{"id":12360,"date":"2026-04-06T13:18:25","date_gmt":"2026-04-06T04:18:25","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=12360"},"modified":"2026-05-06T12:39:47","modified_gmt":"2026-05-06T03:39:47","slug":"digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim\/","title":{"rendered":"Impact of daylight saving time on physical activity patterns – Myna Lim"},"content":{"rendered":"

In this talk, we discuss the paper \u201cImpact of daylight saving time on physical activity patterns\u201d by Hayoung Jeong et al., Nature Health, 2026.<\/p>\n

Abstract
\n<\/strong>Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health\u00a0All of Us<\/i>\u00a0Research Program. Avoiding strict modelling assumptions, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval\u2009=\u2009[78, 326],\u00a0P<\/i>\u2009=\u20090.001) while reducing evening steps by 180 (confidence interval\u2009=\u2009[\u2212263, \u221297],\u00a0P<\/i>\u2009<\u20090.001); spring transitions showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (\u2018morning walker\u2019, \u2018neutral walker\u2019 and \u2018evening walker\u2019). These disparities suggest that structural factors (for example, rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioural changes, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST\u2019s influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this talk, we discuss the paper \u201cImpact of daylight saving time on physical activity patterns\u201d by Hayoung Jeong et al., Nature Health, 2026. Abstract Daylight saving time (DST) remains … <\/p>\n

Continue reading “Impact of daylight saving time on physical activity patterns – Myna Lim”<\/span><\/a><\/p>\n","protected":false},"author":13,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-12360","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"\nImpact of daylight saving time on physical activity patterns - Myna Lim - Biomedical Mathematics Group<\/title>\n<meta name=\"description\" content=\"Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden of active assessments. This study investigated the potential of consumer-grade wearable and mobile technologies to passively predict 21 cognitive and mental health outcomes in real-world conditions. We collected data from 82 cognitively healthy adults, including passively measured behaviour, physiology, and environmental exposures longitudinally, for 10 months. Active data were gathered in four waves using validated patient- and performance-reported outcomes. Data quality assurance involved a data filtering resulting in average wearable data coverage of 96% per day. Artificial Intelligence-powered prediction was applied, and performance was assessed using subject- and wave-dependent cross-validation. Cognitive and affective outcomes were predicted with low scaled errors. Patient-reported outcomes were more predictable than performance-based ones. Environmental and physiological metrics emerged as the most informative predictors. Passive multimodal data captured meaningful variability in cognition and affect, demonstrating the feasibility of low-burden, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. Avoiding strict modelling assumptions, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval\u2009=\u2009[78, 326], P\u2009=\u20090.001) while reducing evening steps by 180 (confidence interval\u2009=\u2009[\u2212263, \u221297], P\u2009<\u20090.001); spring transitions showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (\u2018morning walker\u2019, \u2018neutral walker\u2019 and \u2018evening walker\u2019). These disparities suggest that structural factors (for example, rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioural changes, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST\u2019s influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Impact of daylight saving time on physical activity patterns - Myna Lim - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden of active assessments. This study investigated the potential of consumer-grade wearable and mobile technologies to passively predict 21 cognitive and mental health outcomes in real-world conditions. We collected data from 82 cognitively healthy adults, including passively measured behaviour, physiology, and environmental exposures longitudinally, for 10 months. Active data were gathered in four waves using validated patient- and performance-reported outcomes. Data quality assurance involved a data filtering resulting in average wearable data coverage of 96% per day. Artificial Intelligence-powered prediction was applied, and performance was assessed using subject- and wave-dependent cross-validation. Cognitive and affective outcomes were predicted with low scaled errors. Patient-reported outcomes were more predictable than performance-based ones. Environmental and physiological metrics emerged as the most informative predictors. Passive multimodal data captured meaningful variability in cognition and affect, demonstrating the feasibility of low-burden, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. Avoiding strict modelling assumptions, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval\u2009=\u2009[78, 326], P\u2009=\u20090.001) while reducing evening steps by 180 (confidence interval\u2009=\u2009[\u2212263, \u221297], P\u2009<\u20090.001); spring transitions showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (\u2018morning walker\u2019, \u2018neutral walker\u2019 and \u2018evening walker\u2019). These disparities suggest that structural factors (for example, rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioural changes, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST\u2019s influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/event\/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-06T03:39:47+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim\\\/\",\"name\":\"Impact of daylight saving time on physical activity patterns - Myna Lim - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"datePublished\":\"2026-04-06T04:18:25+00:00\",\"dateModified\":\"2026-05-06T03:39:47+00:00\",\"description\":\"Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden of active assessments. 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Passive multimodal data captured meaningful variability in cognition and affect, demonstrating the feasibility of low-burden, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. 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Avoiding strict modelling assumptions, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval\u2009=\u2009[78, 326], P\u2009=\u20090.001) while reducing evening steps by 180 (confidence interval\u2009=\u2009[\u2212263, \u221297], P\u2009<\u20090.001); spring transitions showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (\u2018morning walker\u2019, \u2018neutral walker\u2019 and \u2018evening walker\u2019). These disparities suggest that structural factors (for example, rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. 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Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioural changes, although differences were clinically insignificant. 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Abstract Daylight saving time (DST) remains … Continue reading \"Impact of daylight saving time on physical activity patterns – Myna Lim\"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12360","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/users\/13"}],"version-history":[{"count":4,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12360\/revisions"}],"predecessor-version":[{"id":12410,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12360\/revisions\/12410"}],"wp:attachment":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media?parent=12360"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tags?post=12360"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events_cat?post=12360"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}