{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/390857"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/390857","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Understanding Temporal Movement Behaviour Patterns: Measurement, Epidemiology, and Cardiometabolic Health Implications","abstract":"The UK and WHO guidelines highlight the importance of regular physical activity and reducing sedentary time but provide limited guidance regarding the temporal patterning of these movement behaviours. Wearable devices have enabled researchers to explore such movement patterns at a time-resolution not possible using self-reported data. However, the substantial and growing number of metrics and methods used to study movement patterns have not been consolidated, making the field difficult to navigate. Additionally, the independent role of movement patterns in cardiometabolic health, beyond total physical activity energy expenditure (PAEE), remains unclear. This thesis aimed to: (1) review and categorise metrics and methods used to describe temporal movement behaviour patterns measured by wearables, and to develop a structured framework around them; (2) describe and quantify the movement behaviour complexity of UK adults using wearable data from the Fenland Study; (3) investigate cross-sectional and longitudinal associations between movement behaviour complexity and the risk of cardiometabolic disease. The first part of my research involved a systematic review which identified and compared metrics and methods used to describe wearable-measured temporal movement behaviour patterns. Searches across four databases yielded 19,060 records, of which 1,962 studies met inclusion criteria. From these, 143 standalone metrics and methods were identified and mapped onto a newly developed taxonomy consisting of four stages and nine nodes. This framework provides researchers with a structured approach for selecting and comparing metrics to study temporal movement behaviour patterns. Using a selection of metrics identified from the systematic review, I assessed movement behaviour complexity in ~12,000 participants from the Fenland Study (Phase 1/Baseline) using minute-level accelerometry and heart rate data. Complexity was quantified using information entropy, sample entropy, and Lempel-Ziv complexity, which capture the predictability, regularity, diversity, and temporal structure of movement sequences. Descriptive analyses showed that higher complexity was observed in more active individuals, while lower complexity was associated with female sex, older age, higher BMI, and sedentary occupations. Sociodemographic associations persisted even after adjusting for PAEE, indicating that complexity captures distinct aspects of movement behaviour beyond overall activity levels. Cross-sectional (~12,000 participants) and longitudinal (~7,000 participants) multivariable regression analyses examined associations between complexity metrics and risk factors for cardiometabolic disease. Before adjusting for PAEE, higher complexity was associated with lower risk. However, after adjusting for PAEE, results showed that higher Lempel-Ziv complexity (more diverse movement sequences) and lower sample entropy (greater regularity) were associated with lower risk, indicating that structured variability in movement patterns may be favourable for cardiometabolic health. Prospective analyses supported these findings, although no significant associations were observed between changes in complexity metrics and changes in cardiometabolic risk over time. This thesis consolidates and structures the study of temporal movement behaviour patterns through a new taxonomy and inventory of metrics and methods. Findings from the Fenland cohort suggest that movement behaviour complexity varies by sex, age, BMI, and occupation and is independently associated with cardiometabolic health. Specifically, maintaining a structured yet varied movement pattern – characterised by lower sample entropy and higher Lempel-Ziv complexity – was linked to better cardiometabolic outcomes. These findings suggest that complexity metrics have an added value in advancing our understanding of movement behaviour and its implications for health, potentially informing future research and the design of targeted interventions.","abstract_html":"The UK and WHO guidelines highlight the importance of regular physical activity and reducing sedentary time but provide limited guidance regarding the temporal patterning of these movement behaviours. Wearable devices have enabled researchers to explore such movement patterns at a time-resolution not possible using self-reported data. However, the substantial and growing number of metrics and methods used to study movement patterns have not been consolidated, making the field difficult to navigate. Additionally, the independent role of movement patterns in cardiometabolic health, beyond total physical activity energy expenditure (PAEE), remains unclear. This thesis aimed to: (1) review and categorise metrics and methods used to describe temporal movement behaviour patterns measured by wearables, and to develop a structured framework around them; (2) describe and quantify the movement behaviour complexity of UK adults using wearable data from the Fenland Study; (3) investigate cross-sectional and longitudinal associations between movement behaviour complexity and the risk of cardiometabolic disease. The first part of my research involved a systematic review which identified and compared metrics and methods used to describe wearable-measured temporal movement behaviour patterns. Searches across four databases yielded 19,060 records, of which 1,962 studies met inclusion criteria. From these, 143 standalone metrics and methods were identified and mapped onto a newly developed taxonomy consisting of four stages and nine nodes. This framework provides researchers with a structured approach for selecting and comparing metrics to study temporal movement behaviour patterns. Using a selection of metrics identified from the systematic review, I assessed movement behaviour complexity in ~12,000 participants from the Fenland Study (Phase 1/Baseline) using minute-level accelerometry and heart rate data. Complexity was quantified using information entropy, sample entropy, and Lempel-Ziv complexity, which capture the predictability, regularity, diversity, and temporal structure of movement sequences. Descriptive analyses showed that higher complexity was observed in more active individuals, while lower complexity was associated with female sex, older age, higher BMI, and sedentary occupations. Sociodemographic associations persisted even after adjusting for PAEE, indicating that complexity captures distinct aspects of movement behaviour beyond overall activity levels. Cross-sectional (~12,000 participants) and longitudinal (~7,000 participants) multivariable regression analyses examined associations between complexity metrics and risk factors for cardiometabolic disease. Before adjusting for PAEE, higher complexity was associated with lower risk. However, after adjusting for PAEE, results showed that higher Lempel-Ziv complexity (more diverse movement sequences) and lower sample entropy (greater regularity) were associated with lower risk, indicating that structured variability in movement patterns may be favourable for cardiometabolic health. Prospective analyses supported these findings, although no significant associations were observed between changes in complexity metrics and changes in cardiometabolic risk over time. This thesis consolidates and structures the study of temporal movement behaviour patterns through a new taxonomy and inventory of metrics and methods. Findings from the Fenland cohort suggest that movement behaviour complexity varies by sex, age, BMI, and occupation and is independently associated with cardiometabolic health. Specifically, maintaining a structured yet varied movement pattern – characterised by lower sample entropy and higher Lempel-Ziv complexity – was linked to better cardiometabolic outcomes. These findings suggest that complexity metrics have an added value in advancing our understanding of movement behaviour and its implications for health, potentially informing future research and the design of targeted interventions.","abstract_has_math":false,"creators":["Kobeissi, Elsa"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Brage, Soren"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-03-27","date_published":"2025-03-27","updated_at":"2026-07-22T22:24:20Z","subjects":["movement behaviour","patterns","taxonomy","cardiometabolic health"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/81d122c9-4e87-4b73-9afe-a150c1ca2f20/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000321804481"],"render_values":[{"text":"0000-0003-2180-4481","href":"https://orcid.org/0000-0003-2180-4481","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.122240","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Brage, Soren"]},{"key":"dc:creator","label":"Author","values":["Kobeissi, Elsa"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000321804481"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-03-27"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/390857"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["movement behaviour","patterns","taxonomy","cardiometabolic health"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/81d122c9-4e87-4b73-9afe-a150c1ca2f20/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.122240"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/7aa40c7b-8499-43bf-9457-a5d7f1b9987e/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The UK and WHO guidelines highlight the importance of regular physical activity and reducing sedentary time but provide limited guidance regarding the temporal patterning of these movement behaviours. Wearable devices have enabled researchers to explore such movement patterns at a time-resolution not possible using self-reported data. However, the substantial and growing number of metrics and methods used to study movement patterns have not been consolidated, making the field difficult to navigate. Additionally, the independent role of movement patterns in cardiometabolic health, beyond total physical activity energy expenditure (PAEE), remains unclear. This thesis aimed to: (1) review and categorise metrics and methods used to describe temporal movement behaviour patterns measured by wearables, and to develop a structured framework around them; (2) describe and quantify the movement behaviour complexity of UK adults using wearable data from the Fenland Study; (3) investigate cross-sectional and longitudinal associations between movement behaviour complexity and the risk of cardiometabolic disease. The first part of my research involved a systematic review which identified and compared metrics and methods used to describe wearable-measured temporal movement behaviour patterns. Searches across four databases yielded 19,060 records, of which 1,962 studies met inclusion criteria. From these, 143 standalone metrics and methods were identified and mapped onto a newly developed taxonomy consisting of four stages and nine nodes. This framework provides researchers with a structured approach for selecting and comparing metrics to study temporal movement behaviour patterns. Using a selection of metrics identified from the systematic review, I assessed movement behaviour complexity in ~12,000 participants from the Fenland Study (Phase 1/Baseline) using minute-level accelerometry and heart rate data. Complexity was quantified using information entropy, sample entropy, and Lempel-Ziv complexity, which capture the predictability, regularity, diversity, and temporal structure of movement sequences. Descriptive analyses showed that higher complexity was observed in more active individuals, while lower complexity was associated with female sex, older age, higher BMI, and sedentary occupations. Sociodemographic associations persisted even after adjusting for PAEE, indicating that complexity captures distinct aspects of movement behaviour beyond overall activity levels. Cross-sectional (~12,000 participants) and longitudinal (~7,000 participants) multivariable regression analyses examined associations between complexity metrics and risk factors for cardiometabolic disease. Before adjusting for PAEE, higher complexity was associated with lower risk. However, after adjusting for PAEE, results showed that higher Lempel-Ziv complexity (more diverse movement sequences) and lower sample entropy (greater regularity) were associated with lower risk, indicating that structured variability in movement patterns may be favourable for cardiometabolic health. Prospective analyses supported these findings, although no significant associations were observed between changes in complexity metrics and changes in cardiometabolic risk over time. This thesis consolidates and structures the study of temporal movement behaviour patterns through a new taxonomy and inventory of metrics and methods. Findings from the Fenland cohort suggest that movement behaviour complexity varies by sex, age, BMI, and occupation and is independently associated with cardiometabolic health. Specifically, maintaining a structured yet varied movement pattern – characterised by lower sample entropy and higher Lempel-Ziv complexity – was linked to better cardiometabolic outcomes. These findings suggest that complexity metrics have an added value in advancing our understanding of movement behaviour and its implications for health, potentially informing future research and the design of targeted interventions."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["50bfb8ee5c91cce60b72fce9432b20b1","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Understanding Temporal Movement Behaviour Patterns: Measurement, Epidemiology, and Cardiometabolic Health Implications"]}]}],"canonical_facts":{"dc:contributor.advisor":["Brage, Soren"],"dc:creator":["Kobeissi, Elsa"],"dc:creator.authoridentifier":["0000000321804481"],"dc:date.issued":["2025-03-27"],"dc:description.abstract":["The UK and WHO guidelines highlight the importance of regular physical activity and reducing sedentary time but provide limited guidance regarding the temporal patterning of these movement behaviours. 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The first part of my research involved a systematic review which identified and compared metrics and methods used to describe wearable-measured temporal movement behaviour patterns. Searches across four databases yielded 19,060 records, of which 1,962 studies met inclusion criteria. From these, 143 standalone metrics and methods were identified and mapped onto a newly developed taxonomy consisting of four stages and nine nodes. This framework provides researchers with a structured approach for selecting and comparing metrics to study temporal movement behaviour patterns. Using a selection of metrics identified from the systematic review, I assessed movement behaviour complexity in ~12,000 participants from the Fenland Study (Phase 1/Baseline) using minute-level accelerometry and heart rate data. Complexity was quantified using information entropy, sample entropy, and Lempel-Ziv complexity, which capture the predictability, regularity, diversity, and temporal structure of movement sequences. Descriptive analyses showed that higher complexity was observed in more active individuals, while lower complexity was associated with female sex, older age, higher BMI, and sedentary occupations. Sociodemographic associations persisted even after adjusting for PAEE, indicating that complexity captures distinct aspects of movement behaviour beyond overall activity levels. Cross-sectional (~12,000 participants) and longitudinal (~7,000 participants) multivariable regression analyses examined associations between complexity metrics and risk factors for cardiometabolic disease. Before adjusting for PAEE, higher complexity was associated with lower risk. However, after adjusting for PAEE, results showed that higher Lempel-Ziv complexity (more diverse movement sequences) and lower sample entropy (greater regularity) were associated with lower risk, indicating that structured variability in movement patterns may be favourable for cardiometabolic health. Prospective analyses supported these findings, although no significant associations were observed between changes in complexity metrics and changes in cardiometabolic risk over time. This thesis consolidates and structures the study of temporal movement behaviour patterns through a new taxonomy and inventory of metrics and methods. Findings from the Fenland cohort suggest that movement behaviour complexity varies by sex, age, BMI, and occupation and is independently associated with cardiometabolic health. Specifically, maintaining a structured yet varied movement pattern – characterised by lower sample entropy and higher Lempel-Ziv complexity – was linked to better cardiometabolic outcomes. These findings suggest that complexity metrics have an added value in advancing our understanding of movement behaviour and its implications for health, potentially informing future research and the design of targeted interventions."],"dc:format.checksum.md5":["50bfb8ee5c91cce60b72fce9432b20b1","87eda9de84448d1f82354d60eee3eb5f"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.122240"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/7aa40c7b-8499-43bf-9457-a5d7f1b9987e/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/390857"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/81d122c9-4e87-4b73-9afe-a150c1ca2f20/download","http://purl.org/NET/rdflicense/allrightsreserved"],"dc:subject":["movement behaviour","patterns","taxonomy","cardiometabolic health"],"dc:title":["Understanding Temporal Movement Behaviour Patterns: Measurement, Epidemiology, and Cardiometabolic Health Implications"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:24:20Z"}