{"doi":"10.2196/46149","title":"Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background</jats:title>\n                    <jats:p>Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, “wearables’ abandonment”).</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective</jats:title>\n                    <jats:p>In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants’ wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining “valid” days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results</jats:title>\n                    <jats:p>For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants’ wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.</jats:p>\n                  </jats:sec>","journal":"JMIR mHealth and uHealth","year":2025,"id":634229,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"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":487988,"name":"Ronald F. Zernicke","orcid":"0000-0003-3898-9507","position":1,"is_corresponding":false},{"id":289203,"name":"Muneesh Tewari","orcid":"0000-0002-7781-3152","position":2,"is_corresponding":false},{"id":361201,"name":"Noelle E. Carlozzi","orcid":"0000-0003-0439-9429","position":3,"is_corresponding":false},{"id":289202,"name":"Sung Won Choi","orcid":"0000-0002-6321-3834","position":4,"is_corresponding":false},{"id":832985,"name":"Stephen M. Cain","orcid":"0000-0003-1961-4195","position":5,"is_corresponding":false},{"id":832980,"name":"Loubna Baroudi","orcid":"0000-0002-3065-6196","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background</jats:title>\n                    <jats:p>Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, “wearables’ abandonment”).</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective</jats:title>\n                    <jats:p>In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants’ wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods</jats:title>\n                    <jats:p>We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining “valid” days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results</jats:title>\n                    <jats:p>For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count.</jats:p>\n                  </jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants’ wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.</jats:p>\n                  </jats:sec>","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"40116717","pmcid":"PMC11951812","openalex_id":"https://openalex.org/W4405630247","authors":[],"funders":[{"funder_name":"NHLBI NIH HHS","grant_id":"R01 HL146354","title":null},{"funder_name":"NINR NIH HHS","grant_id":"R01 NR013658","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"K24 HL156896","title":null},{"funder_name":"NCATS NIH HHS","grant_id":"UL1 TR002240","title":null},{"funder_name":"National Institutes of Health","grant_id":"5R01HL146354-04","title":"Roadmap 2.0: A randomized controlled trial using a technology-mediated platform in family caregivers of BMT patients"},{"funder_name":"National Institutes of Health","grant_id":"1Z01HL000156-01","title":"REGULATION OF GENE EXPRESSION"},{"funder_name":"National Institutes of Health","grant_id":"5R01NR013658-08","title":"Improving Outcomes for Care Partners of Persons with Traumatic Brain Injury"},{"funder_name":"National Institutes of Health","grant_id":"5K24HL156896-04","title":"Patient-Oriented Research and Mentoring in Hematopoietic Cell Transplantation"},{"funder_name":"National Institutes of Health","grant_id":"5UL1TR002240-02","title":"Michigan Institute for Clinical and Health Research (MICHR)"}],"total_grants":9,"fwci":1.9227,"citation_percentile":0.87164036,"influential_citations":0,"citation_trend":[{"year":2025,"count":4},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://mhealth.jmir.org/2025/1/e46149/PDF","host_type":"journal"},{"url":"https://mhealth.jmir.org/2025/1/e46149/PDF","host_type":"publisher"},{"url":"https://mhealth.jmir.org/2025/1/e46149","host_type":"publisher"},{"url":"https://doi.org/10.2196/46149","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/40116717","host_type":"repository"},{"url":"https://doaj.org/article/868f15500ab7456d844f45eb0dec8616","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11951812","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11951812","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11951812?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.2196/46149","host_type":""}],"fields_of_study":["Physical Activity and Health","Context-Aware Activity Recognition Systems","Personal Information Management and User Behavior"],"mesh_terms":["Wearable Electronic Devices","Adolescent","Adult","Female","Humans","Male","Middle Aged","Time Factors"],"keywords":["Preprint","Wearable computer","Smartwatch","Wearable technology","Computer science","Embedded system","World Wide Web","Behavior","Students","reliability","Physical Activity","Engagement","Walking","Caregiver","Mobile Health","Dataset","Users","Wearable Devices","Fitbit","Wear Time","Wearables","Male","Adult","Original Paper","Wearable Electronic Devices","Time Factors","Humans","Female","Information technology","Public aspects of medicine","RA1-1270","Middle Aged","T58.5-58.64"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T13:20:50.489156Z","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":[]}