:p>\n                  </jats:sec>","journal":"JMIR Human Factors","year":2023,"id":647239,"datarank":0.40620753016533157,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.0,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1686176,"name":"Sooyoon Shin","orcid":"0000-0002-0339-4856","position":1,"is_corresponding":false},{"id":463625,"name":"Poulami Barman","orcid":"0000-0002-4604-9868","position":2,"is_corresponding":false},{"id":559148,"name":"Erin Rainaldi","orcid":"0000-0003-1082-7055","position":3,"is_corresponding":false},{"id":1686179,"name":"Sara Popham","orcid":"0000-0002-1203-5724","position":4,"is_corresponding":false},{"id":1686181,"name":"Ritu Kapur","orcid":"0000-0003-3488-9963","position":5,"is_corresponding":false},{"id":1686175,"name":"Nathan Kowahl","orcid":"0000-0002-7049-4793","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study","abstract":"<jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>Mobility is a meaningful aspect of an individual’s health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective</jats:title>\n                    <jats:p>Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device–based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P&lt;.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of &gt;0.75 by 16 days of wear time.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":2.70805020110221,"endowment":2.70805020110221,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37535417","pmcid":"PMC10436116","openalex_id":"https://openalex.org/W4381548324","authors":[],"funders":[],"total_grants":0,"fwci":7.8984,"citation_percentile":0.97475922,"influential_citations":0,"citation_trend":[{"year":2023,"count":2},{"year":2024,"count":7},{"year":2025,"count":2},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://humanfactors.jmir.org/2023/1/e48270/PDF","host_type":"journal"},{"url":"https://humanfactors.jmir.org/2023/1/e48270/PDF","host_type":"publisher"},{"url":"https://humanfactors.jmir.org/2023/1/e48270","host_type":"publisher"},{"url":"https://doi.org/10.2196/48270","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37535417","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10436116","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC10436116","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC10436116?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Balance, Gait, and Falls Prevention","Context-Aware Activity Recognition Systems","Physical Activity and Health"],"mesh_terms":[],"keywords":["Suite","Reliability (semiconductor)","Wearable computer","Key (lock)","Computer science","Preferred walking speed","Wearable technology","Wrist","Physical medicine and rehabilitation","Human–computer interaction","Embedded system","Computer security","Medicine","Geography","Sensors","Measurements","Measurement","Sensor","Mobility","Gait","reliability","Accuracy","Walking","Step","Wearable","Walk","Wearables","Digital Measurements","Walking Patterns","Wrist-worn","Mobility Measurements"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T17:33:56.742948Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}